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Delete Framework-Hooks directory
Signed-off-by: NomenAK <39598727+NomenAK@users.noreply.github.com>
This commit is contained in:
NomenAK
@@ -1,99 +0,0 @@
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# Hook Documentation Update Summary
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## Overview
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Updated hook documentation files to accurately reflect the actual Python implementations, removing marketing language and aspirational features in favor of technical accuracy.
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## Key Changes Made
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### Common Updates Across All Hooks
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1. **Replaced aspirational descriptions** with accurate technical implementation details
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2. **Added actual execution context** including timeout values from `settings.json`
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3. **Updated execution flows** to match stdin/stdout JSON processing pattern
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4. **Documented actual shared module dependencies** and their usage
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5. **Simplified language** to focus on what the code actually does
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6. **Added implementation line counts** for context
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7. **Corrected performance targets** to match configuration values
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### Specific Hook Updates
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#### session_start.md
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- **Lines**: 704-line Python implementation
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- **Timeout**: 10 seconds (from settings.json)
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- **Key Features**: Lazy loading architecture, project structure analysis, user intent analysis, MCP server configuration
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- **Shared Modules**: framework_logic, pattern_detection, mcp_intelligence, compression_engine, learning_engine, yaml_loader, logger
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- **Performance**: <50ms target
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||||
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#### pre_tool_use.md
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- **Lines**: 648-line Python implementation
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- **Timeout**: 15 seconds (from settings.json)
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- **Key Features**: Operation characteristics analysis, tool chain context analysis, MCP server routing, performance optimization
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- **Performance**: <200ms target
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#### post_tool_use.md
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- **Lines**: 794-line Python implementation
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- **Timeout**: 10 seconds (from settings.json)
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- **Key Features**: Validation against RULES.md and PRINCIPLES.md, effectiveness measurement, error pattern detection, learning integration
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- **Performance**: <100ms target
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#### pre_compact.md
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- **Timeout**: 15 seconds (from settings.json)
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- **Key Features**: MODE_Token_Efficiency implementation, selective compression, symbol systems
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- **Performance**: <150ms target
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#### notification.md
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- **Timeout**: 10 seconds (from settings.json)
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- **Key Features**: Just-in-time capability loading, notification type handling
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- **Processing**: High/medium/low priority notification handling
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#### stop.md
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- **Timeout**: 15 seconds (from settings.json)
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- **Key Features**: Session analytics, learning consolidation, data persistence
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- **Performance**: <200ms target
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#### subagent_stop.md
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- **Timeout**: 15 seconds (from settings.json)
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- **Key Features**: Delegation effectiveness measurement, multi-agent coordination analytics
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- **Performance**: <150ms target
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## Technical Accuracy Improvements
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1. **Execution Pattern**: All hooks follow stdin JSON → process → stdout JSON pattern
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2. **Error Handling**: All hooks implement graceful fallback with basic functionality preservation
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3. **Shared Modules**: Documented actual module imports and specific method usage
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4. **Configuration**: Referenced actual configuration files and fallback strategies
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5. **Performance**: Corrected timeout values and performance targets based on actual settings
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## Language Changes
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- **Before**: "comprehensive intelligence layer", "transformative capabilities", "revolutionary approach"
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- **After**: "analyzes project context", "implements pattern detection", "provides MCP server coordination"
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- **Before**: Complex architectural descriptions without implementation details
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- **After**: Actual method names, class structures, and execution flows
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- **Before**: Aspirational features not yet implemented
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- **After**: Features that actually exist in the Python code
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## Documentation Quality
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- Focused on practical implementation details developers need
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- Removed marketing language in favor of technical precision
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- Added concrete examples from actual code
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- Clarified what each hook actually does vs. what it might do
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- Made timeouts and performance targets realistic and accurate
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## Files Updated
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- `/docs/Hooks/session_start.md` - Major revision focusing on actual implementation
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- `/docs/Hooks/pre_tool_use.md` - Streamlined to match 648-line implementation
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- `/docs/Hooks/post_tool_use.md` - Focused on validation and learning implementation
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- `/docs/Hooks/pre_compact.md` - Simplified compression implementation description
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- `/docs/Hooks/notification.md` - Concise notification handling description
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- `/docs/Hooks/stop.md` - Session analytics and persistence focus
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- `/docs/Hooks/subagent_stop.md` - Delegation analytics focus
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## Result
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Documentation now accurately represents what the Python implementations actually do, with humble technical language focused on practical functionality rather than aspirational capabilities.
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Framework-Hooks/cache/adaptations.json
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Framework-Hooks/cache/adaptations.json
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{
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"complexity:0.5_files:3_mcp_server:sequential_op:test_operation_type:mcp_server_preference": {
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"adaptation_id": "adapt_1754411689_0",
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"pattern_signature": "complexity:0.5_files:3_mcp_server:sequential_op:test_operation_type:mcp_server_preference",
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"trigger_conditions": {
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"operation_type": "test_operation",
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"complexity_score": 0.5,
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"file_count": 3
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},
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"modifications": {
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"preferred_mcp_server": "sequential"
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},
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"effectiveness_history": [
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0.8,
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0.8,
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0.8
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],
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"usage_count": 40,
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"last_used": 1754476722.0475128,
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"confidence_score": 0.9
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},
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"op:recovery_test_type:recovery_pattern": {
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"adaptation_id": "adapt_1754411724_1",
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"pattern_signature": "op:recovery_test_type:recovery_pattern",
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"trigger_conditions": {
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"operation_type": "recovery_test"
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},
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"modifications": {},
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"effectiveness_history": [
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0.9,
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0.9
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],
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"usage_count": 39,
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"last_used": 1754476722.0475132,
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"confidence_score": 0.8
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||||
},
|
||||
"unknown_pattern": {
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"adaptation_id": "adapt_1754413397_2",
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||||
"pattern_signature": "unknown_pattern",
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"trigger_conditions": {
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"resource_usage_percent": 0,
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"conversation_length": 0
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},
|
||||
"modifications": {},
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"effectiveness_history": [
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0,
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1.0
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],
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"usage_count": 73,
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||||
"last_used": 1754476722.062738,
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||||
"confidence_score": 0.8
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||||
}
|
||||
}
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10415
Framework-Hooks/cache/learning_records.json
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10415
Framework-Hooks/cache/learning_records.json
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1
Framework-Hooks/cache/project_patterns.json
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1
Framework-Hooks/cache/project_patterns.json
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{}
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1
Framework-Hooks/cache/session_id
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Framework-Hooks/cache/session_id
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55ca6726
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1
Framework-Hooks/cache/user_preferences.json
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Framework-Hooks/cache/user_preferences.json
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{}
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@@ -1,313 +0,0 @@
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# SuperClaude-Lite Compression Configuration
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# Token efficiency strategies and selective compression patterns
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# Compression Levels and Strategies
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compression_levels:
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minimal: # 0-40% compression
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symbol_systems: false
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abbreviation_systems: false
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structural_optimization: false
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quality_threshold: 0.98
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use_cases: ["user_content", "low_resource_usage", "high_quality_required"]
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efficient: # 40-70% compression
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symbol_systems: true
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abbreviation_systems: false
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structural_optimization: true
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quality_threshold: 0.95
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use_cases: ["moderate_resource_usage", "balanced_efficiency"]
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compressed: # 70-85% compression
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symbol_systems: true
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abbreviation_systems: true
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structural_optimization: true
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quality_threshold: 0.90
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use_cases: ["high_resource_usage", "user_requests_brevity"]
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critical: # 85-95% compression
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symbol_systems: true
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abbreviation_systems: true
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structural_optimization: true
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advanced_techniques: true
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quality_threshold: 0.85
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use_cases: ["resource_constraints", "emergency_compression"]
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emergency: # 95%+ compression
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symbol_systems: true
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abbreviation_systems: true
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structural_optimization: true
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advanced_techniques: true
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aggressive_optimization: true
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quality_threshold: 0.80
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use_cases: ["critical_resource_constraints", "emergency_situations"]
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# Selective Compression Patterns
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selective_compression:
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content_classification:
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framework_exclusions:
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patterns:
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- "~/.claude/"
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- ".claude/"
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- "SuperClaude/*"
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- "CLAUDE.md"
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- "FLAGS.md"
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- "PRINCIPLES.md"
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- "ORCHESTRATOR.md"
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- "MCP_*.md"
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- "MODE_*.md"
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- "SESSION_LIFECYCLE.md"
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compression_level: "preserve" # 0% compression
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reasoning: "Framework content must be preserved for proper operation"
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user_content_preservation:
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patterns:
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- "project_files"
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- "user_documentation"
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- "source_code"
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- "configuration_files"
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- "custom_content"
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compression_level: "minimal" # Light compression only
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reasoning: "User content requires high fidelity preservation"
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session_data_optimization:
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patterns:
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- "session_metadata"
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- "checkpoint_data"
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- "cache_content"
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- "working_artifacts"
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- "analysis_results"
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compression_level: "efficient" # 40-70% compression
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reasoning: "Session data can be compressed while maintaining utility"
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compressible_content:
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patterns:
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- "framework_repetition"
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- "historical_session_data"
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- "cached_analysis_results"
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- "temporary_working_data"
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compression_level: "compressed" # 70-85% compression
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reasoning: "Highly compressible content with acceptable quality trade-offs"
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# Symbol Systems Configuration
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symbol_systems:
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core_logic_flow:
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enabled: true
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mappings:
|
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"leads to": "→"
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"implies": "→"
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"transforms to": "⇒"
|
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"converts to": "⇒"
|
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"rollback": "←"
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"reverse": "←"
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||||
"bidirectional": "⇄"
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"sync": "⇄"
|
||||
"and": "&"
|
||||
"combine": "&"
|
||||
"separator": "|"
|
||||
"or": "|"
|
||||
"define": ":"
|
||||
"specify": ":"
|
||||
"sequence": "»"
|
||||
"then": "»"
|
||||
"therefore": "∴"
|
||||
"because": "∵"
|
||||
"equivalent": "≡"
|
||||
"approximately": "≈"
|
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"not equal": "≠"
|
||||
|
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status_progress:
|
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enabled: true
|
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mappings:
|
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"completed": "✅"
|
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"passed": "✅"
|
||||
"failed": "❌"
|
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"error": "❌"
|
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"warning": "⚠️"
|
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"information": "ℹ️"
|
||||
"in progress": "🔄"
|
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"processing": "🔄"
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"waiting": "⏳"
|
||||
"pending": "⏳"
|
||||
"critical": "🚨"
|
||||
"urgent": "🚨"
|
||||
"target": "🎯"
|
||||
"goal": "🎯"
|
||||
"metrics": "📊"
|
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"data": "📊"
|
||||
"insight": "💡"
|
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"learning": "💡"
|
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|
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technical_domains:
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enabled: true
|
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mappings:
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"performance": "⚡"
|
||||
"optimization": "⚡"
|
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"analysis": "🔍"
|
||||
"investigation": "🔍"
|
||||
"configuration": "🔧"
|
||||
"setup": "🔧"
|
||||
"security": "🛡️"
|
||||
"protection": "🛡️"
|
||||
"deployment": "📦"
|
||||
"package": "📦"
|
||||
"design": "🎨"
|
||||
"frontend": "🎨"
|
||||
"network": "🌐"
|
||||
"connectivity": "🌐"
|
||||
"mobile": "📱"
|
||||
"responsive": "📱"
|
||||
"architecture": "🏗️"
|
||||
"system structure": "🏗️"
|
||||
"components": "🧩"
|
||||
"modular": "🧩"
|
||||
|
||||
# Abbreviation Systems Configuration
|
||||
abbreviation_systems:
|
||||
system_architecture:
|
||||
enabled: true
|
||||
mappings:
|
||||
"configuration": "cfg"
|
||||
"settings": "cfg"
|
||||
"implementation": "impl"
|
||||
"code structure": "impl"
|
||||
"architecture": "arch"
|
||||
"system design": "arch"
|
||||
"performance": "perf"
|
||||
"optimization": "perf"
|
||||
"operations": "ops"
|
||||
"deployment": "ops"
|
||||
"environment": "env"
|
||||
"runtime context": "env"
|
||||
|
||||
development_process:
|
||||
enabled: true
|
||||
mappings:
|
||||
"requirements": "req"
|
||||
"dependencies": "deps"
|
||||
"packages": "deps"
|
||||
"validation": "val"
|
||||
"verification": "val"
|
||||
"testing": "test"
|
||||
"quality assurance": "test"
|
||||
"documentation": "docs"
|
||||
"guides": "docs"
|
||||
"standards": "std"
|
||||
"conventions": "std"
|
||||
|
||||
quality_analysis:
|
||||
enabled: true
|
||||
mappings:
|
||||
"quality": "qual"
|
||||
"maintainability": "qual"
|
||||
"security": "sec"
|
||||
"safety measures": "sec"
|
||||
"error": "err"
|
||||
"exception handling": "err"
|
||||
"recovery": "rec"
|
||||
"resilience": "rec"
|
||||
"severity": "sev"
|
||||
"priority level": "sev"
|
||||
"optimization": "opt"
|
||||
"improvement": "opt"
|
||||
|
||||
# Structural Optimization Techniques
|
||||
structural_optimization:
|
||||
whitespace_optimization:
|
||||
enabled: true
|
||||
remove_redundant_spaces: true
|
||||
normalize_line_breaks: true
|
||||
preserve_code_formatting: true
|
||||
|
||||
phrase_simplification:
|
||||
enabled: true
|
||||
common_phrase_replacements:
|
||||
"in order to": "to"
|
||||
"it is important to note that": "note:"
|
||||
"please be aware that": "note:"
|
||||
"it should be noted that": "note:"
|
||||
"for the purpose of": "for"
|
||||
"with regard to": "regarding"
|
||||
"in relation to": "regarding"
|
||||
|
||||
redundancy_removal:
|
||||
enabled: true
|
||||
remove_articles: ["the", "a", "an"] # Only in high compression levels
|
||||
remove_filler_words: ["very", "really", "quite", "rather"]
|
||||
combine_repeated_concepts: true
|
||||
|
||||
# Quality Preservation Standards
|
||||
quality_preservation:
|
||||
minimum_thresholds:
|
||||
information_preservation: 0.95
|
||||
semantic_accuracy: 0.95
|
||||
technical_correctness: 0.98
|
||||
user_content_fidelity: 0.99
|
||||
|
||||
validation_criteria:
|
||||
key_concept_retention: true
|
||||
technical_term_preservation: true
|
||||
code_example_accuracy: true
|
||||
reference_link_preservation: true
|
||||
|
||||
quality_monitoring:
|
||||
real_time_validation: true
|
||||
effectiveness_tracking: true
|
||||
user_feedback_integration: true
|
||||
adaptive_threshold_adjustment: true
|
||||
|
||||
# Adaptive Compression Strategy
|
||||
adaptive_compression:
|
||||
context_awareness:
|
||||
user_expertise_factor: true
|
||||
project_complexity_factor: true
|
||||
domain_specific_optimization: true
|
||||
|
||||
learning_integration:
|
||||
effectiveness_feedback: true
|
||||
user_preference_learning: true
|
||||
pattern_optimization: true
|
||||
|
||||
dynamic_adjustment:
|
||||
resource_pressure_response: true
|
||||
quality_threshold_adaptation: true
|
||||
performance_optimization: true
|
||||
|
||||
# Performance Targets
|
||||
performance_targets:
|
||||
processing_time_ms: 150
|
||||
compression_ratio_target: 0.50 # 50% compression
|
||||
quality_preservation_target: 0.95
|
||||
token_efficiency_gain: 0.40 # 40% token reduction
|
||||
|
||||
# Cache Configuration
|
||||
caching:
|
||||
compression_results:
|
||||
enabled: true
|
||||
cache_duration_minutes: 30
|
||||
max_cache_size_mb: 50
|
||||
invalidation_strategy: "content_change_detection"
|
||||
|
||||
symbol_mappings:
|
||||
enabled: true
|
||||
preload_common_patterns: true
|
||||
learning_based_optimization: true
|
||||
|
||||
pattern_recognition:
|
||||
enabled: true
|
||||
adaptive_pattern_learning: true
|
||||
user_specific_patterns: true
|
||||
|
||||
# Integration with Other Systems
|
||||
integration:
|
||||
mcp_servers:
|
||||
morphllm: "coordinate_compression_with_editing"
|
||||
serena: "memory_compression_strategies"
|
||||
|
||||
modes:
|
||||
token_efficiency: "primary_compression_mode"
|
||||
task_management: "session_data_compression"
|
||||
|
||||
learning_engine:
|
||||
effectiveness_tracking: true
|
||||
pattern_learning: true
|
||||
adaptation_feedback: true
|
||||
@@ -1,322 +0,0 @@
|
||||
# Hook Coordination Configuration
|
||||
# Intelligent hook execution patterns, dependency resolution, and optimization
|
||||
# Enables smart coordination of all Framework-Hooks lifecycle events
|
||||
|
||||
# Metadata
|
||||
version: "1.0.0"
|
||||
last_updated: "2025-01-06"
|
||||
description: "Hook coordination and execution intelligence patterns"
|
||||
|
||||
# Hook Execution Patterns
|
||||
execution_patterns:
|
||||
parallel_execution:
|
||||
# Independent hooks that can run simultaneously
|
||||
groups:
|
||||
- name: "independent_analysis"
|
||||
hooks: ["compression_engine", "pattern_detection"]
|
||||
description: "Compression and pattern analysis can run independently"
|
||||
max_parallel: 2
|
||||
timeout: 5000 # ms
|
||||
|
||||
- name: "intelligence_gathering"
|
||||
hooks: ["mcp_intelligence", "learning_engine"]
|
||||
description: "MCP and learning intelligence can run in parallel"
|
||||
max_parallel: 2
|
||||
timeout: 3000 # ms
|
||||
|
||||
- name: "background_optimization"
|
||||
hooks: ["performance_monitor", "cache_management"]
|
||||
description: "Performance monitoring and cache operations"
|
||||
max_parallel: 2
|
||||
timeout: 2000 # ms
|
||||
|
||||
sequential_execution:
|
||||
# Hooks that must run in specific order
|
||||
chains:
|
||||
- name: "session_lifecycle"
|
||||
sequence: ["session_start", "pre_tool_use", "post_tool_use", "stop"]
|
||||
description: "Core session lifecycle must be sequential"
|
||||
mandatory: true
|
||||
break_on_error: true
|
||||
|
||||
- name: "context_preparation"
|
||||
sequence: ["session_start", "context_loading", "pattern_detection"]
|
||||
description: "Context must be prepared before pattern analysis"
|
||||
conditional: {session_type: "complex"}
|
||||
|
||||
- name: "optimization_chain"
|
||||
sequence: ["compression_engine", "performance_monitor", "learning_engine"]
|
||||
description: "Optimization workflow sequence"
|
||||
trigger: {optimization_mode: true}
|
||||
|
||||
conditional_execution:
|
||||
# Hooks that execute based on context conditions
|
||||
rules:
|
||||
- hook: "compression_engine"
|
||||
conditions:
|
||||
- resource_usage: ">0.75"
|
||||
- conversation_length: ">50"
|
||||
- enable_compression: true
|
||||
priority: "high"
|
||||
|
||||
- hook: "pattern_detection"
|
||||
conditions:
|
||||
- complexity_score: ">0.5"
|
||||
- enable_pattern_analysis: true
|
||||
OR:
|
||||
- operation_type: ["analyze", "review", "debug"]
|
||||
priority: "medium"
|
||||
|
||||
- hook: "mcp_intelligence"
|
||||
conditions:
|
||||
- mcp_servers_available: true
|
||||
- operation_requires_mcp: true
|
||||
priority: "high"
|
||||
|
||||
- hook: "learning_engine"
|
||||
conditions:
|
||||
- learning_enabled: true
|
||||
- session_type: ["interactive", "complex"]
|
||||
priority: "medium"
|
||||
|
||||
- hook: "performance_monitor"
|
||||
conditions:
|
||||
- performance_monitoring: true
|
||||
OR:
|
||||
- complexity_score: ">0.7"
|
||||
- resource_usage: ">0.8"
|
||||
priority: "low"
|
||||
|
||||
# Dependency Resolution
|
||||
dependency_resolution:
|
||||
hook_dependencies:
|
||||
# Define dependencies between hooks
|
||||
session_start:
|
||||
requires: []
|
||||
provides: ["session_context", "initial_state"]
|
||||
|
||||
pre_tool_use:
|
||||
requires: ["session_context"]
|
||||
provides: ["tool_context", "pre_analysis"]
|
||||
depends_on: ["session_start"]
|
||||
|
||||
compression_engine:
|
||||
requires: ["session_context"]
|
||||
provides: ["compression_config", "optimized_context"]
|
||||
optional_depends: ["session_start"]
|
||||
|
||||
pattern_detection:
|
||||
requires: ["session_context"]
|
||||
provides: ["detected_patterns", "pattern_insights"]
|
||||
optional_depends: ["session_start", "compression_engine"]
|
||||
|
||||
mcp_intelligence:
|
||||
requires: ["tool_context", "detected_patterns"]
|
||||
provides: ["mcp_recommendations", "server_selection"]
|
||||
depends_on: ["pre_tool_use"]
|
||||
optional_depends: ["pattern_detection"]
|
||||
|
||||
post_tool_use:
|
||||
requires: ["tool_context", "tool_results"]
|
||||
provides: ["post_analysis", "performance_metrics"]
|
||||
depends_on: ["pre_tool_use"]
|
||||
|
||||
learning_engine:
|
||||
requires: ["post_analysis", "performance_metrics"]
|
||||
provides: ["learning_insights", "adaptations"]
|
||||
depends_on: ["post_tool_use"]
|
||||
optional_depends: ["mcp_intelligence", "pattern_detection"]
|
||||
|
||||
stop:
|
||||
requires: ["session_context"]
|
||||
provides: ["session_summary", "cleanup_status"]
|
||||
depends_on: ["session_start"]
|
||||
optional_depends: ["learning_engine", "post_tool_use"]
|
||||
|
||||
resolution_strategies:
|
||||
# How to resolve dependency conflicts
|
||||
missing_dependency:
|
||||
strategy: "graceful_degradation"
|
||||
fallback: "skip_optional"
|
||||
|
||||
circular_dependency:
|
||||
strategy: "break_weakest_link"
|
||||
priority_order: ["session_start", "pre_tool_use", "post_tool_use", "stop"]
|
||||
|
||||
timeout_handling:
|
||||
strategy: "continue_without_dependency"
|
||||
timeout_threshold: 10000 # ms
|
||||
|
||||
# Performance Optimization
|
||||
performance_optimization:
|
||||
execution_optimization:
|
||||
# Optimize hook execution based on context
|
||||
fast_path:
|
||||
conditions:
|
||||
- complexity_score: "<0.3"
|
||||
- operation_type: ["simple", "basic"]
|
||||
- resource_usage: "<0.5"
|
||||
optimizations:
|
||||
- skip_non_essential_hooks: true
|
||||
- reduce_analysis_depth: true
|
||||
- enable_aggressive_caching: true
|
||||
- parallel_where_possible: true
|
||||
|
||||
comprehensive_path:
|
||||
conditions:
|
||||
- complexity_score: ">0.7"
|
||||
- operation_type: ["complex", "analysis"]
|
||||
- accuracy_priority: "high"
|
||||
optimizations:
|
||||
- enable_all_hooks: true
|
||||
- deep_analysis_mode: true
|
||||
- cross_hook_coordination: true
|
||||
- detailed_logging: true
|
||||
|
||||
resource_management:
|
||||
# Manage resource usage across hooks
|
||||
resource_budgets:
|
||||
cpu_budget: 80 # percent
|
||||
memory_budget: 70 # percent
|
||||
time_budget: 15000 # ms total
|
||||
|
||||
resource_allocation:
|
||||
session_lifecycle: 30 # percent of budget
|
||||
intelligence_hooks: 40 # percent
|
||||
optimization_hooks: 30 # percent
|
||||
|
||||
caching_strategies:
|
||||
# Hook result caching
|
||||
cacheable_hooks:
|
||||
- hook: "pattern_detection"
|
||||
cache_key: ["session_context", "operation_type"]
|
||||
cache_duration: 300 # seconds
|
||||
|
||||
- hook: "mcp_intelligence"
|
||||
cache_key: ["operation_context", "available_servers"]
|
||||
cache_duration: 600 # seconds
|
||||
|
||||
- hook: "compression_engine"
|
||||
cache_key: ["context_size", "compression_level"]
|
||||
cache_duration: 1800 # seconds
|
||||
|
||||
# Context-Aware Execution
|
||||
context_awareness:
|
||||
operation_context:
|
||||
# Adapt execution based on operation context
|
||||
context_patterns:
|
||||
- context_type: "ui_development"
|
||||
hook_priorities: ["mcp_intelligence", "pattern_detection", "compression_engine"]
|
||||
preferred_execution: "fast_parallel"
|
||||
|
||||
- context_type: "code_analysis"
|
||||
hook_priorities: ["pattern_detection", "mcp_intelligence", "learning_engine"]
|
||||
preferred_execution: "comprehensive_sequential"
|
||||
|
||||
- context_type: "performance_optimization"
|
||||
hook_priorities: ["performance_monitor", "compression_engine", "pattern_detection"]
|
||||
preferred_execution: "resource_optimized"
|
||||
|
||||
user_preferences:
|
||||
# Adapt to user preferences and patterns
|
||||
preference_patterns:
|
||||
- user_type: "performance_focused"
|
||||
optimizations: ["aggressive_caching", "parallel_execution", "skip_optional"]
|
||||
|
||||
- user_type: "quality_focused"
|
||||
optimizations: ["comprehensive_analysis", "detailed_validation", "full_coordination"]
|
||||
|
||||
- user_type: "speed_focused"
|
||||
optimizations: ["fast_path", "minimal_hooks", "cached_results"]
|
||||
|
||||
# Error Handling and Recovery
|
||||
error_handling:
|
||||
error_recovery:
|
||||
# Hook failure recovery strategies
|
||||
recovery_strategies:
|
||||
- error_type: "timeout"
|
||||
recovery: "continue_without_hook"
|
||||
log_level: "warning"
|
||||
|
||||
- error_type: "dependency_missing"
|
||||
recovery: "graceful_degradation"
|
||||
log_level: "info"
|
||||
|
||||
- error_type: "critical_failure"
|
||||
recovery: "abort_and_cleanup"
|
||||
log_level: "error"
|
||||
|
||||
resilience_patterns:
|
||||
# Make hook execution resilient
|
||||
resilience_features:
|
||||
retry_failed_hooks: true
|
||||
max_retries: 2
|
||||
retry_backoff: "exponential" # linear, exponential
|
||||
|
||||
graceful_degradation: true
|
||||
fallback_to_basic: true
|
||||
preserve_essential_hooks: ["session_start", "stop"]
|
||||
|
||||
error_isolation: true
|
||||
prevent_error_cascade: true
|
||||
maintain_session_integrity: true
|
||||
|
||||
# Hook Lifecycle Management
|
||||
lifecycle_management:
|
||||
hook_states:
|
||||
# Track hook execution states
|
||||
state_tracking:
|
||||
- pending
|
||||
- initializing
|
||||
- running
|
||||
- completed
|
||||
- failed
|
||||
- skipped
|
||||
- timeout
|
||||
|
||||
lifecycle_events:
|
||||
# Events during hook execution
|
||||
event_handlers:
|
||||
before_hook_execution:
|
||||
actions: ["validate_dependencies", "check_resources", "prepare_context"]
|
||||
|
||||
after_hook_execution:
|
||||
actions: ["update_metrics", "cache_results", "trigger_dependent_hooks"]
|
||||
|
||||
hook_failure:
|
||||
actions: ["log_error", "attempt_recovery", "notify_dependent_hooks"]
|
||||
|
||||
monitoring:
|
||||
# Monitor hook execution
|
||||
performance_tracking:
|
||||
track_execution_time: true
|
||||
track_resource_usage: true
|
||||
track_success_rate: true
|
||||
track_dependency_resolution: true
|
||||
|
||||
health_monitoring:
|
||||
hook_health_checks: true
|
||||
dependency_health_checks: true
|
||||
performance_degradation_detection: true
|
||||
|
||||
# Dynamic Configuration
|
||||
dynamic_configuration:
|
||||
adaptive_execution:
|
||||
# Adapt execution patterns based on performance
|
||||
adaptation_triggers:
|
||||
- performance_degradation: ">20%"
|
||||
action: "switch_to_fast_path"
|
||||
|
||||
- error_rate: ">10%"
|
||||
action: "enable_resilience_mode"
|
||||
|
||||
- resource_pressure: ">90%"
|
||||
action: "reduce_hook_scope"
|
||||
|
||||
learning_integration:
|
||||
# Learn from hook execution patterns
|
||||
learning_features:
|
||||
learn_optimal_execution_order: true
|
||||
learn_user_preferences: true
|
||||
learn_performance_patterns: true
|
||||
adapt_to_project_context: true
|
||||
@@ -1,181 +0,0 @@
|
||||
# Intelligence Patterns Configuration
|
||||
# Core learning intelligence patterns for SuperClaude Framework-Hooks
|
||||
# Defines multi-dimensional pattern recognition, adaptive learning, and intelligence behaviors
|
||||
|
||||
# Metadata
|
||||
version: "1.0.0"
|
||||
last_updated: "2025-01-06"
|
||||
description: "Core intelligence patterns for declarative learning and adaptation"
|
||||
|
||||
# Learning Intelligence Configuration
|
||||
learning_intelligence:
|
||||
pattern_recognition:
|
||||
# Multi-dimensional pattern analysis
|
||||
dimensions:
|
||||
primary:
|
||||
- context_type # Type of operation context
|
||||
- complexity_score # Operation complexity (0.0-1.0)
|
||||
- operation_type # Category of operation
|
||||
- performance_score # Performance effectiveness (0.0-1.0)
|
||||
secondary:
|
||||
- file_count # Number of files involved
|
||||
- directory_count # Number of directories
|
||||
- mcp_server # MCP server involved
|
||||
- user_expertise # Detected user skill level
|
||||
|
||||
# Pattern signature generation
|
||||
signature_generation:
|
||||
method: "multi_dimensional_hash"
|
||||
include_context: true
|
||||
fallback_signature: "unknown_pattern"
|
||||
max_signature_length: 128
|
||||
|
||||
# Pattern clustering for similar behavior grouping
|
||||
clustering:
|
||||
algorithm: "k_means"
|
||||
min_cluster_size: 3
|
||||
max_clusters: 20
|
||||
similarity_threshold: 0.8
|
||||
recalculate_interval: 100 # operations
|
||||
|
||||
adaptive_learning:
|
||||
# Dynamic learning rate adjustment
|
||||
learning_rate:
|
||||
initial: 0.7
|
||||
min: 0.1
|
||||
max: 1.0
|
||||
adaptation_strategy: "confidence_based"
|
||||
|
||||
# Confidence scoring
|
||||
confidence_scoring:
|
||||
base_confidence: 0.5
|
||||
consistency_weight: 0.4
|
||||
frequency_weight: 0.3
|
||||
recency_weight: 0.3
|
||||
|
||||
# Effectiveness thresholds
|
||||
effectiveness_thresholds:
|
||||
learn_threshold: 0.7 # Minimum effectiveness to create adaptation
|
||||
confidence_threshold: 0.6 # Minimum confidence to apply adaptation
|
||||
forget_threshold: 0.3 # Below this, remove adaptation
|
||||
|
||||
pattern_quality:
|
||||
# Pattern validation rules
|
||||
validation_rules:
|
||||
min_usage_count: 3
|
||||
max_consecutive_perfect_scores: 10
|
||||
effectiveness_variance_limit: 0.5
|
||||
required_dimensions: ["context_type", "operation_type"]
|
||||
|
||||
# Quality scoring
|
||||
quality_metrics:
|
||||
diversity_score_weight: 0.4
|
||||
consistency_score_weight: 0.3
|
||||
usage_frequency_weight: 0.3
|
||||
|
||||
# Pattern Analysis Configuration
|
||||
pattern_analysis:
|
||||
anomaly_detection:
|
||||
# Detect unusual patterns that might indicate issues
|
||||
anomaly_patterns:
|
||||
- name: "overfitting_detection"
|
||||
condition: {consecutive_perfect_scores: ">10"}
|
||||
severity: "medium"
|
||||
action: "flag_for_review"
|
||||
|
||||
- name: "pattern_stagnation"
|
||||
condition: {no_new_patterns: ">30_days"}
|
||||
severity: "low"
|
||||
action: "suggest_pattern_diversity"
|
||||
|
||||
- name: "effectiveness_degradation"
|
||||
condition: {effectiveness_trend: "decreasing", duration: ">7_days"}
|
||||
severity: "high"
|
||||
action: "trigger_pattern_analysis"
|
||||
|
||||
trend_analysis:
|
||||
# Track learning trends over time
|
||||
tracking_windows:
|
||||
short_term: 24 # hours
|
||||
medium_term: 168 # hours (1 week)
|
||||
long_term: 720 # hours (30 days)
|
||||
|
||||
trend_indicators:
|
||||
- effectiveness_trend
|
||||
- pattern_diversity_trend
|
||||
- confidence_trend
|
||||
- usage_frequency_trend
|
||||
|
||||
# Intelligence Enhancement Patterns
|
||||
intelligence_enhancement:
|
||||
predictive_capabilities:
|
||||
# Predictive pattern matching
|
||||
prediction_horizon: 5 # operations ahead
|
||||
prediction_confidence_threshold: 0.7
|
||||
prediction_accuracy_tracking: true
|
||||
|
||||
context_awareness:
|
||||
# Context understanding and correlation
|
||||
context_correlation:
|
||||
enable_cross_session: true
|
||||
enable_project_correlation: true
|
||||
enable_user_correlation: true
|
||||
correlation_strength_threshold: 0.6
|
||||
|
||||
adaptive_strategies:
|
||||
# Strategy adaptation based on performance
|
||||
strategy_adaptation:
|
||||
performance_window: 20 # operations
|
||||
adaptation_threshold: 0.8
|
||||
rollback_threshold: 0.5
|
||||
max_adaptations_per_session: 5
|
||||
|
||||
# Pattern Lifecycle Management
|
||||
lifecycle_management:
|
||||
pattern_evolution:
|
||||
# How patterns evolve over time
|
||||
evolution_triggers:
|
||||
- usage_count_milestone: [10, 50, 100, 500]
|
||||
- effectiveness_improvement: 0.1
|
||||
- confidence_improvement: 0.1
|
||||
|
||||
evolution_actions:
|
||||
- promote_to_global
|
||||
- increase_weight
|
||||
- expand_context
|
||||
- merge_similar_patterns
|
||||
|
||||
pattern_cleanup:
|
||||
# Automatic pattern cleanup
|
||||
cleanup_triggers:
|
||||
max_patterns: 1000
|
||||
unused_pattern_age: 30 # days
|
||||
low_effectiveness_threshold: 0.3
|
||||
|
||||
cleanup_strategies:
|
||||
- archive_unused
|
||||
- merge_similar
|
||||
- remove_ineffective
|
||||
- compress_historical
|
||||
|
||||
# Integration Configuration
|
||||
integration:
|
||||
cache_management:
|
||||
# Pattern caching for performance
|
||||
cache_patterns: true
|
||||
cache_duration: 3600 # seconds
|
||||
max_cache_size: 100 # patterns
|
||||
cache_invalidation: "smart" # smart, time_based, usage_based
|
||||
|
||||
performance_optimization:
|
||||
# Performance tuning
|
||||
lazy_loading: true
|
||||
batch_processing: true
|
||||
background_analysis: true
|
||||
max_processing_time_ms: 100
|
||||
|
||||
compatibility:
|
||||
# Backwards compatibility
|
||||
support_legacy_patterns: true
|
||||
migration_assistance: true
|
||||
graceful_degradation: true
|
||||
@@ -1,70 +0,0 @@
|
||||
# SuperClaude-Lite Logging Configuration
|
||||
# Simple logging configuration for hook execution monitoring
|
||||
|
||||
# Core Logging Settings
|
||||
logging:
|
||||
enabled: false
|
||||
level: "ERROR" # ERROR, WARNING, INFO, DEBUG
|
||||
|
||||
# File Settings
|
||||
file_settings:
|
||||
log_directory: "cache/logs"
|
||||
retention_days: 30
|
||||
rotation_strategy: "daily"
|
||||
|
||||
# Hook Logging Settings
|
||||
hook_logging:
|
||||
log_lifecycle: false # Log hook start/end events
|
||||
log_decisions: false # Log decision points
|
||||
log_errors: false # Log error events
|
||||
log_timing: false # Include timing information
|
||||
|
||||
# Performance Settings
|
||||
performance:
|
||||
max_overhead_ms: 1 # Maximum acceptable logging overhead
|
||||
async_logging: false # Keep simple for now
|
||||
|
||||
# Privacy Settings
|
||||
privacy:
|
||||
sanitize_user_content: true
|
||||
exclude_sensitive_data: true
|
||||
anonymize_session_ids: false # Keep for correlation
|
||||
|
||||
# Hook-Specific Configuration
|
||||
hook_configuration:
|
||||
pre_tool_use:
|
||||
enabled: true
|
||||
log_tool_selection: true
|
||||
log_input_validation: true
|
||||
|
||||
post_tool_use:
|
||||
enabled: true
|
||||
log_output_processing: true
|
||||
log_integration_success: true
|
||||
|
||||
session_start:
|
||||
enabled: true
|
||||
log_initialization: true
|
||||
log_configuration_loading: true
|
||||
|
||||
pre_compact:
|
||||
enabled: true
|
||||
log_compression_decisions: true
|
||||
|
||||
notification:
|
||||
enabled: true
|
||||
log_notification_handling: true
|
||||
|
||||
stop:
|
||||
enabled: true
|
||||
log_cleanup_operations: true
|
||||
|
||||
subagent_stop:
|
||||
enabled: true
|
||||
log_subagent_cleanup: true
|
||||
|
||||
# Development Settings
|
||||
development:
|
||||
verbose_errors: false
|
||||
include_stack_traces: false # Keep logs clean
|
||||
debug_mode: false
|
||||
@@ -1,308 +0,0 @@
|
||||
# MCP Orchestration Configuration
|
||||
# Intelligent server selection, coordination, and load balancing patterns
|
||||
# Enables smart MCP server orchestration based on context and performance
|
||||
|
||||
# Metadata
|
||||
version: "1.0.0"
|
||||
last_updated: "2025-01-06"
|
||||
description: "MCP server orchestration intelligence patterns"
|
||||
|
||||
# Server Selection Intelligence
|
||||
server_selection:
|
||||
decision_tree:
|
||||
# UI/Design Operations
|
||||
- name: "ui_component_operations"
|
||||
conditions:
|
||||
keywords: ["component", "ui", "design", "frontend", "jsx", "tsx", "css"]
|
||||
OR:
|
||||
- operation_type: ["build", "implement", "design"]
|
||||
- file_extensions: [".jsx", ".tsx", ".vue", ".css", ".scss"]
|
||||
primary_server: "magic"
|
||||
support_servers: ["context7"]
|
||||
coordination_mode: "parallel"
|
||||
confidence: 0.9
|
||||
|
||||
# Analysis and Architecture Operations
|
||||
- name: "complex_analysis"
|
||||
conditions:
|
||||
AND:
|
||||
- complexity_score: ">0.7"
|
||||
- operation_type: ["analyze", "review", "debug", "troubleshoot"]
|
||||
OR:
|
||||
- file_count: ">10"
|
||||
- keywords: ["architecture", "system", "complex"]
|
||||
primary_server: "sequential"
|
||||
support_servers: ["context7", "serena"]
|
||||
coordination_mode: "sequential"
|
||||
confidence: 0.85
|
||||
|
||||
# Code Refactoring and Transformation
|
||||
- name: "code_refactoring"
|
||||
conditions:
|
||||
AND:
|
||||
- operation_type: ["refactor", "transform", "modify"]
|
||||
OR:
|
||||
- file_count: ">5"
|
||||
- complexity_score: ">0.5"
|
||||
- keywords: ["refactor", "cleanup", "optimize"]
|
||||
primary_server: "serena"
|
||||
support_servers: ["morphllm", "sequential"]
|
||||
coordination_mode: "hybrid"
|
||||
confidence: 0.8
|
||||
|
||||
# Documentation and Learning
|
||||
- name: "documentation_operations"
|
||||
conditions:
|
||||
keywords: ["document", "explain", "guide", "tutorial", "learn"]
|
||||
OR:
|
||||
- operation_type: ["document", "explain"]
|
||||
- file_extensions: [".md", ".rst", ".txt"]
|
||||
primary_server: "context7"
|
||||
support_servers: ["sequential"]
|
||||
coordination_mode: "sequential"
|
||||
confidence: 0.85
|
||||
|
||||
# Testing and Validation
|
||||
- name: "testing_operations"
|
||||
conditions:
|
||||
keywords: ["test", "validate", "check", "verify", "e2e"]
|
||||
OR:
|
||||
- operation_type: ["test", "validate"]
|
||||
- file_patterns: ["*test*", "*spec*", "*e2e*"]
|
||||
primary_server: "playwright"
|
||||
support_servers: ["sequential", "magic"]
|
||||
coordination_mode: "parallel"
|
||||
confidence: 0.8
|
||||
|
||||
# Fast Edits and Transformations
|
||||
- name: "fast_edits"
|
||||
conditions:
|
||||
AND:
|
||||
- complexity_score: "<0.4"
|
||||
- file_count: "<5"
|
||||
operation_type: ["edit", "modify", "fix", "update"]
|
||||
primary_server: "morphllm"
|
||||
support_servers: ["serena"]
|
||||
coordination_mode: "fallback"
|
||||
confidence: 0.7
|
||||
|
||||
# Fallback Strategy
|
||||
fallback_chain:
|
||||
default_primary: "sequential"
|
||||
fallback_sequence: ["context7", "serena", "morphllm", "magic", "playwright"]
|
||||
fallback_threshold: 3.0 # seconds timeout
|
||||
|
||||
# Load Balancing Intelligence
|
||||
load_balancing:
|
||||
health_monitoring:
|
||||
# Server health check configuration
|
||||
check_interval: 30 # seconds
|
||||
timeout: 5 # seconds
|
||||
retry_count: 3
|
||||
|
||||
health_metrics:
|
||||
- response_time
|
||||
- error_rate
|
||||
- request_queue_size
|
||||
- availability_percentage
|
||||
|
||||
performance_thresholds:
|
||||
# Performance-based routing thresholds
|
||||
response_time:
|
||||
excellent: 500 # ms
|
||||
good: 1000 # ms
|
||||
warning: 2000 # ms
|
||||
critical: 5000 # ms
|
||||
|
||||
error_rate:
|
||||
excellent: 0.01 # 1%
|
||||
good: 0.03 # 3%
|
||||
warning: 0.05 # 5%
|
||||
critical: 0.15 # 15%
|
||||
|
||||
queue_size:
|
||||
excellent: 0
|
||||
good: 2
|
||||
warning: 5
|
||||
critical: 10
|
||||
|
||||
routing_strategies:
|
||||
# Load balancing algorithms
|
||||
primary_strategy: "weighted_performance"
|
||||
strategies:
|
||||
round_robin:
|
||||
description: "Distribute requests evenly across healthy servers"
|
||||
weight_factor: "equal"
|
||||
|
||||
weighted_performance:
|
||||
description: "Route based on server performance metrics"
|
||||
weight_factors:
|
||||
response_time: 0.4
|
||||
error_rate: 0.3
|
||||
availability: 0.3
|
||||
|
||||
least_connections:
|
||||
description: "Route to server with fewest active connections"
|
||||
connection_tracking: true
|
||||
|
||||
performance_based:
|
||||
description: "Route to best-performing server"
|
||||
performance_window: 300 # seconds
|
||||
|
||||
# Cross-Server Coordination
|
||||
coordination_patterns:
|
||||
sequential_coordination:
|
||||
# When servers work in sequence
|
||||
patterns:
|
||||
- name: "analysis_then_implementation"
|
||||
sequence: ["sequential", "morphllm"]
|
||||
trigger: {operation: "implement", analysis_required: true}
|
||||
|
||||
- name: "research_then_build"
|
||||
sequence: ["context7", "magic"]
|
||||
trigger: {operation: "build", research_required: true}
|
||||
|
||||
- name: "plan_then_execute"
|
||||
sequence: ["sequential", "serena", "morphllm"]
|
||||
trigger: {complexity: ">0.7", operation: "refactor"}
|
||||
|
||||
parallel_coordination:
|
||||
# When servers work simultaneously
|
||||
patterns:
|
||||
- name: "ui_with_docs"
|
||||
parallel: ["magic", "context7"]
|
||||
trigger: {operation: "build", component_type: "ui"}
|
||||
synchronization: "merge_results"
|
||||
|
||||
- name: "test_with_validation"
|
||||
parallel: ["playwright", "sequential"]
|
||||
trigger: {operation: "test", validation_required: true}
|
||||
synchronization: "wait_all"
|
||||
|
||||
hybrid_coordination:
|
||||
# Mixed coordination patterns
|
||||
patterns:
|
||||
- name: "comprehensive_refactoring"
|
||||
phases:
|
||||
- phase: 1
|
||||
servers: ["sequential"] # Analysis
|
||||
wait_for_completion: true
|
||||
- phase: 2
|
||||
servers: ["serena", "morphllm"] # Parallel execution
|
||||
synchronization: "coordinate_changes"
|
||||
|
||||
# Dynamic Server Capabilities
|
||||
capability_assessment:
|
||||
dynamic_capabilities:
|
||||
# Assess server capabilities in real-time
|
||||
assessment_interval: 60 # seconds
|
||||
capability_metrics:
|
||||
- processing_speed
|
||||
- accuracy_score
|
||||
- specialization_match
|
||||
- current_load
|
||||
|
||||
capability_mapping:
|
||||
# Map operations to server capabilities
|
||||
magic:
|
||||
specializations: ["ui", "components", "design", "frontend"]
|
||||
performance_profile: "medium_latency_high_quality"
|
||||
optimal_load: 3
|
||||
|
||||
sequential:
|
||||
specializations: ["analysis", "debugging", "complex_reasoning"]
|
||||
performance_profile: "high_latency_high_quality"
|
||||
optimal_load: 2
|
||||
|
||||
context7:
|
||||
specializations: ["documentation", "learning", "research"]
|
||||
performance_profile: "low_latency_medium_quality"
|
||||
optimal_load: 5
|
||||
|
||||
serena:
|
||||
specializations: ["refactoring", "large_codebases", "semantic_analysis"]
|
||||
performance_profile: "medium_latency_high_precision"
|
||||
optimal_load: 3
|
||||
|
||||
morphllm:
|
||||
specializations: ["fast_edits", "transformations", "pattern_matching"]
|
||||
performance_profile: "low_latency_medium_quality"
|
||||
optimal_load: 4
|
||||
|
||||
playwright:
|
||||
specializations: ["testing", "validation", "browser_automation"]
|
||||
performance_profile: "high_latency_specialized"
|
||||
optimal_load: 2
|
||||
|
||||
# Error Handling and Recovery
|
||||
error_handling:
|
||||
retry_strategies:
|
||||
# Server error retry patterns
|
||||
exponential_backoff:
|
||||
initial_delay: 1 # seconds
|
||||
max_delay: 60 # seconds
|
||||
multiplier: 2
|
||||
max_retries: 3
|
||||
|
||||
graceful_degradation:
|
||||
# Fallback when servers fail
|
||||
degradation_levels:
|
||||
- level: 1
|
||||
strategy: "use_secondary_server"
|
||||
performance_impact: "minimal"
|
||||
|
||||
- level: 2
|
||||
strategy: "reduce_functionality"
|
||||
performance_impact: "moderate"
|
||||
|
||||
- level: 3
|
||||
strategy: "basic_operation_only"
|
||||
performance_impact: "significant"
|
||||
|
||||
circuit_breaker:
|
||||
# Circuit breaker pattern for failing servers
|
||||
failure_threshold: 5 # failures before opening circuit
|
||||
recovery_timeout: 30 # seconds before attempting recovery
|
||||
half_open_requests: 3 # test requests during recovery
|
||||
|
||||
# Performance Optimization
|
||||
performance_optimization:
|
||||
caching:
|
||||
# Server response caching
|
||||
enable_response_caching: true
|
||||
cache_duration: 300 # seconds
|
||||
max_cache_size: 100 # responses
|
||||
cache_key_strategy: "operation_context_hash"
|
||||
|
||||
request_optimization:
|
||||
# Request batching and optimization
|
||||
enable_request_batching: true
|
||||
batch_size: 3
|
||||
batch_timeout: 1000 # ms
|
||||
|
||||
predictive_routing:
|
||||
# Predict optimal server based on patterns
|
||||
enable_prediction: true
|
||||
prediction_model: "pattern_based"
|
||||
prediction_confidence_threshold: 0.7
|
||||
|
||||
# Monitoring and Analytics
|
||||
monitoring:
|
||||
metrics_collection:
|
||||
# Collect orchestration metrics
|
||||
collect_routing_decisions: true
|
||||
collect_performance_metrics: true
|
||||
collect_error_patterns: true
|
||||
retention_days: 30
|
||||
|
||||
analytics:
|
||||
# Server orchestration analytics
|
||||
routing_accuracy_tracking: true
|
||||
performance_trend_analysis: true
|
||||
optimization_recommendations: true
|
||||
|
||||
alerts:
|
||||
# Alert thresholds
|
||||
high_error_rate: 0.1 # 10%
|
||||
slow_response_time: 5000 # ms
|
||||
server_unavailable: true
|
||||
@@ -1,59 +0,0 @@
|
||||
# Mode detection patterns for SuperClaude-Lite
|
||||
mode_detection:
|
||||
brainstorming:
|
||||
enabled: true
|
||||
trigger_patterns:
|
||||
- "I want to build"
|
||||
- "thinking about"
|
||||
- "not sure"
|
||||
- "maybe.*could"
|
||||
- "brainstorm"
|
||||
- "explore"
|
||||
- "figure out"
|
||||
- "unclear.*requirements"
|
||||
- "ambiguous.*needs"
|
||||
confidence_threshold: 0.7
|
||||
auto_activate: true
|
||||
|
||||
task_management:
|
||||
enabled: true
|
||||
trigger_patterns:
|
||||
- "multiple.*tasks"
|
||||
- "complex.*system"
|
||||
- "build.*comprehensive"
|
||||
- "coordinate.*work"
|
||||
- "large-scale.*operation"
|
||||
- "manage.*operations"
|
||||
- "comprehensive.*refactoring"
|
||||
- "authentication.*system"
|
||||
confidence_threshold: 0.7
|
||||
auto_activate: true
|
||||
auto_activation_thresholds:
|
||||
file_count: 3
|
||||
complexity_score: 0.4
|
||||
|
||||
token_efficiency:
|
||||
enabled: true
|
||||
trigger_patterns:
|
||||
- "brief"
|
||||
- "concise"
|
||||
- "compressed"
|
||||
- "efficient.*output"
|
||||
- "token.*optimization"
|
||||
- "short.*response"
|
||||
- "running.*low.*context"
|
||||
confidence_threshold: 0.75
|
||||
auto_activate: true
|
||||
|
||||
introspection:
|
||||
enabled: true
|
||||
trigger_patterns:
|
||||
- "analyze.*reasoning"
|
||||
- "examine.*decision"
|
||||
- "reflect.*on"
|
||||
- "meta.*cognitive"
|
||||
- "thinking.*process"
|
||||
- "reasoning.*process"
|
||||
- "decision.*made"
|
||||
confidence_threshold: 0.6
|
||||
auto_activate: true
|
||||
@@ -1,97 +0,0 @@
|
||||
# Orchestrator routing patterns
|
||||
routing_patterns:
|
||||
context7:
|
||||
triggers:
|
||||
- "library.*documentation"
|
||||
- "framework.*patterns"
|
||||
- "react|vue|angular"
|
||||
- "official.*way"
|
||||
- "React Query"
|
||||
- "integrate.*library"
|
||||
capabilities: ["documentation", "patterns", "integration"]
|
||||
priority: "medium"
|
||||
|
||||
sequential:
|
||||
triggers:
|
||||
- "analyze.*complex"
|
||||
- "debug.*systematic"
|
||||
- "troubleshoot.*bottleneck"
|
||||
- "investigate.*root"
|
||||
- "debug.*why"
|
||||
- "detailed.*analysis"
|
||||
- "running.*slowly"
|
||||
- "performance.*bottleneck"
|
||||
- "bundle.*size"
|
||||
capabilities: ["analysis", "debugging", "systematic"]
|
||||
priority: "high"
|
||||
|
||||
magic:
|
||||
triggers:
|
||||
- "component.*ui"
|
||||
- "responsive.*modal"
|
||||
- "navigation.*component"
|
||||
- "mobile.*friendly"
|
||||
- "responsive.*dashboard"
|
||||
- "charts.*real-time"
|
||||
- "build.*dashboard"
|
||||
capabilities: ["ui", "components", "responsive"]
|
||||
priority: "medium"
|
||||
|
||||
playwright:
|
||||
triggers:
|
||||
- "test.*workflow"
|
||||
- "browser.*automation"
|
||||
- "cross-browser.*testing"
|
||||
- "performance.*testing"
|
||||
- "end-to-end.*tests"
|
||||
- "checkout.*flow"
|
||||
- "e2e.*tests"
|
||||
capabilities: ["testing", "automation", "e2e"]
|
||||
priority: "medium"
|
||||
|
||||
morphllm:
|
||||
triggers:
|
||||
- "edit.*file"
|
||||
- "simple.*modification"
|
||||
- "quick.*change"
|
||||
capabilities: ["editing", "modification"]
|
||||
priority: "low"
|
||||
|
||||
serena:
|
||||
triggers:
|
||||
- "refactor.*codebase"
|
||||
- "complex.*analysis"
|
||||
- "multi.*file"
|
||||
- "refactor.*entire"
|
||||
- "new.*API.*patterns"
|
||||
capabilities: ["refactoring", "semantic", "large-scale"]
|
||||
priority: "high"
|
||||
|
||||
# Auto-activation thresholds
|
||||
auto_activation:
|
||||
complexity_thresholds:
|
||||
enable_delegation:
|
||||
file_count: 3
|
||||
directory_count: 2
|
||||
complexity_score: 0.4
|
||||
enable_sequential:
|
||||
complexity_score: 0.6
|
||||
enable_validation:
|
||||
risk_level: ["high", "critical"]
|
||||
|
||||
# Hybrid intelligence selection
|
||||
hybrid_intelligence:
|
||||
morphllm_vs_serena:
|
||||
morphllm_criteria:
|
||||
file_count_max: 10
|
||||
complexity_max: 0.6
|
||||
preferred_operations: ["edit", "modify", "simple_refactor"]
|
||||
serena_criteria:
|
||||
file_count_min: 5
|
||||
complexity_min: 0.4
|
||||
preferred_operations: ["refactor", "analyze", "extract", "move"]
|
||||
|
||||
# Performance optimization
|
||||
performance_optimization:
|
||||
resource_management:
|
||||
token_threshold_percent: 75
|
||||
@@ -1,346 +0,0 @@
|
||||
# SuperClaude-Lite Performance Configuration
|
||||
# Performance targets, thresholds, and optimization strategies
|
||||
|
||||
# Hook Performance Targets
|
||||
hook_targets:
|
||||
session_start:
|
||||
target_ms: 50
|
||||
warning_threshold_ms: 75
|
||||
critical_threshold_ms: 100
|
||||
optimization_priority: "critical"
|
||||
|
||||
pre_tool_use:
|
||||
target_ms: 200
|
||||
warning_threshold_ms: 300
|
||||
critical_threshold_ms: 500
|
||||
optimization_priority: "high"
|
||||
|
||||
post_tool_use:
|
||||
target_ms: 100
|
||||
warning_threshold_ms: 150
|
||||
critical_threshold_ms: 250
|
||||
optimization_priority: "medium"
|
||||
|
||||
pre_compact:
|
||||
target_ms: 150
|
||||
warning_threshold_ms: 200
|
||||
critical_threshold_ms: 300
|
||||
optimization_priority: "high"
|
||||
|
||||
notification:
|
||||
target_ms: 100
|
||||
warning_threshold_ms: 150
|
||||
critical_threshold_ms: 200
|
||||
optimization_priority: "medium"
|
||||
|
||||
stop:
|
||||
target_ms: 200
|
||||
warning_threshold_ms: 300
|
||||
critical_threshold_ms: 500
|
||||
optimization_priority: "low"
|
||||
|
||||
subagent_stop:
|
||||
target_ms: 150
|
||||
warning_threshold_ms: 200
|
||||
critical_threshold_ms: 300
|
||||
optimization_priority: "medium"
|
||||
|
||||
# System Performance Targets
|
||||
system_targets:
|
||||
overall_session_efficiency: 0.75
|
||||
mcp_coordination_efficiency: 0.70
|
||||
compression_effectiveness: 0.50
|
||||
learning_adaptation_rate: 0.80
|
||||
user_satisfaction_target: 0.75
|
||||
|
||||
resource_utilization:
|
||||
memory_target_mb: 100
|
||||
memory_warning_mb: 150
|
||||
memory_critical_mb: 200
|
||||
|
||||
cpu_target_percent: 40
|
||||
cpu_warning_percent: 60
|
||||
cpu_critical_percent: 80
|
||||
|
||||
token_efficiency_target: 0.40
|
||||
token_warning_threshold: 0.20
|
||||
token_critical_threshold: 0.10
|
||||
|
||||
# MCP Server Performance
|
||||
mcp_server_performance:
|
||||
context7:
|
||||
activation_target_ms: 150
|
||||
response_target_ms: 500
|
||||
cache_hit_ratio_target: 0.70
|
||||
quality_score_target: 0.90
|
||||
|
||||
sequential:
|
||||
activation_target_ms: 200
|
||||
response_target_ms: 1000
|
||||
analysis_depth_target: 0.80
|
||||
reasoning_quality_target: 0.85
|
||||
|
||||
magic:
|
||||
activation_target_ms: 120
|
||||
response_target_ms: 800
|
||||
component_quality_target: 0.85
|
||||
generation_speed_target: 0.75
|
||||
|
||||
playwright:
|
||||
activation_target_ms: 300
|
||||
response_target_ms: 2000
|
||||
test_reliability_target: 0.90
|
||||
automation_efficiency_target: 0.80
|
||||
|
||||
morphllm:
|
||||
activation_target_ms: 80
|
||||
response_target_ms: 400
|
||||
edit_accuracy_target: 0.95
|
||||
processing_efficiency_target: 0.85
|
||||
|
||||
serena:
|
||||
activation_target_ms: 100
|
||||
response_target_ms: 600
|
||||
semantic_accuracy_target: 0.90
|
||||
memory_efficiency_target: 0.80
|
||||
|
||||
# Compression Performance
|
||||
compression_performance:
|
||||
target_compression_ratio: 0.50
|
||||
quality_preservation_minimum: 0.95
|
||||
processing_speed_target_chars_per_ms: 100
|
||||
|
||||
level_targets:
|
||||
minimal:
|
||||
compression_ratio: 0.15
|
||||
quality_preservation: 0.98
|
||||
processing_time_factor: 1.0
|
||||
|
||||
efficient:
|
||||
compression_ratio: 0.40
|
||||
quality_preservation: 0.95
|
||||
processing_time_factor: 1.2
|
||||
|
||||
compressed:
|
||||
compression_ratio: 0.60
|
||||
quality_preservation: 0.90
|
||||
processing_time_factor: 1.5
|
||||
|
||||
critical:
|
||||
compression_ratio: 0.75
|
||||
quality_preservation: 0.85
|
||||
processing_time_factor: 1.8
|
||||
|
||||
emergency:
|
||||
compression_ratio: 0.85
|
||||
quality_preservation: 0.80
|
||||
processing_time_factor: 2.0
|
||||
|
||||
# Learning Engine Performance
|
||||
learning_performance:
|
||||
adaptation_response_time_ms: 200
|
||||
pattern_detection_accuracy: 0.80
|
||||
effectiveness_prediction_accuracy: 0.75
|
||||
|
||||
learning_rates:
|
||||
user_preference_learning: 0.85
|
||||
operation_pattern_learning: 0.80
|
||||
performance_optimization_learning: 0.75
|
||||
error_recovery_learning: 0.90
|
||||
|
||||
memory_efficiency:
|
||||
learning_data_compression_ratio: 0.30
|
||||
memory_cleanup_efficiency: 0.90
|
||||
cache_hit_ratio: 0.70
|
||||
|
||||
# Quality Gate Performance
|
||||
quality_gate_performance:
|
||||
validation_speed_targets:
|
||||
syntax_validation_ms: 50
|
||||
type_analysis_ms: 100
|
||||
code_quality_ms: 150
|
||||
security_assessment_ms: 200
|
||||
performance_analysis_ms: 250
|
||||
|
||||
accuracy_targets:
|
||||
rule_compliance_detection: 0.95
|
||||
principle_alignment_assessment: 0.90
|
||||
quality_scoring_accuracy: 0.85
|
||||
security_vulnerability_detection: 0.98
|
||||
|
||||
comprehensive_validation_target_ms: 500
|
||||
|
||||
# Task Management Performance
|
||||
task_management_performance:
|
||||
delegation_efficiency_targets:
|
||||
file_based_delegation: 0.65
|
||||
folder_based_delegation: 0.70
|
||||
auto_delegation: 0.75
|
||||
|
||||
wave_orchestration_targets:
|
||||
coordination_overhead_max: 0.20
|
||||
wave_synchronization_efficiency: 0.85
|
||||
parallel_execution_speedup: 1.50
|
||||
|
||||
task_completion_targets:
|
||||
success_rate: 0.90
|
||||
quality_score: 0.80
|
||||
time_efficiency: 0.75
|
||||
|
||||
# Mode-Specific Performance
|
||||
mode_performance:
|
||||
brainstorming:
|
||||
dialogue_response_time_ms: 300
|
||||
convergence_efficiency: 0.80
|
||||
brief_generation_quality: 0.85
|
||||
user_satisfaction_target: 0.85
|
||||
|
||||
task_management:
|
||||
coordination_overhead_max: 0.15
|
||||
delegation_efficiency: 0.70
|
||||
parallel_execution_benefit: 1.40
|
||||
analytics_generation_time_ms: 500
|
||||
|
||||
token_efficiency:
|
||||
compression_processing_time_ms: 150
|
||||
efficiency_gain_target: 0.40
|
||||
quality_preservation_target: 0.95
|
||||
user_acceptance_rate: 0.80
|
||||
|
||||
introspection:
|
||||
analysis_depth_target: 0.80
|
||||
insight_quality_target: 0.75
|
||||
transparency_effectiveness: 0.85
|
||||
learning_value_target: 0.70
|
||||
|
||||
# Performance Monitoring
|
||||
performance_monitoring:
|
||||
real_time_tracking:
|
||||
enabled: true
|
||||
sampling_interval_ms: 100
|
||||
metric_aggregation_window_s: 60
|
||||
alert_threshold_breaches: 3
|
||||
|
||||
metrics_collection:
|
||||
execution_times: true
|
||||
resource_utilization: true
|
||||
quality_scores: true
|
||||
user_satisfaction: true
|
||||
error_rates: true
|
||||
|
||||
alerting:
|
||||
performance_degradation: true
|
||||
resource_exhaustion: true
|
||||
quality_threshold_breach: true
|
||||
user_satisfaction_drop: true
|
||||
|
||||
reporting:
|
||||
hourly_summaries: true
|
||||
daily_analytics: true
|
||||
weekly_trends: true
|
||||
monthly_optimization_reports: true
|
||||
|
||||
# Optimization Strategies
|
||||
optimization_strategies:
|
||||
caching:
|
||||
intelligent_caching: true
|
||||
cache_warming: true
|
||||
predictive_loading: true
|
||||
cache_invalidation: "smart"
|
||||
|
||||
parallel_processing:
|
||||
auto_detection: true
|
||||
optimal_concurrency: "dynamic"
|
||||
load_balancing: "intelligent"
|
||||
resource_coordination: "adaptive"
|
||||
|
||||
resource_management:
|
||||
memory_optimization: true
|
||||
cpu_optimization: true
|
||||
token_optimization: true
|
||||
storage_optimization: true
|
||||
|
||||
adaptive_performance:
|
||||
dynamic_target_adjustment: true
|
||||
context_aware_optimization: true
|
||||
learning_based_improvement: true
|
||||
user_preference_integration: true
|
||||
|
||||
# Performance Thresholds
|
||||
performance_thresholds:
|
||||
green_zone: # 0-70% resource usage
|
||||
all_optimizations_available: true
|
||||
proactive_caching: true
|
||||
full_feature_set: true
|
||||
normal_verbosity: true
|
||||
|
||||
yellow_zone: # 70-85% resource usage
|
||||
efficiency_mode_activation: true
|
||||
cache_optimization: true
|
||||
reduced_verbosity: true
|
||||
non_critical_feature_deferral: true
|
||||
|
||||
orange_zone: # 85-95% resource usage
|
||||
aggressive_optimization: true
|
||||
compression_activation: true
|
||||
feature_reduction: true
|
||||
essential_operations_only: true
|
||||
|
||||
red_zone: # 95%+ resource usage
|
||||
emergency_mode: true
|
||||
maximum_compression: true
|
||||
minimal_features: true
|
||||
critical_operations_only: true
|
||||
|
||||
# Fallback Performance
|
||||
fallback_performance:
|
||||
graceful_degradation:
|
||||
feature_prioritization: true
|
||||
quality_vs_speed_tradeoffs: "intelligent"
|
||||
user_notification: true
|
||||
automatic_recovery: true
|
||||
|
||||
emergency_protocols:
|
||||
resource_exhaustion: "immediate_compression"
|
||||
timeout_protection: "operation_cancellation"
|
||||
error_cascade_prevention: "circuit_breaker"
|
||||
|
||||
recovery_strategies:
|
||||
performance_restoration: "gradual"
|
||||
feature_reactivation: "conditional"
|
||||
quality_normalization: "monitored"
|
||||
|
||||
# Benchmarking and Testing
|
||||
benchmarking:
|
||||
performance_baselines:
|
||||
establish_on_startup: true
|
||||
regular_recalibration: true
|
||||
environment_specific: true
|
||||
|
||||
load_testing:
|
||||
synthetic_workloads: true
|
||||
stress_testing: true
|
||||
endurance_testing: true
|
||||
|
||||
regression_testing:
|
||||
performance_regression_detection: true
|
||||
quality_regression_detection: true
|
||||
feature_regression_detection: true
|
||||
|
||||
# Integration Performance
|
||||
integration_performance:
|
||||
cross_hook_coordination: 0.90
|
||||
mcp_server_orchestration: 0.85
|
||||
mode_switching_efficiency: 0.80
|
||||
learning_engine_responsiveness: 0.85
|
||||
|
||||
end_to_end_targets:
|
||||
session_initialization: 500 # ms
|
||||
complex_operation_completion: 5000 # ms
|
||||
session_termination: 1000 # ms
|
||||
|
||||
system_health_indicators:
|
||||
overall_efficiency: 0.75
|
||||
user_experience_quality: 0.80
|
||||
system_reliability: 0.95
|
||||
adaptation_effectiveness: 0.70
|
||||
@@ -1,299 +0,0 @@
|
||||
# Performance Intelligence Configuration
|
||||
# Adaptive performance patterns, auto-optimization, and resource management
|
||||
# Enables intelligent performance monitoring and self-optimization
|
||||
|
||||
# Metadata
|
||||
version: "1.0.0"
|
||||
last_updated: "2025-01-06"
|
||||
description: "Performance intelligence and auto-optimization patterns"
|
||||
|
||||
# Adaptive Performance Targets
|
||||
adaptive_targets:
|
||||
baseline_management:
|
||||
# Dynamic baseline adjustment based on system performance
|
||||
adjustment_strategy: "rolling_average"
|
||||
adjustment_window: 50 # operations
|
||||
adjustment_sensitivity: 0.15 # 15% threshold for adjustment
|
||||
min_samples: 10 # minimum samples before adjustment
|
||||
|
||||
baseline_metrics:
|
||||
response_time:
|
||||
initial_target: 500 # ms
|
||||
acceptable_variance: 0.3
|
||||
improvement_threshold: 0.1
|
||||
|
||||
resource_usage:
|
||||
initial_target: 0.7 # 70%
|
||||
acceptable_variance: 0.2
|
||||
critical_threshold: 0.9
|
||||
|
||||
error_rate:
|
||||
initial_target: 0.02 # 2%
|
||||
acceptable_variance: 0.01
|
||||
critical_threshold: 0.1
|
||||
|
||||
target_adaptation:
|
||||
# How targets adapt to system capabilities
|
||||
adaptation_triggers:
|
||||
- condition: {performance_improvement: ">20%", duration: ">7_days"}
|
||||
action: "tighten_targets"
|
||||
adjustment: 0.15
|
||||
|
||||
- condition: {performance_degradation: ">15%", duration: ">3_days"}
|
||||
action: "relax_targets"
|
||||
adjustment: 0.2
|
||||
|
||||
- condition: {system_upgrade_detected: true}
|
||||
action: "recalibrate_baselines"
|
||||
reset_period: "24_hours"
|
||||
|
||||
adaptation_limits:
|
||||
max_target_tightening: 0.5 # Don't make targets too aggressive
|
||||
max_target_relaxation: 2.0 # Don't make targets too loose
|
||||
adaptation_cooldown: 3600 # seconds between major adjustments
|
||||
|
||||
# Auto-Optimization Engine
|
||||
auto_optimization:
|
||||
optimization_triggers:
|
||||
# Automatic optimization triggers
|
||||
performance_triggers:
|
||||
- name: "response_time_degradation"
|
||||
condition: {avg_response_time: ">target*1.3", samples: ">10"}
|
||||
urgency: "high"
|
||||
actions: ["enable_aggressive_caching", "reduce_analysis_depth", "parallel_processing"]
|
||||
|
||||
- name: "memory_pressure"
|
||||
condition: {memory_usage: ">0.85", duration: ">300_seconds"}
|
||||
urgency: "critical"
|
||||
actions: ["garbage_collection", "cache_cleanup", "reduce_context_size"]
|
||||
|
||||
- name: "cpu_saturation"
|
||||
condition: {cpu_usage: ">0.9", duration: ">60_seconds"}
|
||||
urgency: "high"
|
||||
actions: ["reduce_concurrent_operations", "defer_non_critical", "enable_throttling"]
|
||||
|
||||
- name: "error_rate_spike"
|
||||
condition: {error_rate: ">0.1", recent_window: "5_minutes"}
|
||||
urgency: "critical"
|
||||
actions: ["enable_fallback_mode", "increase_timeouts", "reduce_complexity"]
|
||||
|
||||
optimization_strategies:
|
||||
# Available optimization strategies
|
||||
aggressive_caching:
|
||||
description: "Enable aggressive caching of results and computations"
|
||||
performance_impact: 0.3 # Expected improvement
|
||||
resource_cost: 0.1 # Memory cost
|
||||
duration: 1800 # seconds
|
||||
|
||||
parallel_processing:
|
||||
description: "Increase parallelization where possible"
|
||||
performance_impact: 0.25
|
||||
resource_cost: 0.2
|
||||
duration: 3600
|
||||
|
||||
reduce_analysis_depth:
|
||||
description: "Reduce depth of analysis to improve speed"
|
||||
performance_impact: 0.4
|
||||
quality_impact: -0.1 # Slight quality reduction
|
||||
duration: 1800
|
||||
|
||||
intelligent_batching:
|
||||
description: "Batch similar operations for efficiency"
|
||||
performance_impact: 0.2
|
||||
resource_cost: -0.05 # Reduces resource usage
|
||||
duration: 3600
|
||||
|
||||
# Resource Management Intelligence
|
||||
resource_management:
|
||||
resource_zones:
|
||||
# Performance zones with different strategies
|
||||
green_zone:
|
||||
threshold: 0.60 # Below 60% resource usage
|
||||
strategy: "optimal_performance"
|
||||
features_enabled: ["full_analysis", "comprehensive_caching", "background_optimization"]
|
||||
|
||||
yellow_zone:
|
||||
threshold: 0.75 # 60-75% resource usage
|
||||
strategy: "balanced_optimization"
|
||||
features_enabled: ["standard_analysis", "selective_caching", "reduced_background"]
|
||||
optimizations: ["defer_non_critical", "reduce_verbosity"]
|
||||
|
||||
orange_zone:
|
||||
threshold: 0.85 # 75-85% resource usage
|
||||
strategy: "performance_preservation"
|
||||
features_enabled: ["essential_analysis", "minimal_caching"]
|
||||
optimizations: ["aggressive_caching", "parallel_where_safe", "reduce_context"]
|
||||
|
||||
red_zone:
|
||||
threshold: 0.95 # 85-95% resource usage
|
||||
strategy: "resource_conservation"
|
||||
features_enabled: ["critical_only"]
|
||||
optimizations: ["emergency_cleanup", "minimal_processing", "fail_fast"]
|
||||
|
||||
critical_zone:
|
||||
threshold: 1.0 # Above 95% resource usage
|
||||
strategy: "emergency_mode"
|
||||
features_enabled: []
|
||||
optimizations: ["immediate_cleanup", "operation_rejection", "system_protection"]
|
||||
|
||||
dynamic_allocation:
|
||||
# Intelligent resource allocation
|
||||
allocation_strategies:
|
||||
workload_based:
|
||||
description: "Allocate based on current workload patterns"
|
||||
factors: ["operation_complexity", "expected_duration", "priority"]
|
||||
|
||||
predictive:
|
||||
description: "Allocate based on predicted resource needs"
|
||||
factors: ["historical_patterns", "operation_type", "context_size"]
|
||||
|
||||
adaptive:
|
||||
description: "Adapt allocation based on real-time performance"
|
||||
factors: ["current_performance", "resource_availability", "optimization_goals"]
|
||||
|
||||
# Performance Regression Detection
|
||||
regression_detection:
|
||||
detection_algorithms:
|
||||
# Algorithms for detecting performance regression
|
||||
statistical_analysis:
|
||||
algorithm: "t_test"
|
||||
confidence_level: 0.95
|
||||
minimum_samples: 20
|
||||
window_size: 100 # operations
|
||||
|
||||
trend_analysis:
|
||||
algorithm: "linear_regression"
|
||||
trend_threshold: 0.1 # 10% degradation trend
|
||||
analysis_window: 168 # hours (1 week)
|
||||
|
||||
anomaly_detection:
|
||||
algorithm: "isolation_forest"
|
||||
contamination: 0.1 # Expected anomaly rate
|
||||
sensitivity: 0.8
|
||||
|
||||
regression_patterns:
|
||||
# Common regression patterns to detect
|
||||
gradual_degradation:
|
||||
pattern: {performance_trend: "decreasing", duration: ">5_days"}
|
||||
severity: "medium"
|
||||
investigation: "check_for_memory_leaks"
|
||||
|
||||
sudden_degradation:
|
||||
pattern: {performance_drop: ">30%", timeframe: "<1_hour"}
|
||||
severity: "high"
|
||||
investigation: "check_recent_changes"
|
||||
|
||||
periodic_degradation:
|
||||
pattern: {performance_cycles: "detected", frequency: "regular"}
|
||||
severity: "low"
|
||||
investigation: "analyze_periodic_patterns"
|
||||
|
||||
# Intelligent Resource Optimization
|
||||
intelligent_optimization:
|
||||
predictive_optimization:
|
||||
# Predict and prevent performance issues
|
||||
prediction_models:
|
||||
resource_exhaustion:
|
||||
model_type: "time_series"
|
||||
prediction_horizon: 3600 # seconds
|
||||
accuracy_threshold: 0.8
|
||||
|
||||
performance_degradation:
|
||||
model_type: "pattern_matching"
|
||||
pattern_library: "historical_degradations"
|
||||
confidence_threshold: 0.7
|
||||
|
||||
proactive_actions:
|
||||
- prediction: "memory_exhaustion"
|
||||
lead_time: 1800 # seconds
|
||||
actions: ["preemptive_cleanup", "cache_optimization", "context_reduction"]
|
||||
|
||||
- prediction: "cpu_saturation"
|
||||
lead_time: 600 # seconds
|
||||
actions: ["reduce_parallelism", "defer_background_tasks", "enable_throttling"]
|
||||
|
||||
optimization_recommendations:
|
||||
# Generate optimization recommendations
|
||||
recommendation_engine:
|
||||
analysis_depth: "comprehensive"
|
||||
recommendation_confidence: 0.8
|
||||
implementation_difficulty: "user_friendly"
|
||||
|
||||
recommendation_types:
|
||||
configuration_tuning:
|
||||
description: "Suggest configuration changes for better performance"
|
||||
impact_assessment: "quantified"
|
||||
|
||||
resource_allocation:
|
||||
description: "Recommend better resource allocation strategies"
|
||||
cost_benefit_analysis: true
|
||||
|
||||
workflow_optimization:
|
||||
description: "Suggest workflow improvements"
|
||||
user_experience_impact: "minimal"
|
||||
|
||||
# Performance Monitoring Intelligence
|
||||
monitoring_intelligence:
|
||||
intelligent_metrics:
|
||||
# Smart metric collection and analysis
|
||||
adaptive_sampling:
|
||||
base_sampling_rate: 1.0 # Sample every operation
|
||||
high_load_rate: 0.5 # Reduce sampling under load
|
||||
critical_load_rate: 0.1 # Minimal sampling in critical situations
|
||||
|
||||
contextual_metrics:
|
||||
# Collect different metrics based on context
|
||||
ui_operations:
|
||||
focus_metrics: ["response_time", "render_time", "user_interaction_delay"]
|
||||
|
||||
analysis_operations:
|
||||
focus_metrics: ["processing_time", "memory_usage", "accuracy_score"]
|
||||
|
||||
batch_operations:
|
||||
focus_metrics: ["throughput", "resource_efficiency", "completion_rate"]
|
||||
|
||||
performance_insights:
|
||||
# Generate performance insights
|
||||
insight_generation:
|
||||
pattern_recognition: true
|
||||
correlation_analysis: true
|
||||
root_cause_analysis: true
|
||||
improvement_suggestions: true
|
||||
|
||||
insight_types:
|
||||
bottleneck_identification:
|
||||
description: "Identify performance bottlenecks"
|
||||
priority: "high"
|
||||
|
||||
optimization_opportunities:
|
||||
description: "Find optimization opportunities"
|
||||
priority: "medium"
|
||||
|
||||
capacity_planning:
|
||||
description: "Predict capacity requirements"
|
||||
priority: "low"
|
||||
|
||||
# Performance Validation
|
||||
performance_validation:
|
||||
validation_framework:
|
||||
# Validate performance improvements
|
||||
a_b_testing:
|
||||
enable_automatic_testing: true
|
||||
test_duration: 3600 # seconds
|
||||
statistical_significance: 0.95
|
||||
|
||||
performance_benchmarking:
|
||||
benchmark_frequency: "weekly"
|
||||
regression_threshold: 0.05 # 5% regression tolerance
|
||||
|
||||
continuous_improvement:
|
||||
# Continuous performance improvement
|
||||
improvement_tracking:
|
||||
track_optimization_effectiveness: true
|
||||
measure_user_satisfaction: true
|
||||
monitor_system_health: true
|
||||
|
||||
feedback_loops:
|
||||
performance_feedback: "real_time"
|
||||
user_feedback_integration: true
|
||||
system_learning_integration: true
|
||||
@@ -1,351 +0,0 @@
|
||||
# SuperClaude-Lite Session Configuration
|
||||
# SessionStart/Stop lifecycle management and analytics
|
||||
|
||||
# Session Lifecycle Configuration
|
||||
session_lifecycle:
|
||||
initialization:
|
||||
performance_target_ms: 50
|
||||
auto_project_detection: true
|
||||
context_loading_strategy: "selective"
|
||||
framework_exclusion_enabled: true
|
||||
|
||||
default_modes:
|
||||
- "adaptive_intelligence"
|
||||
- "performance_monitoring"
|
||||
|
||||
intelligence_activation:
|
||||
pattern_detection: true
|
||||
mcp_routing: true
|
||||
learning_integration: true
|
||||
compression_optimization: true
|
||||
|
||||
termination:
|
||||
performance_target_ms: 200
|
||||
analytics_generation: true
|
||||
learning_consolidation: true
|
||||
session_persistence: true
|
||||
cleanup_optimization: true
|
||||
|
||||
# Project Type Detection
|
||||
project_detection:
|
||||
file_indicators:
|
||||
nodejs:
|
||||
- "package.json"
|
||||
- "node_modules/"
|
||||
- "yarn.lock"
|
||||
- "pnpm-lock.yaml"
|
||||
|
||||
python:
|
||||
- "pyproject.toml"
|
||||
- "setup.py"
|
||||
- "requirements.txt"
|
||||
- "__pycache__/"
|
||||
- ".py"
|
||||
|
||||
rust:
|
||||
- "Cargo.toml"
|
||||
- "Cargo.lock"
|
||||
- "src/main.rs"
|
||||
- "src/lib.rs"
|
||||
|
||||
go:
|
||||
- "go.mod"
|
||||
- "go.sum"
|
||||
- "main.go"
|
||||
|
||||
web_frontend:
|
||||
- "index.html"
|
||||
- "public/"
|
||||
- "dist/"
|
||||
- "build/"
|
||||
- "src/components/"
|
||||
|
||||
framework_detection:
|
||||
react:
|
||||
- "react"
|
||||
- "next.js"
|
||||
- "@types/react"
|
||||
|
||||
vue:
|
||||
- "vue"
|
||||
- "nuxt"
|
||||
- "@vue/cli"
|
||||
|
||||
angular:
|
||||
- "@angular/core"
|
||||
- "angular.json"
|
||||
|
||||
express:
|
||||
- "express"
|
||||
- "app.js"
|
||||
- "server.js"
|
||||
|
||||
# Intelligence Activation Rules
|
||||
intelligence_activation:
|
||||
mode_detection:
|
||||
brainstorming:
|
||||
triggers:
|
||||
- "new project"
|
||||
- "not sure"
|
||||
- "thinking about"
|
||||
- "explore"
|
||||
- "brainstorm"
|
||||
confidence_threshold: 0.7
|
||||
auto_activate: true
|
||||
|
||||
task_management:
|
||||
triggers:
|
||||
- "multiple files"
|
||||
- "complex operation"
|
||||
- "system-wide"
|
||||
- "comprehensive"
|
||||
file_count_threshold: 3
|
||||
complexity_threshold: 0.4
|
||||
auto_activate: true
|
||||
|
||||
token_efficiency:
|
||||
triggers:
|
||||
- "resource constraint"
|
||||
- "brevity"
|
||||
- "compressed"
|
||||
- "efficient"
|
||||
resource_threshold_percent: 75
|
||||
conversation_length_threshold: 100
|
||||
auto_activate: true
|
||||
|
||||
mcp_server_activation:
|
||||
context7:
|
||||
triggers:
|
||||
- "library"
|
||||
- "documentation"
|
||||
- "framework"
|
||||
- "api reference"
|
||||
project_indicators:
|
||||
- "external_dependencies"
|
||||
- "framework_detected"
|
||||
auto_activate: true
|
||||
|
||||
sequential:
|
||||
triggers:
|
||||
- "analyze"
|
||||
- "debug"
|
||||
- "complex"
|
||||
- "systematic"
|
||||
complexity_threshold: 0.6
|
||||
auto_activate: true
|
||||
|
||||
magic:
|
||||
triggers:
|
||||
- "component"
|
||||
- "ui"
|
||||
- "frontend"
|
||||
- "design"
|
||||
project_type_match: ["web_frontend", "react", "vue", "angular"]
|
||||
auto_activate: true
|
||||
|
||||
playwright:
|
||||
triggers:
|
||||
- "test"
|
||||
- "automation"
|
||||
- "browser"
|
||||
- "e2e"
|
||||
project_indicators:
|
||||
- "has_tests"
|
||||
- "test_framework_detected"
|
||||
auto_activate: false # Manual activation preferred
|
||||
|
||||
morphllm:
|
||||
triggers:
|
||||
- "edit"
|
||||
- "modify"
|
||||
- "quick change"
|
||||
file_count_max: 10
|
||||
complexity_max: 0.6
|
||||
auto_activate: true
|
||||
|
||||
serena:
|
||||
triggers:
|
||||
- "navigate"
|
||||
- "find"
|
||||
- "search"
|
||||
- "analyze"
|
||||
file_count_min: 5
|
||||
complexity_min: 0.4
|
||||
auto_activate: true
|
||||
|
||||
# Session Analytics Configuration
|
||||
session_analytics:
|
||||
performance_tracking:
|
||||
enabled: true
|
||||
metrics:
|
||||
- "operation_count"
|
||||
- "tool_usage_patterns"
|
||||
- "mcp_server_effectiveness"
|
||||
- "error_rates"
|
||||
- "completion_times"
|
||||
- "resource_utilization"
|
||||
|
||||
effectiveness_measurement:
|
||||
enabled: true
|
||||
factors:
|
||||
productivity: "weight: 0.4"
|
||||
quality: "weight: 0.3"
|
||||
user_satisfaction: "weight: 0.2"
|
||||
learning_value: "weight: 0.1"
|
||||
|
||||
learning_consolidation:
|
||||
enabled: true
|
||||
pattern_detection: true
|
||||
adaptation_creation: true
|
||||
effectiveness_feedback: true
|
||||
insight_generation: true
|
||||
|
||||
# Session Persistence
|
||||
session_persistence:
|
||||
enabled: true
|
||||
storage_strategy: "intelligent_compression"
|
||||
retention_policy:
|
||||
session_data_days: 90
|
||||
analytics_data_days: 365
|
||||
learning_data_persistent: true
|
||||
|
||||
compression_settings:
|
||||
session_metadata: "efficient" # 40-70% compression
|
||||
analytics_data: "compressed" # 70-85% compression
|
||||
learning_data: "minimal" # Preserve learning quality
|
||||
|
||||
cleanup_automation:
|
||||
enabled: true
|
||||
old_session_cleanup: true
|
||||
max_sessions_retained: 50
|
||||
storage_optimization: true
|
||||
|
||||
# Notification Processing
|
||||
notifications:
|
||||
enabled: true
|
||||
just_in_time_loading: true
|
||||
pattern_updates: true
|
||||
intelligence_updates: true
|
||||
|
||||
priority_handling:
|
||||
critical: "immediate_processing"
|
||||
high: "fast_track_processing"
|
||||
medium: "standard_processing"
|
||||
low: "background_processing"
|
||||
|
||||
caching_strategy:
|
||||
documentation_cache_minutes: 30
|
||||
pattern_cache_minutes: 60
|
||||
intelligence_cache_minutes: 15
|
||||
|
||||
# Task Management Integration
|
||||
task_management:
|
||||
enabled: true
|
||||
delegation_strategies:
|
||||
files: "file_based_delegation"
|
||||
folders: "directory_based_delegation"
|
||||
auto: "intelligent_auto_detection"
|
||||
|
||||
wave_orchestration:
|
||||
enabled: true
|
||||
complexity_threshold: 0.4
|
||||
file_count_threshold: 3
|
||||
operation_types_threshold: 2
|
||||
|
||||
performance_optimization:
|
||||
parallel_execution: true
|
||||
resource_management: true
|
||||
coordination_efficiency: true
|
||||
|
||||
# User Experience Configuration
|
||||
user_experience:
|
||||
session_feedback:
|
||||
enabled: true
|
||||
satisfaction_tracking: true
|
||||
improvement_suggestions: true
|
||||
|
||||
personalization:
|
||||
enabled: true
|
||||
preference_learning: true
|
||||
adaptation_application: true
|
||||
context_awareness: true
|
||||
|
||||
progressive_enhancement:
|
||||
enabled: true
|
||||
capability_discovery: true
|
||||
feature_introduction: true
|
||||
learning_curve_optimization: true
|
||||
|
||||
# Performance Targets
|
||||
performance_targets:
|
||||
session_start_ms: 50
|
||||
session_stop_ms: 200
|
||||
context_loading_ms: 500
|
||||
analytics_generation_ms: 1000
|
||||
|
||||
efficiency_targets:
|
||||
productivity_score: 0.7
|
||||
quality_score: 0.8
|
||||
satisfaction_score: 0.7
|
||||
learning_value: 0.6
|
||||
|
||||
resource_utilization:
|
||||
memory_efficient: true
|
||||
cpu_optimization: true
|
||||
token_management: true
|
||||
storage_optimization: true
|
||||
|
||||
# Error Handling and Recovery
|
||||
error_handling:
|
||||
graceful_degradation: true
|
||||
fallback_strategies: true
|
||||
error_learning: true
|
||||
recovery_optimization: true
|
||||
|
||||
session_recovery:
|
||||
auto_recovery: true
|
||||
state_preservation: true
|
||||
context_restoration: true
|
||||
learning_retention: true
|
||||
|
||||
error_patterns:
|
||||
detection: true
|
||||
prevention: true
|
||||
learning_integration: true
|
||||
adaptation_triggers: true
|
||||
|
||||
# Integration Configuration
|
||||
integration:
|
||||
mcp_servers:
|
||||
coordination: "seamless"
|
||||
fallback_handling: "automatic"
|
||||
performance_monitoring: "continuous"
|
||||
|
||||
learning_engine:
|
||||
session_learning: true
|
||||
pattern_recognition: true
|
||||
effectiveness_tracking: true
|
||||
adaptation_application: true
|
||||
|
||||
compression_engine:
|
||||
session_data_compression: true
|
||||
quality_preservation: true
|
||||
selective_application: true
|
||||
|
||||
quality_gates:
|
||||
session_validation: true
|
||||
analytics_verification: true
|
||||
learning_quality_assurance: true
|
||||
|
||||
# Development and Debugging
|
||||
development_support:
|
||||
session_debugging: true
|
||||
performance_profiling: true
|
||||
analytics_validation: true
|
||||
learning_verification: true
|
||||
|
||||
metrics_collection:
|
||||
detailed_timing: true
|
||||
resource_tracking: true
|
||||
effectiveness_measurement: true
|
||||
quality_assessment: true
|
||||
@@ -1,385 +0,0 @@
|
||||
# User Experience Intelligence Configuration
|
||||
# UX optimization, project detection, and user-centric intelligence patterns
|
||||
# Enables intelligent user experience through smart defaults and proactive assistance
|
||||
|
||||
# Metadata
|
||||
version: "1.0.0"
|
||||
last_updated: "2025-01-06"
|
||||
description: "User experience optimization and project intelligence patterns"
|
||||
|
||||
# Project Type Detection
|
||||
project_detection:
|
||||
detection_patterns:
|
||||
# Detect project types based on files and structure
|
||||
frontend_frameworks:
|
||||
react_project:
|
||||
file_indicators:
|
||||
- "package.json"
|
||||
- "*.tsx"
|
||||
- "*.jsx"
|
||||
- "react" # in package.json dependencies
|
||||
directory_indicators:
|
||||
- "src/components"
|
||||
- "public"
|
||||
- "node_modules"
|
||||
confidence_threshold: 0.8
|
||||
recommendations:
|
||||
mcp_servers: ["magic", "context7", "playwright"]
|
||||
compression_level: "minimal"
|
||||
performance_focus: "ui_responsiveness"
|
||||
|
||||
vue_project:
|
||||
file_indicators:
|
||||
- "package.json"
|
||||
- "*.vue"
|
||||
- "vue.config.js"
|
||||
- "vue" # in dependencies
|
||||
directory_indicators:
|
||||
- "src/components"
|
||||
- "src/views"
|
||||
confidence_threshold: 0.8
|
||||
recommendations:
|
||||
mcp_servers: ["magic", "context7"]
|
||||
compression_level: "standard"
|
||||
|
||||
angular_project:
|
||||
file_indicators:
|
||||
- "angular.json"
|
||||
- "*.component.ts"
|
||||
- "@angular" # in dependencies
|
||||
directory_indicators:
|
||||
- "src/app"
|
||||
- "e2e"
|
||||
confidence_threshold: 0.9
|
||||
recommendations:
|
||||
mcp_servers: ["magic", "context7", "playwright"]
|
||||
|
||||
backend_frameworks:
|
||||
python_project:
|
||||
file_indicators:
|
||||
- "requirements.txt"
|
||||
- "pyproject.toml"
|
||||
- "setup.py"
|
||||
- "*.py"
|
||||
directory_indicators:
|
||||
- "src"
|
||||
- "tests"
|
||||
- "__pycache__"
|
||||
confidence_threshold: 0.7
|
||||
recommendations:
|
||||
mcp_servers: ["serena", "sequential", "context7"]
|
||||
compression_level: "standard"
|
||||
validation_level: "enhanced"
|
||||
|
||||
node_backend:
|
||||
file_indicators:
|
||||
- "package.json"
|
||||
- "*.js"
|
||||
- "express" # in dependencies
|
||||
- "server.js"
|
||||
directory_indicators:
|
||||
- "routes"
|
||||
- "controllers"
|
||||
- "middleware"
|
||||
confidence_threshold: 0.8
|
||||
recommendations:
|
||||
mcp_servers: ["sequential", "context7", "serena"]
|
||||
|
||||
data_science:
|
||||
jupyter_project:
|
||||
file_indicators:
|
||||
- "*.ipynb"
|
||||
- "requirements.txt"
|
||||
- "conda.yml"
|
||||
directory_indicators:
|
||||
- "notebooks"
|
||||
- "data"
|
||||
confidence_threshold: 0.9
|
||||
recommendations:
|
||||
mcp_servers: ["sequential", "context7"]
|
||||
compression_level: "minimal"
|
||||
analysis_depth: "comprehensive"
|
||||
|
||||
documentation:
|
||||
docs_project:
|
||||
file_indicators:
|
||||
- "*.md"
|
||||
- "docs/"
|
||||
- "README.md"
|
||||
- "mkdocs.yml"
|
||||
directory_indicators:
|
||||
- "docs"
|
||||
- "documentation"
|
||||
confidence_threshold: 0.8
|
||||
recommendations:
|
||||
mcp_servers: ["context7", "sequential"]
|
||||
focus_areas: ["clarity", "completeness"]
|
||||
|
||||
# User Preference Intelligence
|
||||
user_preferences:
|
||||
preference_learning:
|
||||
# Learn user preferences from behavior patterns
|
||||
interaction_patterns:
|
||||
command_preferences:
|
||||
track_command_usage: true
|
||||
track_flag_preferences: true
|
||||
track_workflow_patterns: true
|
||||
learning_window: 100 # operations
|
||||
|
||||
performance_preferences:
|
||||
speed_vs_quality_preference:
|
||||
indicators: ["timeout_tolerance", "quality_acceptance", "performance_complaints"]
|
||||
classification: ["speed_focused", "quality_focused", "balanced"]
|
||||
|
||||
detail_level_preference:
|
||||
indicators: ["verbose_mode_usage", "summary_requests", "detail_requests"]
|
||||
classification: ["concise", "detailed", "adaptive"]
|
||||
|
||||
preference_adaptation:
|
||||
# Adapt behavior based on learned preferences
|
||||
adaptation_strategies:
|
||||
speed_focused_user:
|
||||
optimizations: ["aggressive_caching", "parallel_execution", "reduced_analysis"]
|
||||
ui_changes: ["shorter_responses", "quick_suggestions", "minimal_explanations"]
|
||||
|
||||
quality_focused_user:
|
||||
optimizations: ["comprehensive_analysis", "detailed_validation", "thorough_documentation"]
|
||||
ui_changes: ["detailed_responses", "comprehensive_suggestions", "full_explanations"]
|
||||
|
||||
efficiency_focused_user:
|
||||
optimizations: ["smart_defaults", "workflow_automation", "predictive_suggestions"]
|
||||
ui_changes: ["proactive_help", "automated_optimizations", "efficiency_tips"]
|
||||
|
||||
# Proactive User Assistance
|
||||
proactive_assistance:
|
||||
intelligent_suggestions:
|
||||
# Provide intelligent suggestions based on context
|
||||
optimization_suggestions:
|
||||
- trigger: {repeated_operations: ">5", same_pattern: true}
|
||||
suggestion: "Consider creating a script or alias for this repeated operation"
|
||||
confidence: 0.8
|
||||
category: "workflow_optimization"
|
||||
|
||||
- trigger: {performance_issues: "detected", duration: ">3_sessions"}
|
||||
suggestion: "Performance optimization recommendations available"
|
||||
action: "show_performance_guide"
|
||||
confidence: 0.9
|
||||
category: "performance"
|
||||
|
||||
- trigger: {error_pattern: "recurring", count: ">3"}
|
||||
suggestion: "Automated error recovery pattern available"
|
||||
action: "enable_auto_recovery"
|
||||
confidence: 0.85
|
||||
category: "error_prevention"
|
||||
|
||||
contextual_help:
|
||||
# Provide contextual help and guidance
|
||||
help_triggers:
|
||||
- context: {new_user: true, session_count: "<5"}
|
||||
help_type: "onboarding_guidance"
|
||||
content: "Getting started tips and best practices"
|
||||
|
||||
- context: {error_rate: ">10%", recent_errors: ">3"}
|
||||
help_type: "troubleshooting_assistance"
|
||||
content: "Common error solutions and debugging tips"
|
||||
|
||||
- context: {complex_operation: true, user_expertise: "beginner"}
|
||||
help_type: "step_by_step_guidance"
|
||||
content: "Detailed guidance for complex operations"
|
||||
|
||||
# Smart Defaults Intelligence
|
||||
smart_defaults:
|
||||
context_aware_defaults:
|
||||
# Generate smart defaults based on context
|
||||
project_based_defaults:
|
||||
react_project:
|
||||
default_mcp_servers: ["magic", "context7"]
|
||||
default_compression: "minimal"
|
||||
default_analysis_depth: "ui_focused"
|
||||
default_validation: "component_focused"
|
||||
|
||||
python_project:
|
||||
default_mcp_servers: ["serena", "sequential"]
|
||||
default_compression: "standard"
|
||||
default_analysis_depth: "comprehensive"
|
||||
default_validation: "enhanced"
|
||||
|
||||
documentation_project:
|
||||
default_mcp_servers: ["context7"]
|
||||
default_compression: "minimal"
|
||||
default_analysis_depth: "content_focused"
|
||||
default_validation: "readability_focused"
|
||||
|
||||
dynamic_configuration:
|
||||
# Dynamically adjust configuration
|
||||
configuration_adaptation:
|
||||
performance_based:
|
||||
triggers:
|
||||
- condition: {system_performance: "high"}
|
||||
adjustments: {analysis_depth: "comprehensive", features: "all_enabled"}
|
||||
|
||||
- condition: {system_performance: "low"}
|
||||
adjustments: {analysis_depth: "essential", features: "performance_focused"}
|
||||
|
||||
user_expertise_based:
|
||||
triggers:
|
||||
- condition: {user_expertise: "expert"}
|
||||
adjustments: {verbosity: "minimal", automation: "high", warnings: "reduced"}
|
||||
|
||||
- condition: {user_expertise: "beginner"}
|
||||
adjustments: {verbosity: "detailed", automation: "guided", warnings: "comprehensive"}
|
||||
|
||||
# Error Recovery Intelligence
|
||||
error_recovery:
|
||||
intelligent_error_handling:
|
||||
# Smart error handling and recovery
|
||||
error_classification:
|
||||
user_errors:
|
||||
- type: "syntax_error"
|
||||
recovery: "suggest_correction"
|
||||
user_guidance: "detailed"
|
||||
|
||||
- type: "configuration_error"
|
||||
recovery: "auto_fix_with_approval"
|
||||
user_guidance: "educational"
|
||||
|
||||
- type: "workflow_error"
|
||||
recovery: "suggest_alternative_approach"
|
||||
user_guidance: "workflow_tips"
|
||||
|
||||
system_errors:
|
||||
- type: "performance_degradation"
|
||||
recovery: "automatic_optimization"
|
||||
user_notification: "informational"
|
||||
|
||||
- type: "resource_exhaustion"
|
||||
recovery: "resource_management_mode"
|
||||
user_notification: "status_update"
|
||||
|
||||
- type: "service_unavailable"
|
||||
recovery: "graceful_fallback"
|
||||
user_notification: "service_status"
|
||||
|
||||
recovery_learning:
|
||||
# Learn from error recovery patterns
|
||||
recovery_effectiveness:
|
||||
track_recovery_success: true
|
||||
learn_recovery_patterns: true
|
||||
improve_recovery_strategies: true
|
||||
|
||||
user_recovery_preferences:
|
||||
learn_preferred_recovery: true
|
||||
adapt_recovery_approach: true
|
||||
personalize_error_handling: true
|
||||
|
||||
# User Expertise Detection
|
||||
expertise_detection:
|
||||
expertise_indicators:
|
||||
# Detect user expertise level
|
||||
behavioral_indicators:
|
||||
command_proficiency:
|
||||
indicators: ["advanced_flags", "complex_operations", "custom_configurations"]
|
||||
weight: 0.4
|
||||
|
||||
error_recovery_ability:
|
||||
indicators: ["self_correction", "minimal_help_needed", "independent_problem_solving"]
|
||||
weight: 0.3
|
||||
|
||||
workflow_sophistication:
|
||||
indicators: ["efficient_workflows", "automation_usage", "advanced_patterns"]
|
||||
weight: 0.3
|
||||
|
||||
expertise_adaptation:
|
||||
# Adapt interface based on expertise
|
||||
beginner_adaptations:
|
||||
interface: ["detailed_explanations", "step_by_step_guidance", "comprehensive_warnings"]
|
||||
defaults: ["safe_options", "guided_workflows", "educational_mode"]
|
||||
|
||||
intermediate_adaptations:
|
||||
interface: ["balanced_explanations", "contextual_help", "smart_suggestions"]
|
||||
defaults: ["optimized_workflows", "intelligent_automation", "performance_focused"]
|
||||
|
||||
expert_adaptations:
|
||||
interface: ["minimal_explanations", "advanced_options", "efficiency_focused"]
|
||||
defaults: ["maximum_automation", "performance_optimization", "minimal_interruptions"]
|
||||
|
||||
# User Satisfaction Intelligence
|
||||
satisfaction_intelligence:
|
||||
satisfaction_tracking:
|
||||
# Track user satisfaction indicators
|
||||
satisfaction_metrics:
|
||||
task_completion_rate:
|
||||
weight: 0.3
|
||||
target_threshold: 0.85
|
||||
|
||||
error_resolution_speed:
|
||||
weight: 0.25
|
||||
target_threshold: "fast"
|
||||
|
||||
feature_adoption_rate:
|
||||
weight: 0.2
|
||||
target_threshold: 0.6
|
||||
|
||||
user_feedback_sentiment:
|
||||
weight: 0.25
|
||||
target_threshold: "positive"
|
||||
|
||||
satisfaction_optimization:
|
||||
# Optimize for user satisfaction
|
||||
optimization_strategies:
|
||||
low_satisfaction_triggers:
|
||||
- trigger: {completion_rate: "<0.7"}
|
||||
action: "simplify_workflows"
|
||||
priority: "high"
|
||||
|
||||
- trigger: {error_rate: ">15%"}
|
||||
action: "improve_error_prevention"
|
||||
priority: "critical"
|
||||
|
||||
- trigger: {feature_adoption: "<0.3"}
|
||||
action: "improve_feature_discoverability"
|
||||
priority: "medium"
|
||||
|
||||
# Personalization Engine
|
||||
personalization:
|
||||
adaptive_interface:
|
||||
# Personalize interface based on user patterns
|
||||
interface_personalization:
|
||||
layout_preferences:
|
||||
learn_preferred_layouts: true
|
||||
adapt_information_density: true
|
||||
customize_interaction_patterns: true
|
||||
|
||||
content_personalization:
|
||||
learn_content_preferences: true
|
||||
adapt_explanation_depth: true
|
||||
customize_suggestion_types: true
|
||||
|
||||
workflow_optimization:
|
||||
# Optimize workflows for individual users
|
||||
personal_workflow_learning:
|
||||
common_task_patterns: true
|
||||
workflow_efficiency_analysis: true
|
||||
personalized_shortcuts: true
|
||||
|
||||
workflow_recommendations:
|
||||
suggest_workflow_improvements: true
|
||||
recommend_automation_opportunities: true
|
||||
provide_efficiency_insights: true
|
||||
|
||||
# Accessibility Intelligence
|
||||
accessibility:
|
||||
adaptive_accessibility:
|
||||
# Adapt interface for accessibility needs
|
||||
accessibility_detection:
|
||||
detect_accessibility_needs: true
|
||||
learn_accessibility_preferences: true
|
||||
adapt_interface_accordingly: true
|
||||
|
||||
inclusive_design:
|
||||
# Ensure inclusive user experience
|
||||
inclusive_features:
|
||||
multiple_interaction_modes: true
|
||||
flexible_interface_scaling: true
|
||||
comprehensive_keyboard_support: true
|
||||
screen_reader_optimization: true
|
||||
@@ -1,291 +0,0 @@
|
||||
# SuperClaude-Lite Validation Configuration
|
||||
# RULES.md + PRINCIPLES.md enforcement and quality standards
|
||||
|
||||
# Core SuperClaude Rules Validation
|
||||
rules_validation:
|
||||
file_operations:
|
||||
read_before_write:
|
||||
enabled: true
|
||||
severity: "error"
|
||||
message: "RULES violation: No Read operation detected before Write/Edit"
|
||||
check_recent_tools: 3
|
||||
exceptions: ["new_file_creation"]
|
||||
|
||||
absolute_paths_only:
|
||||
enabled: true
|
||||
severity: "error"
|
||||
message: "RULES violation: Relative path used"
|
||||
path_parameters: ["file_path", "path", "directory", "output_path"]
|
||||
allowed_prefixes: ["http://", "https://", "/"]
|
||||
|
||||
validate_before_execution:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "RULES recommendation: High-risk operation should include validation"
|
||||
high_risk_operations: ["delete", "refactor", "deploy", "migrate"]
|
||||
complexity_threshold: 0.7
|
||||
|
||||
security_requirements:
|
||||
input_validation:
|
||||
enabled: true
|
||||
severity: "error"
|
||||
message: "RULES violation: User input handling without validation"
|
||||
check_patterns: ["user_input", "external_data", "api_input"]
|
||||
|
||||
no_hardcoded_secrets:
|
||||
enabled: true
|
||||
severity: "critical"
|
||||
message: "RULES violation: Hardcoded sensitive information detected"
|
||||
patterns: ["password", "api_key", "secret", "token"]
|
||||
|
||||
production_safety:
|
||||
enabled: true
|
||||
severity: "error"
|
||||
message: "RULES violation: Unsafe operation in production context"
|
||||
production_indicators: ["is_production", "prod_env", "production"]
|
||||
|
||||
# SuperClaude Principles Validation
|
||||
principles_validation:
|
||||
evidence_over_assumptions:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "PRINCIPLES: Provide evidence to support assumptions"
|
||||
check_for_assumptions: true
|
||||
require_evidence: true
|
||||
confidence_threshold: 0.7
|
||||
|
||||
code_over_documentation:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "PRINCIPLES: Documentation should follow working code, not precede it"
|
||||
documentation_operations: ["document", "readme", "guide"]
|
||||
require_working_code: true
|
||||
|
||||
efficiency_over_verbosity:
|
||||
enabled: true
|
||||
severity: "suggestion"
|
||||
message: "PRINCIPLES: Consider token efficiency techniques for large outputs"
|
||||
output_size_threshold: 5000
|
||||
verbosity_indicators: ["repetitive_content", "unnecessary_detail"]
|
||||
|
||||
test_driven_development:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "PRINCIPLES: Logic changes should include tests"
|
||||
logic_operations: ["write", "edit", "generate", "implement"]
|
||||
test_file_patterns: ["*test*", "*spec*", "test_*", "*_test.*"]
|
||||
|
||||
single_responsibility:
|
||||
enabled: true
|
||||
severity: "suggestion"
|
||||
message: "PRINCIPLES: Functions/classes should have single responsibility"
|
||||
complexity_indicators: ["multiple_purposes", "large_function", "many_parameters"]
|
||||
|
||||
error_handling_required:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "PRINCIPLES: Error handling not implemented"
|
||||
critical_operations: ["write", "edit", "deploy", "api_calls"]
|
||||
|
||||
# Quality Standards
|
||||
quality_standards:
|
||||
code_quality:
|
||||
minimum_score: 0.7
|
||||
factors:
|
||||
- syntax_correctness
|
||||
- logical_consistency
|
||||
- error_handling_presence
|
||||
- documentation_adequacy
|
||||
- test_coverage
|
||||
|
||||
security_compliance:
|
||||
minimum_score: 0.8
|
||||
checks:
|
||||
- input_validation
|
||||
- output_sanitization
|
||||
- authentication_checks
|
||||
- authorization_verification
|
||||
- secure_communication
|
||||
|
||||
performance_standards:
|
||||
response_time_threshold_ms: 2000
|
||||
resource_efficiency_min: 0.6
|
||||
optimization_indicators:
|
||||
- algorithm_efficiency
|
||||
- memory_usage
|
||||
- processing_speed
|
||||
|
||||
maintainability:
|
||||
minimum_score: 0.6
|
||||
factors:
|
||||
- code_clarity
|
||||
- documentation_quality
|
||||
- modular_design
|
||||
- consistent_style
|
||||
|
||||
# Validation Workflow
|
||||
validation_workflow:
|
||||
pre_validation:
|
||||
enabled: true
|
||||
quick_checks:
|
||||
- syntax_validation
|
||||
- basic_security_scan
|
||||
- rule_compliance_check
|
||||
|
||||
post_validation:
|
||||
enabled: true
|
||||
comprehensive_checks:
|
||||
- quality_assessment
|
||||
- principle_alignment
|
||||
- effectiveness_measurement
|
||||
- learning_opportunity_detection
|
||||
|
||||
continuous_validation:
|
||||
enabled: true
|
||||
real_time_monitoring:
|
||||
- pattern_violation_detection
|
||||
- quality_degradation_alerts
|
||||
- performance_regression_detection
|
||||
|
||||
# Error Classification and Handling
|
||||
error_classification:
|
||||
critical_errors:
|
||||
severity_level: "critical"
|
||||
block_execution: true
|
||||
examples:
|
||||
- security_vulnerabilities
|
||||
- data_corruption_risk
|
||||
- system_instability
|
||||
|
||||
standard_errors:
|
||||
severity_level: "error"
|
||||
block_execution: false
|
||||
require_acknowledgment: true
|
||||
examples:
|
||||
- rule_violations
|
||||
- quality_failures
|
||||
- incomplete_implementation
|
||||
|
||||
warnings:
|
||||
severity_level: "warning"
|
||||
block_execution: false
|
||||
examples:
|
||||
- principle_deviations
|
||||
- optimization_opportunities
|
||||
- best_practice_suggestions
|
||||
|
||||
suggestions:
|
||||
severity_level: "suggestion"
|
||||
informational: true
|
||||
examples:
|
||||
- code_improvements
|
||||
- efficiency_enhancements
|
||||
- learning_recommendations
|
||||
|
||||
# Effectiveness Measurement
|
||||
effectiveness_measurement:
|
||||
success_indicators:
|
||||
task_completion: "weight: 0.4"
|
||||
quality_achievement: "weight: 0.3"
|
||||
user_satisfaction: "weight: 0.2"
|
||||
learning_value: "weight: 0.1"
|
||||
|
||||
performance_metrics:
|
||||
execution_time: "target: <2000ms"
|
||||
resource_efficiency: "target: >0.6"
|
||||
error_rate: "target: <0.1"
|
||||
validation_accuracy: "target: >0.9"
|
||||
|
||||
quality_metrics:
|
||||
code_quality_score: "target: >0.7"
|
||||
security_compliance: "target: >0.8"
|
||||
principle_alignment: "target: >0.7"
|
||||
rule_compliance: "target: >0.9"
|
||||
|
||||
# Learning Integration
|
||||
learning_integration:
|
||||
pattern_detection:
|
||||
success_patterns: true
|
||||
failure_patterns: true
|
||||
optimization_patterns: true
|
||||
user_preference_patterns: true
|
||||
|
||||
effectiveness_feedback:
|
||||
real_time_collection: true
|
||||
user_satisfaction_tracking: true
|
||||
quality_trend_analysis: true
|
||||
adaptation_triggers: true
|
||||
|
||||
continuous_improvement:
|
||||
threshold_adjustment: true
|
||||
rule_refinement: true
|
||||
principle_enhancement: true
|
||||
validation_optimization: true
|
||||
|
||||
# Context-Aware Validation
|
||||
context_awareness:
|
||||
project_type_adaptations:
|
||||
frontend_projects:
|
||||
additional_checks: ["accessibility", "responsive_design", "browser_compatibility"]
|
||||
|
||||
backend_projects:
|
||||
additional_checks: ["api_security", "data_validation", "performance_optimization"]
|
||||
|
||||
full_stack_projects:
|
||||
additional_checks: ["integration_testing", "end_to_end_validation", "deployment_safety"]
|
||||
|
||||
user_expertise_adjustments:
|
||||
beginner:
|
||||
validation_verbosity: "high"
|
||||
educational_suggestions: true
|
||||
step_by_step_guidance: true
|
||||
|
||||
intermediate:
|
||||
validation_verbosity: "medium"
|
||||
best_practice_suggestions: true
|
||||
optimization_recommendations: true
|
||||
|
||||
expert:
|
||||
validation_verbosity: "low"
|
||||
advanced_optimization_suggestions: true
|
||||
architectural_guidance: true
|
||||
|
||||
# Performance Configuration
|
||||
performance_configuration:
|
||||
validation_targets:
|
||||
processing_time_ms: 100
|
||||
memory_usage_mb: 50
|
||||
cpu_utilization_percent: 30
|
||||
|
||||
optimization_strategies:
|
||||
parallel_validation: true
|
||||
cached_results: true
|
||||
incremental_validation: true
|
||||
smart_rule_selection: true
|
||||
|
||||
resource_management:
|
||||
max_validation_time_ms: 500
|
||||
memory_limit_mb: 100
|
||||
cpu_limit_percent: 50
|
||||
fallback_on_resource_limit: true
|
||||
|
||||
# Integration Points
|
||||
integration_points:
|
||||
mcp_servers:
|
||||
serena: "semantic_validation_support"
|
||||
morphllm: "edit_validation_coordination"
|
||||
sequential: "complex_validation_analysis"
|
||||
|
||||
learning_engine:
|
||||
effectiveness_tracking: true
|
||||
pattern_learning: true
|
||||
adaptation_feedback: true
|
||||
|
||||
compression_engine:
|
||||
validation_result_compression: true
|
||||
quality_preservation_verification: true
|
||||
|
||||
other_hooks:
|
||||
pre_tool_use: "validation_preparation"
|
||||
session_start: "validation_configuration"
|
||||
stop: "validation_summary_generation"
|
||||
@@ -1,347 +0,0 @@
|
||||
# Validation Intelligence Configuration
|
||||
# Health scoring, diagnostic patterns, and proactive system validation
|
||||
# Enables intelligent health monitoring and predictive diagnostics
|
||||
|
||||
# Metadata
|
||||
version: "1.0.0"
|
||||
last_updated: "2025-01-06"
|
||||
description: "Validation intelligence and health scoring patterns"
|
||||
|
||||
# Health Scoring Framework
|
||||
health_scoring:
|
||||
component_weights:
|
||||
# Weighted importance of different system components
|
||||
learning_system: 0.25 # 25% - Core intelligence
|
||||
performance_system: 0.20 # 20% - System performance
|
||||
mcp_coordination: 0.20 # 20% - Server coordination
|
||||
hook_system: 0.15 # 15% - Hook execution
|
||||
configuration_system: 0.10 # 10% - Configuration management
|
||||
cache_system: 0.10 # 10% - Caching and storage
|
||||
|
||||
scoring_metrics:
|
||||
learning_system:
|
||||
pattern_diversity:
|
||||
weight: 0.3
|
||||
healthy_range: [0.6, 0.95] # Not too low, not perfect
|
||||
critical_threshold: 0.3
|
||||
measurement: "pattern_signature_entropy"
|
||||
|
||||
effectiveness_consistency:
|
||||
weight: 0.3
|
||||
healthy_range: [0.7, 0.9] # Consistent but not perfect
|
||||
critical_threshold: 0.5
|
||||
measurement: "effectiveness_score_variance"
|
||||
|
||||
adaptation_responsiveness:
|
||||
weight: 0.2
|
||||
healthy_range: [0.6, 1.0]
|
||||
critical_threshold: 0.4
|
||||
measurement: "adaptation_success_rate"
|
||||
|
||||
learning_velocity:
|
||||
weight: 0.2
|
||||
healthy_range: [0.5, 1.0]
|
||||
critical_threshold: 0.3
|
||||
measurement: "patterns_learned_per_session"
|
||||
|
||||
performance_system:
|
||||
response_time_stability:
|
||||
weight: 0.4
|
||||
healthy_range: [0.7, 1.0] # Low variance preferred
|
||||
critical_threshold: 0.4
|
||||
measurement: "response_time_coefficient_variation"
|
||||
|
||||
resource_efficiency:
|
||||
weight: 0.3
|
||||
healthy_range: [0.6, 0.85] # Efficient but not resource-starved
|
||||
critical_threshold: 0.4
|
||||
measurement: "resource_utilization_efficiency"
|
||||
|
||||
error_rate:
|
||||
weight: 0.3
|
||||
healthy_range: [0.95, 1.0] # Low error rate (inverted)
|
||||
critical_threshold: 0.8
|
||||
measurement: "success_rate"
|
||||
|
||||
mcp_coordination:
|
||||
server_selection_accuracy:
|
||||
weight: 0.4
|
||||
healthy_range: [0.8, 1.0]
|
||||
critical_threshold: 0.6
|
||||
measurement: "optimal_server_selection_rate"
|
||||
|
||||
coordination_efficiency:
|
||||
weight: 0.3
|
||||
healthy_range: [0.7, 1.0]
|
||||
critical_threshold: 0.5
|
||||
measurement: "coordination_overhead_ratio"
|
||||
|
||||
server_availability:
|
||||
weight: 0.3
|
||||
healthy_range: [0.9, 1.0]
|
||||
critical_threshold: 0.7
|
||||
measurement: "average_server_availability"
|
||||
|
||||
# Proactive Diagnostic Patterns
|
||||
proactive_diagnostics:
|
||||
early_warning_patterns:
|
||||
# Detect issues before they become critical
|
||||
learning_system_warnings:
|
||||
- name: "pattern_overfitting"
|
||||
pattern:
|
||||
consecutive_perfect_scores: ">15"
|
||||
pattern_diversity: "<0.5"
|
||||
severity: "medium"
|
||||
lead_time: "2-5_days"
|
||||
recommendation: "Increase pattern complexity or add noise"
|
||||
remediation: "automatic_pattern_diversification"
|
||||
|
||||
- name: "learning_stagnation"
|
||||
pattern:
|
||||
new_patterns_per_day: "<0.1"
|
||||
effectiveness_improvement: "<0.01"
|
||||
duration: ">7_days"
|
||||
severity: "low"
|
||||
lead_time: "1-2_weeks"
|
||||
recommendation: "Review learning triggers and thresholds"
|
||||
|
||||
- name: "adaptation_failure"
|
||||
pattern:
|
||||
failed_adaptations: ">30%"
|
||||
confidence_scores: "decreasing"
|
||||
duration: ">3_days"
|
||||
severity: "high"
|
||||
lead_time: "1-3_days"
|
||||
recommendation: "Review adaptation logic and data quality"
|
||||
|
||||
performance_warnings:
|
||||
- name: "performance_degradation_trend"
|
||||
pattern:
|
||||
response_time_trend: "increasing"
|
||||
degradation_rate: ">5%_per_week"
|
||||
duration: ">10_days"
|
||||
severity: "medium"
|
||||
lead_time: "1-2_weeks"
|
||||
recommendation: "Investigate resource leaks or optimize bottlenecks"
|
||||
|
||||
- name: "memory_leak_indication"
|
||||
pattern:
|
||||
memory_usage_trend: "steadily_increasing"
|
||||
memory_cleanup_efficiency: "decreasing"
|
||||
duration: ">5_days"
|
||||
severity: "high"
|
||||
lead_time: "3-7_days"
|
||||
recommendation: "Check for memory leaks and optimize garbage collection"
|
||||
|
||||
- name: "cache_inefficiency"
|
||||
pattern:
|
||||
cache_hit_rate: "decreasing"
|
||||
cache_size: "growing"
|
||||
cache_cleanup_frequency: "increasing"
|
||||
severity: "low"
|
||||
lead_time: "1_week"
|
||||
recommendation: "Optimize cache strategies and cleanup policies"
|
||||
|
||||
coordination_warnings:
|
||||
- name: "server_selection_degradation"
|
||||
pattern:
|
||||
suboptimal_selections: "increasing"
|
||||
selection_confidence: "decreasing"
|
||||
user_satisfaction: "decreasing"
|
||||
severity: "medium"
|
||||
lead_time: "2-5_days"
|
||||
recommendation: "Retrain server selection algorithms"
|
||||
|
||||
- name: "coordination_overhead_increase"
|
||||
pattern:
|
||||
coordination_time: "increasing"
|
||||
coordination_complexity: "increasing"
|
||||
efficiency_metrics: "decreasing"
|
||||
severity: "medium"
|
||||
lead_time: "1_week"
|
||||
recommendation: "Optimize coordination protocols"
|
||||
|
||||
# Predictive Health Analysis
|
||||
predictive_analysis:
|
||||
health_prediction:
|
||||
# Predict future health issues
|
||||
prediction_models:
|
||||
trend_analysis:
|
||||
model_type: "linear_regression"
|
||||
prediction_horizon: 14 # days
|
||||
confidence_threshold: 0.8
|
||||
|
||||
pattern_matching:
|
||||
model_type: "similarity_search"
|
||||
historical_window: 90 # days
|
||||
pattern_similarity_threshold: 0.85
|
||||
|
||||
anomaly_prediction:
|
||||
model_type: "isolation_forest"
|
||||
anomaly_threshold: 0.1
|
||||
prediction_accuracy_target: 0.75
|
||||
|
||||
health_forecasting:
|
||||
# Forecast health scores
|
||||
forecasting_metrics:
|
||||
- metric: "overall_health_score"
|
||||
horizon: [1, 7, 14, 30] # days
|
||||
accuracy_target: 0.8
|
||||
|
||||
- metric: "component_health_scores"
|
||||
horizon: [1, 7, 14] # days
|
||||
accuracy_target: 0.75
|
||||
|
||||
- metric: "critical_issue_probability"
|
||||
horizon: [1, 3, 7] # days
|
||||
accuracy_target: 0.85
|
||||
|
||||
# Diagnostic Intelligence
|
||||
diagnostic_intelligence:
|
||||
intelligent_diagnosis:
|
||||
# Smart diagnostic capabilities
|
||||
symptom_analysis:
|
||||
symptom_correlation: true
|
||||
root_cause_analysis: true
|
||||
multi_component_diagnosis: true
|
||||
|
||||
diagnostic_algorithms:
|
||||
decision_tree:
|
||||
algorithm: "gradient_boosted_trees"
|
||||
feature_importance_threshold: 0.1
|
||||
|
||||
pattern_matching:
|
||||
algorithm: "k_nearest_neighbors"
|
||||
similarity_metric: "cosine"
|
||||
k_value: 5
|
||||
|
||||
statistical_analysis:
|
||||
algorithm: "hypothesis_testing"
|
||||
confidence_level: 0.95
|
||||
|
||||
automated_remediation:
|
||||
# Automated remediation suggestions
|
||||
remediation_patterns:
|
||||
- symptom: "high_error_rate"
|
||||
diagnosis: "configuration_issue"
|
||||
remediation: "reset_to_known_good_config"
|
||||
automation_level: "suggest"
|
||||
|
||||
- symptom: "memory_leak"
|
||||
diagnosis: "cache_overflow"
|
||||
remediation: "aggressive_cache_cleanup"
|
||||
automation_level: "auto_with_approval"
|
||||
|
||||
- symptom: "performance_degradation"
|
||||
diagnosis: "resource_exhaustion"
|
||||
remediation: "resource_optimization_mode"
|
||||
automation_level: "automatic"
|
||||
|
||||
# Validation Rules
|
||||
validation_rules:
|
||||
system_consistency:
|
||||
# Validate system consistency
|
||||
consistency_checks:
|
||||
configuration_coherence:
|
||||
check_type: "cross_reference"
|
||||
validation_frequency: "on_change"
|
||||
error_threshold: 0
|
||||
|
||||
data_integrity:
|
||||
check_type: "checksum_validation"
|
||||
validation_frequency: "hourly"
|
||||
error_threshold: 0
|
||||
|
||||
dependency_resolution:
|
||||
check_type: "graph_validation"
|
||||
validation_frequency: "on_startup"
|
||||
error_threshold: 0
|
||||
|
||||
performance_validation:
|
||||
# Validate performance expectations
|
||||
performance_checks:
|
||||
response_time_validation:
|
||||
expected_range: [100, 2000] # ms
|
||||
validation_window: 20 # operations
|
||||
failure_threshold: 0.2 # 20% failures allowed
|
||||
|
||||
resource_usage_validation:
|
||||
expected_range: [0.1, 0.9] # utilization
|
||||
validation_frequency: "continuous"
|
||||
alert_threshold: 0.85
|
||||
|
||||
throughput_validation:
|
||||
expected_minimum: 0.5 # operations per second
|
||||
validation_window: 60 # seconds
|
||||
degradation_threshold: 0.3 # 30% degradation
|
||||
|
||||
# Health Monitoring Intelligence
|
||||
monitoring_intelligence:
|
||||
adaptive_monitoring:
|
||||
# Adapt monitoring based on system state
|
||||
monitoring_intensity:
|
||||
healthy_state:
|
||||
sampling_rate: 0.1 # 10% sampling
|
||||
check_frequency: 300 # seconds
|
||||
|
||||
warning_state:
|
||||
sampling_rate: 0.5 # 50% sampling
|
||||
check_frequency: 60 # seconds
|
||||
|
||||
critical_state:
|
||||
sampling_rate: 1.0 # 100% sampling
|
||||
check_frequency: 10 # seconds
|
||||
|
||||
intelligent_alerting:
|
||||
# Smart alerting to reduce noise
|
||||
alert_intelligence:
|
||||
alert_correlation: true # Correlate related alerts
|
||||
alert_suppression: true # Suppress duplicate alerts
|
||||
alert_escalation: true # Escalate based on severity
|
||||
|
||||
alert_thresholds:
|
||||
health_score_critical: 0.6
|
||||
health_score_warning: 0.8
|
||||
component_failure: true
|
||||
performance_degradation: 0.3 # 30% degradation
|
||||
|
||||
# Continuous Validation
|
||||
continuous_validation:
|
||||
validation_cycles:
|
||||
# Continuous validation cycles
|
||||
real_time_validation:
|
||||
validation_frequency: "per_operation"
|
||||
validation_scope: "critical_path"
|
||||
performance_impact: "minimal"
|
||||
|
||||
periodic_validation:
|
||||
validation_frequency: "hourly"
|
||||
validation_scope: "comprehensive"
|
||||
performance_impact: "low"
|
||||
|
||||
deep_validation:
|
||||
validation_frequency: "daily"
|
||||
validation_scope: "exhaustive"
|
||||
performance_impact: "acceptable"
|
||||
|
||||
validation_evolution:
|
||||
# Evolve validation based on findings
|
||||
learning_from_failures: true
|
||||
adaptive_validation_rules: true
|
||||
validation_effectiveness_tracking: true
|
||||
|
||||
# Quality Assurance Integration
|
||||
quality_assurance:
|
||||
quality_gates:
|
||||
# Integration with quality gates
|
||||
gate_validation:
|
||||
syntax_validation: "automatic"
|
||||
performance_validation: "threshold_based"
|
||||
integration_validation: "comprehensive"
|
||||
|
||||
continuous_improvement:
|
||||
# Continuous quality improvement
|
||||
quality_metrics_tracking: true
|
||||
validation_accuracy_tracking: true
|
||||
false_positive_reduction: true
|
||||
diagnostic_accuracy_improvement: true
|
||||
@@ -1,400 +0,0 @@
|
||||
# Configuration Guide
|
||||
|
||||
Framework-Hooks uses YAML configuration files to control hook behavior, performance targets, and system features. This guide covers the essential configuration options for customizing the system.
|
||||
|
||||
## Configuration Files Overview
|
||||
|
||||
The `config/` directory contains 12+ YAML files that control different aspects of the hook system:
|
||||
|
||||
```
|
||||
config/
|
||||
├── session.yaml # Session lifecycle settings
|
||||
├── performance.yaml # Performance targets and limits
|
||||
├── compression.yaml # Context compression settings
|
||||
├── modes.yaml # Mode activation thresholds
|
||||
├── mcp_orchestration.yaml # MCP server coordination
|
||||
├── orchestrator.yaml # General orchestration settings
|
||||
├── logging.yaml # Logging configuration
|
||||
├── validation.yaml # System validation rules
|
||||
└── [other specialized configs]
|
||||
```
|
||||
|
||||
## Essential Configuration Files
|
||||
|
||||
### logging.yaml - System Logging
|
||||
|
||||
Controls logging behavior and output:
|
||||
|
||||
```yaml
|
||||
# Core Logging Settings
|
||||
logging:
|
||||
enabled: false # Default: disabled for performance
|
||||
level: "ERROR" # ERROR, WARNING, INFO, DEBUG
|
||||
|
||||
file_settings:
|
||||
log_directory: "cache/logs"
|
||||
retention_days: 30
|
||||
rotation_strategy: "daily"
|
||||
|
||||
hook_logging:
|
||||
log_lifecycle: false # Log hook start/end events
|
||||
log_decisions: false # Log decision points
|
||||
log_errors: false # Log error events
|
||||
log_timing: false # Include timing information
|
||||
|
||||
performance:
|
||||
max_overhead_ms: 1 # Maximum logging overhead
|
||||
async_logging: false # Keep simple for now
|
||||
|
||||
privacy:
|
||||
sanitize_user_content: true
|
||||
exclude_sensitive_data: true
|
||||
anonymize_session_ids: false
|
||||
```
|
||||
|
||||
**Common customizations:**
|
||||
```yaml
|
||||
# Enable basic logging
|
||||
logging:
|
||||
enabled: true
|
||||
level: "INFO"
|
||||
|
||||
# Enable debugging
|
||||
logging:
|
||||
enabled: true
|
||||
level: "DEBUG"
|
||||
hook_logging:
|
||||
log_lifecycle: true
|
||||
log_decisions: true
|
||||
log_timing: true
|
||||
|
||||
development:
|
||||
verbose_errors: true
|
||||
include_stack_traces: true
|
||||
debug_mode: true
|
||||
```
|
||||
|
||||
### performance.yaml - Performance Targets
|
||||
|
||||
Defines execution time targets for each hook:
|
||||
|
||||
```yaml
|
||||
performance_targets:
|
||||
session_start: 50 # ms - Session initialization
|
||||
pre_tool_use: 200 # ms - Tool preparation
|
||||
post_tool_use: 100 # ms - Tool usage recording
|
||||
pre_compact: 150 # ms - Context compression
|
||||
notification: 50 # ms - Notification handling
|
||||
stop: 100 # ms - Session cleanup
|
||||
subagent_stop: 100 # ms - Subagent coordination
|
||||
|
||||
# Pattern loading performance
|
||||
pattern_loading:
|
||||
minimal_patterns: 100 # ms - Basic patterns (3-5KB each)
|
||||
dynamic_patterns: 200 # ms - Feature patterns (8-12KB each)
|
||||
learned_patterns: 300 # ms - User patterns (10-20KB each)
|
||||
|
||||
# Cache operation limits
|
||||
cache_operations:
|
||||
read_timeout: 10 # ms
|
||||
write_timeout: 50 # ms
|
||||
|
||||
# Timeouts (from settings.json)
|
||||
hook_timeouts:
|
||||
default: 10 # seconds
|
||||
extended: 15 # seconds (pre_tool_use, pre_compact, etc.)
|
||||
```
|
||||
|
||||
### session.yaml - Session Management
|
||||
|
||||
Controls session lifecycle behavior:
|
||||
|
||||
```yaml
|
||||
session_lifecycle:
|
||||
initialization:
|
||||
load_minimal_patterns: true
|
||||
enable_project_detection: true
|
||||
activate_learning_engine: true
|
||||
|
||||
context_management:
|
||||
preserve_user_content: true
|
||||
compress_framework_content: false # Keep framework content uncompressed
|
||||
apply_selective_compression: true
|
||||
|
||||
cleanup:
|
||||
save_learning_data: true
|
||||
persist_adaptations: true
|
||||
cleanup_temp_files: true
|
||||
```
|
||||
|
||||
### compression.yaml - Context Compression
|
||||
|
||||
Controls how the compression engine handles content:
|
||||
|
||||
```yaml
|
||||
compression_settings:
|
||||
enabled: true
|
||||
|
||||
# Content classification for selective compression
|
||||
content_types:
|
||||
framework_content:
|
||||
compression_level: 0 # No compression for SuperClaude framework
|
||||
exclusion_patterns:
|
||||
- "/SuperClaude/"
|
||||
- "~/.claude/"
|
||||
- ".claude/"
|
||||
- "framework_*"
|
||||
|
||||
session_data:
|
||||
compression_level: 0.4 # 40% compression for session operational data
|
||||
apply_to:
|
||||
- "session_metadata"
|
||||
- "checkpoint_data"
|
||||
- "cache_content"
|
||||
|
||||
user_content:
|
||||
compression_level: 0 # No compression for user content
|
||||
preserve_patterns:
|
||||
- "project_files"
|
||||
- "user_documentation"
|
||||
- "source_code"
|
||||
- "configuration_*"
|
||||
|
||||
compression_levels:
|
||||
minimal: 0.40 # 40% compression
|
||||
efficient: 0.70 # 70% compression
|
||||
compressed: 0.85 # 85% compression
|
||||
critical: 0.95 # 95% compression
|
||||
|
||||
quality_targets:
|
||||
preservation_minimum: 0.95 # 95% information preservation required
|
||||
processing_time_limit: 100 # ms
|
||||
```
|
||||
|
||||
## Hook-Specific Configuration
|
||||
|
||||
Each hook can be configured individually. The general pattern is:
|
||||
|
||||
### Hook Enable/Disable
|
||||
|
||||
In `logging.yaml`:
|
||||
```yaml
|
||||
hook_configuration:
|
||||
pre_tool_use:
|
||||
enabled: true # Hook enabled/disabled
|
||||
log_tool_selection: true
|
||||
log_input_validation: true
|
||||
|
||||
post_tool_use:
|
||||
enabled: true
|
||||
log_output_processing: true
|
||||
log_integration_success: true
|
||||
|
||||
# Similar blocks for other hooks
|
||||
```
|
||||
|
||||
### MCP Server Coordination
|
||||
|
||||
In `mcp_orchestration.yaml`:
|
||||
```yaml
|
||||
mcp_servers:
|
||||
context7:
|
||||
enabled: true
|
||||
auto_activation_patterns:
|
||||
- "external library imports"
|
||||
- "framework-specific questions"
|
||||
|
||||
sequential:
|
||||
enabled: true
|
||||
auto_activation_patterns:
|
||||
- "complex debugging scenarios"
|
||||
- "system design questions"
|
||||
|
||||
magic:
|
||||
enabled: true
|
||||
auto_activation_patterns:
|
||||
- "UI component requests"
|
||||
- "design system queries"
|
||||
|
||||
# Configuration for other MCP servers
|
||||
```
|
||||
|
||||
## Pattern System Configuration
|
||||
|
||||
The 3-tier pattern system can be customized:
|
||||
|
||||
### Pattern Loading
|
||||
|
||||
```yaml
|
||||
# In session.yaml or dedicated pattern config
|
||||
pattern_system:
|
||||
minimal_patterns:
|
||||
always_load: true
|
||||
size_limit_kb: 5
|
||||
|
||||
dynamic_patterns:
|
||||
load_on_demand: true
|
||||
size_limit_kb: 12
|
||||
|
||||
learned_patterns:
|
||||
adaptation_enabled: true
|
||||
size_limit_kb: 20
|
||||
evolution_threshold: 0.8
|
||||
```
|
||||
|
||||
### Project Detection Patterns
|
||||
|
||||
Add custom patterns in `patterns/minimal/` directories following existing YAML structure:
|
||||
|
||||
```yaml
|
||||
# Example: patterns/minimal/custom-project.yaml
|
||||
pattern_type: "project_detection"
|
||||
name: "custom_project"
|
||||
triggers:
|
||||
- "specific-file-pattern"
|
||||
- "directory-structure"
|
||||
features_to_activate:
|
||||
- "custom_mode"
|
||||
- "specific_mcp_servers"
|
||||
```
|
||||
|
||||
## Advanced Configuration
|
||||
|
||||
### Learning Engine Tuning
|
||||
|
||||
```yaml
|
||||
# In learning configuration
|
||||
learning_engine:
|
||||
adaptation_settings:
|
||||
learning_rate: 0.1
|
||||
adaptation_threshold: 0.75
|
||||
persistence_enabled: true
|
||||
|
||||
data_retention:
|
||||
session_history_days: 90
|
||||
pattern_evolution_days: 30
|
||||
cache_cleanup_interval: 7
|
||||
```
|
||||
|
||||
### Validation Rules
|
||||
|
||||
```yaml
|
||||
# In validation.yaml
|
||||
validation_rules:
|
||||
hook_performance:
|
||||
enforce_timing_targets: true
|
||||
alert_on_timeout: true
|
||||
|
||||
configuration_integrity:
|
||||
yaml_syntax_check: true
|
||||
required_fields_check: true
|
||||
|
||||
system_health:
|
||||
file_permissions_check: true
|
||||
directory_structure_check: true
|
||||
```
|
||||
|
||||
## Environment-Specific Configuration
|
||||
|
||||
### Development Environment
|
||||
|
||||
```yaml
|
||||
# Enable verbose logging and debugging
|
||||
logging:
|
||||
enabled: true
|
||||
level: "DEBUG"
|
||||
|
||||
development:
|
||||
verbose_errors: true
|
||||
include_stack_traces: true
|
||||
debug_mode: true
|
||||
|
||||
performance_targets:
|
||||
# Relaxed targets for development
|
||||
session_start: 100
|
||||
pre_tool_use: 500
|
||||
```
|
||||
|
||||
### Production Environment
|
||||
|
||||
```yaml
|
||||
# Minimal logging, strict performance
|
||||
logging:
|
||||
enabled: false
|
||||
level: "ERROR"
|
||||
|
||||
performance_targets:
|
||||
# Strict targets for production
|
||||
session_start: 30
|
||||
pre_tool_use: 150
|
||||
|
||||
compression_settings:
|
||||
# Aggressive optimization
|
||||
enabled: true
|
||||
default_level: "efficient"
|
||||
```
|
||||
|
||||
## Configuration Validation
|
||||
|
||||
### Manual Validation
|
||||
|
||||
Test configuration changes:
|
||||
|
||||
```bash
|
||||
cd Framework-Hooks/hooks/shared
|
||||
python3 validate_system.py --check-config
|
||||
```
|
||||
|
||||
### YAML Syntax Check
|
||||
|
||||
```bash
|
||||
python3 -c "
|
||||
import yaml
|
||||
import glob
|
||||
for file in glob.glob('config/*.yaml'):
|
||||
try:
|
||||
yaml.safe_load(open(file))
|
||||
print(f'{file}: OK')
|
||||
except Exception as e:
|
||||
print(f'{file}: ERROR - {e}')
|
||||
"
|
||||
```
|
||||
|
||||
## Configuration Best Practices
|
||||
|
||||
### Performance Optimization
|
||||
1. **Keep logging disabled** in production for best performance
|
||||
2. **Set realistic timing targets** based on your hardware
|
||||
3. **Enable selective compression** to balance performance and quality
|
||||
4. **Tune pattern loading** based on project complexity
|
||||
|
||||
### Debugging and Development
|
||||
1. **Enable comprehensive logging** during development
|
||||
2. **Use debug mode** for detailed error information
|
||||
3. **Test configuration changes** with validation tools
|
||||
4. **Monitor hook performance** against targets
|
||||
|
||||
### Customization Guidelines
|
||||
1. **Back up configurations** before making changes
|
||||
2. **Test changes incrementally** rather than bulk modifications
|
||||
3. **Use validation tools** to verify configuration integrity
|
||||
4. **Document custom patterns** for team collaboration
|
||||
|
||||
## Configuration Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
**YAML Syntax Errors**: Use Python YAML validation or online checkers
|
||||
**Performance Degradation**: Review enabled features and logging verbosity
|
||||
**Hook Failures**: Check required configuration fields are present
|
||||
**Pattern Loading Issues**: Verify pattern file sizes and structure
|
||||
|
||||
### Reset to Defaults
|
||||
|
||||
```bash
|
||||
# Reset all configurations (backup first!)
|
||||
git checkout config/*.yaml
|
||||
# Or restore from installation backup
|
||||
```
|
||||
|
||||
The configuration system provides extensive customization while maintaining sensible defaults for immediate usability.
|
||||
@@ -1,514 +0,0 @@
|
||||
# Compression Configuration (`compression.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `compression.yaml` file defines intelligent token optimization strategies for the SuperClaude-Lite framework. This configuration implements the Token Efficiency Mode with adaptive compression levels, selective content preservation, and quality-gated optimization.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
The compression configuration serves as the foundation for:
|
||||
- **Token Efficiency Mode**: Implements intelligent token optimization with 30-50% reduction targets
|
||||
- **Selective Compression**: Protects framework content while optimizing session data
|
||||
- **Quality Preservation**: Maintains ≥95% information fidelity during compression
|
||||
- **Symbol Systems**: Provides efficient communication through standardized symbols
|
||||
- **Abbreviation Systems**: Intelligent abbreviation for technical terminology
|
||||
- **Adaptive Intelligence**: Context-aware compression based on user expertise and task complexity
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### 1. Compression Levels (`compression_levels`)
|
||||
|
||||
The framework implements 5 compression levels with specific targets and use cases:
|
||||
|
||||
#### Minimal Compression (0-40%)
|
||||
```yaml
|
||||
minimal:
|
||||
symbol_systems: false
|
||||
abbreviation_systems: false
|
||||
structural_optimization: false
|
||||
quality_threshold: 0.98
|
||||
use_cases: ["user_content", "low_resource_usage", "high_quality_required"]
|
||||
```
|
||||
|
||||
**Purpose**: Preserves maximum quality for critical content
|
||||
**Usage**: User project code, important documentation, complex technical content
|
||||
**Quality**: 98% preservation guarantee
|
||||
|
||||
#### Efficient Compression (40-70%)
|
||||
```yaml
|
||||
efficient:
|
||||
symbol_systems: true
|
||||
abbreviation_systems: false
|
||||
structural_optimization: true
|
||||
quality_threshold: 0.95
|
||||
use_cases: ["moderate_resource_usage", "balanced_efficiency"]
|
||||
```
|
||||
|
||||
**Purpose**: Balanced optimization for standard operations
|
||||
**Usage**: Session metadata, working artifacts, analysis results
|
||||
**Quality**: 95% preservation with symbol enhancement
|
||||
|
||||
#### Compressed Level (70-85%)
|
||||
```yaml
|
||||
compressed:
|
||||
symbol_systems: true
|
||||
abbreviation_systems: true
|
||||
structural_optimization: true
|
||||
quality_threshold: 0.90
|
||||
use_cases: ["high_resource_usage", "user_requests_brevity"]
|
||||
```
|
||||
|
||||
**Purpose**: Aggressive optimization for resource constraints
|
||||
**Usage**: Large-scale operations, user-requested brevity
|
||||
**Quality**: 90% preservation with full optimization suite
|
||||
|
||||
#### Critical Compression (85-95%)
|
||||
```yaml
|
||||
critical:
|
||||
symbol_systems: true
|
||||
abbreviation_systems: true
|
||||
structural_optimization: true
|
||||
advanced_techniques: true
|
||||
quality_threshold: 0.85
|
||||
use_cases: ["resource_constraints", "emergency_compression"]
|
||||
```
|
||||
|
||||
**Purpose**: Maximum optimization for severe constraints
|
||||
**Usage**: Resource exhaustion scenarios, emergency situations
|
||||
**Quality**: 85% preservation with advanced techniques
|
||||
|
||||
#### Emergency Compression (95%+)
|
||||
```yaml
|
||||
emergency:
|
||||
symbol_systems: true
|
||||
abbreviation_systems: true
|
||||
structural_optimization: true
|
||||
advanced_techniques: true
|
||||
aggressive_optimization: true
|
||||
quality_threshold: 0.80
|
||||
use_cases: ["critical_resource_constraints", "emergency_situations"]
|
||||
```
|
||||
|
||||
**Purpose**: Ultra-compression for critical resource constraints
|
||||
**Usage**: System overload, critical memory constraints
|
||||
**Quality**: 80% preservation with aggressive optimization
|
||||
|
||||
### 2. Selective Compression (`selective_compression`)
|
||||
|
||||
#### Framework Exclusions
|
||||
```yaml
|
||||
framework_exclusions:
|
||||
patterns:
|
||||
- "~/.claude/"
|
||||
- ".claude/"
|
||||
- "SuperClaude/*"
|
||||
- "CLAUDE.md"
|
||||
- "FLAGS.md"
|
||||
- "PRINCIPLES.md"
|
||||
- "ORCHESTRATOR.md"
|
||||
- "MCP_*.md"
|
||||
- "MODE_*.md"
|
||||
- "SESSION_LIFECYCLE.md"
|
||||
compression_level: "preserve" # 0% compression
|
||||
reasoning: "Framework content must be preserved for proper operation"
|
||||
```
|
||||
|
||||
**Critical Protection**: Framework components receive zero compression to ensure operational integrity.
|
||||
|
||||
#### User Content Preservation
|
||||
```yaml
|
||||
user_content_preservation:
|
||||
patterns:
|
||||
- "project_files"
|
||||
- "user_documentation"
|
||||
- "source_code"
|
||||
- "configuration_files"
|
||||
- "custom_content"
|
||||
compression_level: "minimal" # Light compression only
|
||||
reasoning: "User content requires high fidelity preservation"
|
||||
```
|
||||
|
||||
**Quality Guarantee**: User content maintains 98% fidelity through minimal compression only.
|
||||
|
||||
#### Session Data Optimization
|
||||
```yaml
|
||||
session_data_optimization:
|
||||
patterns:
|
||||
- "session_metadata"
|
||||
- "checkpoint_data"
|
||||
- "cache_content"
|
||||
- "working_artifacts"
|
||||
- "analysis_results"
|
||||
compression_level: "efficient" # 40-70% compression
|
||||
reasoning: "Session data can be compressed while maintaining utility"
|
||||
```
|
||||
|
||||
**Balanced Approach**: Session operational data compressed efficiently while preserving utility.
|
||||
|
||||
### 3. Symbol Systems (`symbol_systems`)
|
||||
|
||||
#### Core Logic and Flow Symbols
|
||||
```yaml
|
||||
core_logic_flow:
|
||||
enabled: true
|
||||
mappings:
|
||||
"leads to": "→"
|
||||
"implies": "→"
|
||||
"transforms to": "⇒"
|
||||
"rollback": "←"
|
||||
"bidirectional": "⇄"
|
||||
"and": "&"
|
||||
"separator": "|"
|
||||
"sequence": "»"
|
||||
"therefore": "∴"
|
||||
"because": "∵"
|
||||
"equivalent": "≡"
|
||||
"approximately": "≈"
|
||||
"not equal": "≠"
|
||||
```
|
||||
|
||||
**Purpose**: Express logical relationships and flow with mathematical precision
|
||||
**Token Savings**: 50-70% reduction in logical expression length
|
||||
|
||||
#### Status and Progress Symbols
|
||||
```yaml
|
||||
status_progress:
|
||||
enabled: true
|
||||
mappings:
|
||||
"completed": "✅"
|
||||
"failed": "❌"
|
||||
"warning": "⚠️"
|
||||
"information": "ℹ️"
|
||||
"in progress": "🔄"
|
||||
"waiting": "⏳"
|
||||
"critical": "🚨"
|
||||
"target": "🎯"
|
||||
"metrics": "📊"
|
||||
"insight": "💡"
|
||||
```
|
||||
|
||||
**Purpose**: Visual status communication with immediate recognition
|
||||
**User Experience**: Enhanced readability through universal symbols
|
||||
|
||||
#### Technical Domain Symbols
|
||||
```yaml
|
||||
technical_domains:
|
||||
enabled: true
|
||||
mappings:
|
||||
"performance": "⚡"
|
||||
"analysis": "🔍"
|
||||
"configuration": "🔧"
|
||||
"security": "🛡️"
|
||||
"deployment": "📦"
|
||||
"design": "🎨"
|
||||
"network": "🌐"
|
||||
"mobile": "📱"
|
||||
"architecture": "🏗️"
|
||||
"components": "🧩"
|
||||
```
|
||||
|
||||
**Purpose**: Domain-specific communication with contextual relevance
|
||||
**Persona Integration**: Symbols adapt to active persona domains
|
||||
|
||||
### 4. Abbreviation Systems (`abbreviation_systems`)
|
||||
|
||||
#### System and Architecture
|
||||
```yaml
|
||||
system_architecture:
|
||||
enabled: true
|
||||
mappings:
|
||||
"configuration": "cfg"
|
||||
"implementation": "impl"
|
||||
"architecture": "arch"
|
||||
"performance": "perf"
|
||||
"operations": "ops"
|
||||
"environment": "env"
|
||||
```
|
||||
|
||||
**Technical Focus**: Core system terminology with consistent abbreviations
|
||||
|
||||
#### Development Process
|
||||
```yaml
|
||||
development_process:
|
||||
enabled: true
|
||||
mappings:
|
||||
"requirements": "req"
|
||||
"dependencies": "deps"
|
||||
"validation": "val"
|
||||
"testing": "test"
|
||||
"documentation": "docs"
|
||||
"standards": "std"
|
||||
```
|
||||
|
||||
**Workflow Integration**: Development lifecycle terminology optimization
|
||||
|
||||
#### Quality Analysis
|
||||
```yaml
|
||||
quality_analysis:
|
||||
enabled: true
|
||||
mappings:
|
||||
"quality": "qual"
|
||||
"security": "sec"
|
||||
"error": "err"
|
||||
"recovery": "rec"
|
||||
"severity": "sev"
|
||||
"optimization": "opt"
|
||||
```
|
||||
|
||||
**Quality Focus**: Quality assurance and analysis terminology
|
||||
|
||||
### 5. Structural Optimization (`structural_optimization`)
|
||||
|
||||
#### Whitespace Optimization
|
||||
```yaml
|
||||
whitespace_optimization:
|
||||
enabled: true
|
||||
remove_redundant_spaces: true
|
||||
normalize_line_breaks: true
|
||||
preserve_code_formatting: true
|
||||
```
|
||||
|
||||
**Code Safety**: Preserves code formatting while optimizing prose content
|
||||
|
||||
#### Phrase Simplification
|
||||
```yaml
|
||||
phrase_simplification:
|
||||
enabled: true
|
||||
common_phrase_replacements:
|
||||
"in order to": "to"
|
||||
"it is important to note that": "note:"
|
||||
"please be aware that": "note:"
|
||||
"for the purpose of": "for"
|
||||
"with regard to": "regarding"
|
||||
```
|
||||
|
||||
**Natural Language**: Simplifies verbose phrasing while maintaining meaning
|
||||
|
||||
#### Redundancy Removal
|
||||
```yaml
|
||||
redundancy_removal:
|
||||
enabled: true
|
||||
remove_articles: ["the", "a", "an"] # Only in high compression levels
|
||||
remove_filler_words: ["very", "really", "quite", "rather"]
|
||||
combine_repeated_concepts: true
|
||||
```
|
||||
|
||||
**Intelligent Reduction**: Context-aware redundancy elimination
|
||||
|
||||
### 6. Quality Preservation (`quality_preservation`)
|
||||
|
||||
#### Minimum Thresholds
|
||||
```yaml
|
||||
minimum_thresholds:
|
||||
information_preservation: 0.95
|
||||
semantic_accuracy: 0.95
|
||||
technical_correctness: 0.98
|
||||
user_content_fidelity: 0.99
|
||||
```
|
||||
|
||||
**Quality Gates**: Enforces minimum quality standards across all compression levels
|
||||
|
||||
#### Validation Criteria
|
||||
```yaml
|
||||
validation_criteria:
|
||||
key_concept_retention: true
|
||||
technical_term_preservation: true
|
||||
code_example_accuracy: true
|
||||
reference_link_preservation: true
|
||||
```
|
||||
|
||||
**Content Integrity**: Ensures critical content elements remain intact
|
||||
|
||||
#### Quality Monitoring
|
||||
```yaml
|
||||
quality_monitoring:
|
||||
real_time_validation: true
|
||||
effectiveness_tracking: true
|
||||
user_feedback_integration: true
|
||||
adaptive_threshold_adjustment: true
|
||||
```
|
||||
|
||||
**Continuous Improvement**: Real-time quality assessment and adaptation
|
||||
|
||||
### 7. Adaptive Compression (`adaptive_compression`)
|
||||
|
||||
#### Context Awareness
|
||||
```yaml
|
||||
context_awareness:
|
||||
user_expertise_factor: true
|
||||
project_complexity_factor: true
|
||||
domain_specific_optimization: true
|
||||
```
|
||||
|
||||
**Personalization**: Compression adapts to user expertise and project context
|
||||
|
||||
#### Learning Integration
|
||||
```yaml
|
||||
learning_integration:
|
||||
effectiveness_feedback: true
|
||||
user_preference_learning: true
|
||||
pattern_optimization: true
|
||||
```
|
||||
|
||||
**Machine Learning**: Continuous improvement through usage patterns
|
||||
|
||||
#### Dynamic Adjustment
|
||||
```yaml
|
||||
dynamic_adjustment:
|
||||
resource_pressure_response: true
|
||||
quality_threshold_adaptation: true
|
||||
performance_optimization: true
|
||||
```
|
||||
|
||||
**Real-Time Adaptation**: Adjusts compression based on system state
|
||||
|
||||
## Performance Targets
|
||||
|
||||
### Processing Performance
|
||||
```yaml
|
||||
performance_targets:
|
||||
processing_time_ms: 150
|
||||
compression_ratio_target: 0.50 # 50% compression
|
||||
quality_preservation_target: 0.95
|
||||
token_efficiency_gain: 0.40 # 40% token reduction
|
||||
```
|
||||
|
||||
**Optimization Goals**: Balances speed, compression, and quality
|
||||
|
||||
### Compression Level Performance
|
||||
Each compression level has specific performance characteristics:
|
||||
- **Minimal**: 1.0x processing time, 98% quality
|
||||
- **Efficient**: 1.2x processing time, 95% quality
|
||||
- **Compressed**: 1.5x processing time, 90% quality
|
||||
- **Critical**: 1.8x processing time, 85% quality
|
||||
- **Emergency**: 2.0x processing time, 80% quality
|
||||
|
||||
## Integration Points
|
||||
|
||||
### MCP Server Integration
|
||||
```yaml
|
||||
integration:
|
||||
mcp_servers:
|
||||
morphllm: "coordinate_compression_with_editing"
|
||||
serena: "memory_compression_strategies"
|
||||
|
||||
modes:
|
||||
token_efficiency: "primary_compression_mode"
|
||||
task_management: "session_data_compression"
|
||||
```
|
||||
|
||||
**System Coordination**: Integrates with MCP servers for coordinated optimization
|
||||
|
||||
### Learning Engine Integration
|
||||
```yaml
|
||||
learning_engine:
|
||||
effectiveness_tracking: true
|
||||
pattern_learning: true
|
||||
adaptation_feedback: true
|
||||
```
|
||||
|
||||
**Continuous Learning**: Improves compression effectiveness through usage analysis
|
||||
|
||||
## Cache Configuration
|
||||
|
||||
### Compression Results Caching
|
||||
```yaml
|
||||
caching:
|
||||
compression_results:
|
||||
enabled: true
|
||||
cache_duration_minutes: 30
|
||||
max_cache_size_mb: 50
|
||||
invalidation_strategy: "content_change_detection"
|
||||
```
|
||||
|
||||
**Performance Optimization**: Caches compression results for repeated content
|
||||
|
||||
### Pattern Recognition Caching
|
||||
```yaml
|
||||
pattern_recognition:
|
||||
enabled: true
|
||||
adaptive_pattern_learning: true
|
||||
user_specific_patterns: true
|
||||
```
|
||||
|
||||
**Intelligent Caching**: Learns and caches user-specific compression patterns
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Content Classification
|
||||
**Always classify content before compression**:
|
||||
- Framework content → Zero compression
|
||||
- User project content → Minimal compression
|
||||
- Session data → Efficient compression
|
||||
- Temporary data → Compressed/Critical levels
|
||||
|
||||
### 2. Quality Monitoring
|
||||
**Monitor compression effectiveness**:
|
||||
- Track quality preservation metrics
|
||||
- Monitor user satisfaction with compressed content
|
||||
- Adjust thresholds based on effectiveness feedback
|
||||
|
||||
### 3. Context-Aware Application
|
||||
**Adapt compression to context**:
|
||||
- User expertise level (beginner → minimal, expert → compressed)
|
||||
- Project complexity (simple → efficient, complex → minimal)
|
||||
- Resource pressure (low → minimal, high → critical)
|
||||
|
||||
### 4. Performance Optimization
|
||||
**Balance compression with performance**:
|
||||
- Use caching for repeated content
|
||||
- Monitor processing time vs. quality trade-offs
|
||||
- Adjust compression levels based on system resources
|
||||
|
||||
### 5. Learning Integration
|
||||
**Enable continuous improvement**:
|
||||
- Track compression effectiveness
|
||||
- Learn user preferences for compression levels
|
||||
- Adapt symbol and abbreviation usage based on domain
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
#### Quality Degradation
|
||||
- **Symptom**: Users report information loss or confusion
|
||||
- **Solution**: Reduce compression level, adjust quality thresholds
|
||||
- **Prevention**: Enable real-time quality monitoring
|
||||
|
||||
#### Performance Issues
|
||||
- **Symptom**: Compression takes too long (>150ms target)
|
||||
- **Solution**: Enable caching, reduce compression complexity
|
||||
- **Monitoring**: Track processing time per compression level
|
||||
|
||||
#### Symbol/Abbreviation Confusion
|
||||
- **Symptom**: Users don't understand compressed content
|
||||
- **Solution**: Adjust to user expertise level, provide symbol legend
|
||||
- **Adaptation**: Learn user preference patterns
|
||||
|
||||
#### Cache Issues
|
||||
- **Symptom**: Stale compression results, cache bloat
|
||||
- **Solution**: Adjust cache invalidation strategy, reduce cache size
|
||||
- **Maintenance**: Enable automatic cache cleanup
|
||||
|
||||
### Configuration Validation
|
||||
|
||||
The framework validates compression configuration:
|
||||
- **Range Validation**: Quality thresholds between 0.0-1.0
|
||||
- **Performance Validation**: Processing time targets achievable
|
||||
- **Pattern Validation**: Symbol and abbreviation mappings are valid
|
||||
- **Integration Validation**: MCP server and mode coordination settings
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Token Efficiency Mode**: See `MODE_Token_Efficiency.md` for behavioral patterns
|
||||
- **Pre-Compact Hook**: Review hook implementation for compression execution
|
||||
- **MCP Integration**: Reference Morphllm documentation for editing coordination
|
||||
- **Quality Gates**: See validation documentation for quality preservation
|
||||
|
||||
## Version History
|
||||
|
||||
- **v1.0.0**: Initial compression configuration with 5-level strategy
|
||||
- Selective compression with framework protection
|
||||
- Symbol and abbreviation systems implementation
|
||||
- Adaptive compression with learning integration
|
||||
- Quality preservation with real-time monitoring
|
||||
@@ -1,267 +0,0 @@
|
||||
# Hook Coordination Configuration (`hook_coordination.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `hook_coordination.yaml` file configures intelligent hook execution patterns, dependency resolution, and optimization strategies for the SuperClaude-Lite framework. This configuration enables smart coordination of all Framework-Hooks lifecycle events.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
This configuration provides:
|
||||
- **Execution Patterns**: Parallel, sequential, and conditional execution strategies
|
||||
- **Dependency Resolution**: Smart dependency management between hooks
|
||||
- **Performance Optimization**: Resource management and caching strategies
|
||||
- **Error Handling**: Resilient execution with graceful degradation
|
||||
- **Context Awareness**: Adaptive execution based on operation context
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### 1. Execution Patterns
|
||||
|
||||
#### Parallel Execution
|
||||
```yaml
|
||||
parallel_execution:
|
||||
groups:
|
||||
- name: "independent_analysis"
|
||||
hooks: ["compression_engine", "pattern_detection"]
|
||||
max_parallel: 2
|
||||
timeout: 5000 # ms
|
||||
```
|
||||
|
||||
**Purpose**: Run independent hooks simultaneously for performance
|
||||
**Groups**: Logical groupings of hooks that can execute in parallel
|
||||
**Limits**: Maximum concurrent hooks and timeout protection
|
||||
|
||||
#### Sequential Execution
|
||||
```yaml
|
||||
sequential_execution:
|
||||
chains:
|
||||
- name: "session_lifecycle"
|
||||
sequence: ["session_start", "pre_tool_use", "post_tool_use", "stop"]
|
||||
mandatory: true
|
||||
break_on_error: true
|
||||
```
|
||||
|
||||
**Purpose**: Enforce execution order for dependent operations
|
||||
**Chains**: Named sequences with defined order and error handling
|
||||
**Control**: Mandatory sequences with error breaking behavior
|
||||
|
||||
#### Conditional Execution
|
||||
```yaml
|
||||
conditional_execution:
|
||||
rules:
|
||||
- hook: "compression_engine"
|
||||
conditions:
|
||||
- resource_usage: ">0.75"
|
||||
- conversation_length: ">50"
|
||||
- enable_compression: true
|
||||
priority: "high"
|
||||
```
|
||||
|
||||
**Purpose**: Execute hooks based on runtime conditions
|
||||
**Conditions**: Logical rules for hook activation
|
||||
**Priority**: Execution priority for resource management
|
||||
|
||||
### 2. Dependency Resolution
|
||||
|
||||
#### Hook Dependencies
|
||||
```yaml
|
||||
hook_dependencies:
|
||||
session_start:
|
||||
requires: []
|
||||
provides: ["session_context", "initial_state"]
|
||||
pre_tool_use:
|
||||
requires: ["session_context"]
|
||||
provides: ["tool_context", "pre_analysis"]
|
||||
depends_on: ["session_start"]
|
||||
```
|
||||
|
||||
**Dependencies**: What each hook requires and provides
|
||||
**Resolution**: Automatic dependency chain calculation
|
||||
**Optional**: Soft dependencies that don't block execution
|
||||
|
||||
#### Resolution Strategies
|
||||
```yaml
|
||||
resolution_strategies:
|
||||
missing_dependency:
|
||||
strategy: "graceful_degradation"
|
||||
fallback: "skip_optional"
|
||||
circular_dependency:
|
||||
strategy: "break_weakest_link"
|
||||
priority_order: ["session_start", "pre_tool_use", "post_tool_use", "stop"]
|
||||
```
|
||||
|
||||
**Graceful Degradation**: Continue execution without non-critical dependencies
|
||||
**Circular Resolution**: Break cycles using priority ordering
|
||||
**Timeout Handling**: Continue execution when dependencies timeout
|
||||
|
||||
### 3. Performance Optimization
|
||||
|
||||
#### Execution Paths
|
||||
```yaml
|
||||
fast_path:
|
||||
conditions:
|
||||
- complexity_score: "<0.3"
|
||||
- operation_type: ["simple", "basic"]
|
||||
optimizations:
|
||||
- skip_non_essential_hooks: true
|
||||
- enable_aggressive_caching: true
|
||||
- parallel_where_possible: true
|
||||
```
|
||||
|
||||
**Fast Path**: Optimized execution for simple operations
|
||||
**Comprehensive Path**: Full analysis for complex operations
|
||||
**Resource Budgets**: CPU, memory, and time limits
|
||||
|
||||
#### Caching Strategies
|
||||
```yaml
|
||||
cacheable_hooks:
|
||||
- hook: "pattern_detection"
|
||||
cache_key: ["session_context", "operation_type"]
|
||||
cache_duration: 300 # seconds
|
||||
```
|
||||
|
||||
**Hook Caching**: Cache hook results to avoid recomputation
|
||||
**Cache Keys**: Contextual keys for cache invalidation
|
||||
**TTL Management**: Time-based cache expiration
|
||||
|
||||
### 4. Context Awareness
|
||||
|
||||
#### Operation Context
|
||||
```yaml
|
||||
context_patterns:
|
||||
- context_type: "ui_development"
|
||||
hook_priorities: ["mcp_intelligence", "pattern_detection", "compression_engine"]
|
||||
preferred_execution: "fast_parallel"
|
||||
```
|
||||
|
||||
**Adaptive Execution**: Adjust hook execution based on operation type
|
||||
**Priority Ordering**: Context-specific hook priority
|
||||
**Execution Preference**: Optimal execution strategy per context
|
||||
|
||||
#### User Preferences
|
||||
```yaml
|
||||
preference_patterns:
|
||||
- user_type: "performance_focused"
|
||||
optimizations: ["aggressive_caching", "parallel_execution", "skip_optional"]
|
||||
```
|
||||
|
||||
**User Adaptation**: Adapt to user preferences and patterns
|
||||
**Performance Profiles**: Optimize for speed, quality, or balance
|
||||
**Learning Integration**: Improve based on user behavior patterns
|
||||
|
||||
### 5. Error Handling and Recovery
|
||||
|
||||
#### Recovery Strategies
|
||||
```yaml
|
||||
recovery_strategies:
|
||||
- error_type: "timeout"
|
||||
recovery: "continue_without_hook"
|
||||
log_level: "warning"
|
||||
- error_type: "critical_failure"
|
||||
recovery: "abort_and_cleanup"
|
||||
log_level: "error"
|
||||
```
|
||||
|
||||
**Error Types**: Different failure modes with appropriate responses
|
||||
**Recovery Actions**: Continue, retry, degrade, or abort
|
||||
**Logging**: Appropriate log levels for different error types
|
||||
|
||||
#### Resilience Features
|
||||
```yaml
|
||||
resilience_features:
|
||||
retry_failed_hooks: true
|
||||
max_retries: 2
|
||||
graceful_degradation: true
|
||||
error_isolation: true
|
||||
```
|
||||
|
||||
**Retry Logic**: Automatic retry with backoff for transient failures
|
||||
**Degradation**: Continue with reduced functionality when possible
|
||||
**Isolation**: Prevent error cascade across hook execution
|
||||
|
||||
### 6. Lifecycle Management
|
||||
|
||||
#### State Tracking
|
||||
```yaml
|
||||
state_tracking:
|
||||
- pending
|
||||
- initializing
|
||||
- running
|
||||
- completed
|
||||
- failed
|
||||
- skipped
|
||||
- timeout
|
||||
```
|
||||
|
||||
**Hook States**: Complete lifecycle state management
|
||||
**Monitoring**: Performance and health tracking
|
||||
**Events**: Before/after hook execution handling
|
||||
|
||||
### 7. Dynamic Configuration
|
||||
|
||||
#### Adaptive Execution
|
||||
```yaml
|
||||
adaptation_triggers:
|
||||
- performance_degradation: ">20%"
|
||||
action: "switch_to_fast_path"
|
||||
- error_rate: ">10%"
|
||||
action: "enable_resilience_mode"
|
||||
```
|
||||
|
||||
**Performance Adaptation**: Switch execution strategies based on performance
|
||||
**Error Response**: Enable resilience mode when error rates increase
|
||||
**Resource Management**: Reduce scope when resources are constrained
|
||||
|
||||
## Configuration Guidelines
|
||||
|
||||
### Performance Tuning
|
||||
- **Fast Path**: Enable for simple operations to reduce overhead
|
||||
- **Parallel Groups**: Group independent hooks for concurrent execution
|
||||
- **Caching**: Cache expensive operations like pattern detection
|
||||
- **Resource Budgets**: Set appropriate limits for your environment
|
||||
|
||||
### Reliability Configuration
|
||||
- **Error Recovery**: Configure appropriate recovery strategies
|
||||
- **Dependency Management**: Use optional dependencies for non-critical hooks
|
||||
- **Resilience**: Enable retry and graceful degradation features
|
||||
- **Monitoring**: Track hook performance and health
|
||||
|
||||
### Context Optimization
|
||||
- **Operation Types**: Define context patterns for your common workflows
|
||||
- **User Preferences**: Adapt to user performance vs quality preferences
|
||||
- **Learning**: Enable learning features for continuous improvement
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Hook Integration
|
||||
- All Framework-Hooks use this coordination configuration
|
||||
- Hook execution follows defined patterns and dependencies
|
||||
- Performance targets integrated with hook implementations
|
||||
|
||||
### Resource Management
|
||||
- Coordinates with performance monitoring systems
|
||||
- Integrates with caching and optimization frameworks
|
||||
- Manages resource allocation across hook execution
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Performance Issues
|
||||
- **Slow Execution**: Check if comprehensive path is being used unnecessarily
|
||||
- **Resource Usage**: Monitor CPU and memory budgets
|
||||
- **Caching**: Verify cache hit rates for expensive operations
|
||||
|
||||
### Execution Problems
|
||||
- **Missing Dependencies**: Check dependency resolution strategies
|
||||
- **Hook Failures**: Review error recovery configuration
|
||||
- **Timeout Issues**: Adjust timeout values for your environment
|
||||
|
||||
### Context Issues
|
||||
- **Wrong Path Selection**: Review context pattern matching
|
||||
- **User Preferences**: Check preference pattern configuration
|
||||
- **Adaptation**: Monitor adaptation trigger effectiveness
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Hook Implementation**: Individual hook documentation for specific behavior
|
||||
- **Performance Configuration**: `performance.yaml.md` for performance targets
|
||||
- **Error Handling**: Framework error handling and logging configuration
|
||||
@@ -1,63 +0,0 @@
|
||||
# Intelligence Patterns Configuration (`intelligence_patterns.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `intelligence_patterns.yaml` file defines core learning intelligence patterns for SuperClaude Framework-Hooks. This configuration enables multi-dimensional pattern recognition, adaptive learning, and intelligent behavior adaptation.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
This configuration provides:
|
||||
- **Pattern Recognition**: Multi-dimensional analysis of operation patterns
|
||||
- **Adaptive Learning**: Dynamic learning rate and confidence adjustment
|
||||
- **Behavior Intelligence**: Context-aware decision making and optimization
|
||||
- **Performance Intelligence**: Success pattern recognition and optimization
|
||||
|
||||
## Key Configuration Areas
|
||||
|
||||
### 1. Pattern Recognition
|
||||
- **Multi-Dimensional Analysis**: Context type, complexity, operation type, performance
|
||||
- **Signature Generation**: Unique pattern identification for caching and learning
|
||||
- **Pattern Clustering**: Groups similar patterns for behavioral optimization
|
||||
- **Similarity Thresholds**: Controls pattern matching sensitivity
|
||||
|
||||
### 2. Adaptive Learning
|
||||
- **Dynamic Learning Rates**: Confidence-based learning rate adjustment (0.1-1.0)
|
||||
- **Confidence Scoring**: Multi-factor confidence assessment
|
||||
- **Learning Windows**: Time-based and operation-based learning boundaries
|
||||
- **Adaptation Strategies**: How the system adapts to new patterns
|
||||
|
||||
### 3. Intelligence Behaviors
|
||||
- **Context Intelligence**: Situation-aware decision making
|
||||
- **Performance Intelligence**: Success pattern recognition and replication
|
||||
- **User Intelligence**: User behavior pattern learning and adaptation
|
||||
- **System Intelligence**: System performance pattern optimization
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
The file includes detailed configurations for:
|
||||
- Learning intelligence parameters and thresholds
|
||||
- Pattern recognition algorithms and clustering
|
||||
- Confidence scoring and adaptation strategies
|
||||
- Intelligence behavior definitions and triggers
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Hook Integration
|
||||
- Pattern recognition runs during hook execution
|
||||
- Learning updates occur post-operation
|
||||
- Intelligence behaviors influence hook coordination
|
||||
|
||||
### Performance Integration
|
||||
- Performance patterns inform optimization decisions
|
||||
- Success patterns guide resource allocation
|
||||
- Failure patterns trigger adaptation strategies
|
||||
|
||||
## Usage Guidelines
|
||||
|
||||
This is an advanced configuration file that controls the core learning and intelligence capabilities of the Framework-Hooks system. Most users should not need to modify these settings, as they are tuned for optimal performance across different use cases.
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Hook Coordination**: `hook_coordination.yaml.md` for execution patterns
|
||||
- **Performance**: `performance.yaml.md` for performance optimization
|
||||
- **User Experience**: `user_experience.yaml.md` for user-focused intelligence
|
||||
@@ -1,423 +0,0 @@
|
||||
# Logging Configuration (`logging.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `logging.yaml` file defines the logging configuration for the SuperClaude-Lite framework hooks. This configuration provides comprehensive logging capabilities while maintaining performance and privacy standards for production environments.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
The logging configuration serves as:
|
||||
- **Execution Monitoring**: Tracks hook lifecycle events and execution patterns
|
||||
- **Performance Analysis**: Logs timing information for optimization analysis
|
||||
- **Error Tracking**: Captures and logs error events with appropriate detail
|
||||
- **Privacy Protection**: Sanitizes user content while preserving debugging capability
|
||||
- **Development Support**: Provides configurable verbosity for development and troubleshooting
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### 1. Core Logging Settings (`logging`)
|
||||
|
||||
#### Basic Configuration
|
||||
```yaml
|
||||
logging:
|
||||
enabled: false
|
||||
level: "ERROR" # ERROR, WARNING, INFO, DEBUG
|
||||
```
|
||||
|
||||
**Purpose**: Controls overall logging enablement and verbosity level
|
||||
**Levels**: ERROR (critical only) → WARNING (issues) → INFO (operations) → DEBUG (detailed)
|
||||
**Default**: Disabled by default with ERROR level when enabled to minimize overhead
|
||||
|
||||
#### File Settings
|
||||
```yaml
|
||||
file_settings:
|
||||
log_directory: "cache/logs"
|
||||
retention_days: 30
|
||||
rotation_strategy: "daily"
|
||||
```
|
||||
|
||||
**Log Directory**: Stores logs in cache directory for easy cleanup
|
||||
**Retention Policy**: 30-day retention balances storage with debugging needs
|
||||
**Rotation Strategy**: Daily rotation prevents large log files
|
||||
|
||||
#### Hook Logging Settings
|
||||
```yaml
|
||||
hook_logging:
|
||||
log_lifecycle: false # Log hook start/end events
|
||||
log_decisions: false # Log decision points
|
||||
log_errors: false # Log error events
|
||||
log_timing: false # Include timing information
|
||||
```
|
||||
|
||||
**Lifecycle Logging**: Disabled by default for performance
|
||||
**Decision Logging**: Disabled by default to reduce overhead
|
||||
**Error Logging**: Disabled by default (can be enabled for debugging)
|
||||
**Timing Logging**: Disabled by default to minimize performance impact
|
||||
|
||||
#### Performance Settings
|
||||
```yaml
|
||||
performance:
|
||||
max_overhead_ms: 1 # Maximum acceptable logging overhead
|
||||
async_logging: false # Keep simple for now
|
||||
```
|
||||
|
||||
**Overhead Limit**: 1ms maximum overhead ensures logging doesn't impact performance
|
||||
**Synchronous Logging**: Simple synchronous approach for reliability and consistency
|
||||
|
||||
#### Privacy Settings
|
||||
```yaml
|
||||
privacy:
|
||||
sanitize_user_content: true
|
||||
exclude_sensitive_data: true
|
||||
anonymize_session_ids: false # Keep for correlation
|
||||
```
|
||||
|
||||
**Content Sanitization**: Removes or masks user content from logs
|
||||
**Sensitive Data Protection**: Excludes passwords, tokens, and personal information
|
||||
**Session Correlation**: Preserves session IDs for debugging while protecting user identity
|
||||
|
||||
### 2. Hook-Specific Configuration (`hook_configuration`)
|
||||
|
||||
#### Pre-Tool Use Hook
|
||||
```yaml
|
||||
pre_tool_use:
|
||||
enabled: true
|
||||
log_tool_selection: true
|
||||
log_input_validation: true
|
||||
```
|
||||
|
||||
**Tool Selection Logging**: Records MCP server routing decisions
|
||||
**Input Validation Logging**: Tracks validation results and failures
|
||||
**Purpose**: Debug routing logic and validate input processing
|
||||
|
||||
#### Post-Tool Use Hook
|
||||
```yaml
|
||||
post_tool_use:
|
||||
enabled: true
|
||||
log_output_processing: true
|
||||
log_integration_success: true
|
||||
```
|
||||
|
||||
**Output Processing**: Logs quality validation and rule compliance checks
|
||||
**Integration Success**: Records successful framework integration outcomes
|
||||
**Purpose**: Monitor quality gates and integration effectiveness
|
||||
|
||||
#### Session Start Hook
|
||||
```yaml
|
||||
session_start:
|
||||
enabled: true
|
||||
log_initialization: true
|
||||
log_configuration_loading: true
|
||||
```
|
||||
|
||||
**Initialization Logging**: Tracks project detection and mode activation
|
||||
**Configuration Loading**: Records YAML configuration loading and validation
|
||||
**Purpose**: Debug session startup issues and configuration problems
|
||||
|
||||
#### Pre-Compact Hook
|
||||
```yaml
|
||||
pre_compact:
|
||||
enabled: true
|
||||
log_compression_decisions: true
|
||||
```
|
||||
|
||||
**Compression Decisions**: Records compression level selection and strategy choices
|
||||
**Purpose**: Optimize compression effectiveness and debug quality issues
|
||||
|
||||
#### Notification Hook
|
||||
```yaml
|
||||
notification:
|
||||
enabled: true
|
||||
log_notification_handling: true
|
||||
```
|
||||
|
||||
**Notification Handling**: Tracks notification processing and pattern updates
|
||||
**Purpose**: Debug notification system and monitor pattern update effectiveness
|
||||
|
||||
#### Stop Hook
|
||||
```yaml
|
||||
stop:
|
||||
enabled: true
|
||||
log_cleanup_operations: true
|
||||
```
|
||||
|
||||
**Cleanup Operations**: Records session analytics generation and cleanup processes
|
||||
**Purpose**: Monitor session termination and ensure proper cleanup
|
||||
|
||||
#### Subagent Stop Hook
|
||||
```yaml
|
||||
subagent_stop:
|
||||
enabled: true
|
||||
log_subagent_cleanup: true
|
||||
```
|
||||
|
||||
**Subagent Cleanup**: Tracks task management analytics and coordination cleanup
|
||||
**Purpose**: Debug task management delegation and monitor coordination effectiveness
|
||||
|
||||
### 3. Development Settings (`development`)
|
||||
|
||||
```yaml
|
||||
development:
|
||||
verbose_errors: false
|
||||
include_stack_traces: false # Keep logs clean
|
||||
debug_mode: false
|
||||
```
|
||||
|
||||
**Verbose Errors**: Disabled by default for minimal output
|
||||
**Stack Traces**: Disabled by default to keep logs clean and readable
|
||||
**Debug Mode**: Disabled for production performance, can be enabled for deep debugging
|
||||
|
||||
## Default Values and Meanings
|
||||
|
||||
### Log Levels
|
||||
- **ERROR**: Only critical errors that prevent operation (default for production)
|
||||
- **WARNING**: Issues that don't prevent operation but should be addressed
|
||||
- **INFO**: Normal operational information and key decision points (recommended default)
|
||||
- **DEBUG**: Detailed execution information for deep troubleshooting
|
||||
|
||||
### Retention Policy
|
||||
- **30 Days**: Balances debugging capability with storage requirements
|
||||
- **Daily Rotation**: Prevents large log files, enables efficient log management
|
||||
- **Automatic Cleanup**: Prevents log directory bloat over time
|
||||
|
||||
### Privacy Defaults
|
||||
- **Sanitize User Content**: Always enabled to protect user privacy
|
||||
- **Exclude Sensitive Data**: Always enabled to prevent credential exposure
|
||||
- **Session ID Preservation**: Enabled for debugging correlation while protecting user identity
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### 1. Hook Execution Logging
|
||||
|
||||
Each hook logs key execution events:
|
||||
```
|
||||
[INFO] [SessionStart] Hook execution started - session_id: abc123
|
||||
[INFO] [SessionStart] Project type detected: nodejs
|
||||
[INFO] [SessionStart] Mode activated: task_management
|
||||
[INFO] [SessionStart] Hook execution completed - duration: 125ms
|
||||
```
|
||||
|
||||
### 2. Decision Point Logging
|
||||
|
||||
Critical decisions are logged for analysis:
|
||||
```
|
||||
[INFO] [PreToolUse] MCP server selected: serena - confidence: 0.85
|
||||
[INFO] [PreToolUse] Routing decision: multi_file_operation detected
|
||||
[WARNING] [PreToolUse] Fallback activated: serena unavailable
|
||||
```
|
||||
|
||||
### 3. Performance Logging
|
||||
|
||||
Timing information for optimization:
|
||||
```
|
||||
[INFO] [PostToolUse] Quality validation completed - duration: 45ms
|
||||
[WARNING] [PreCompact] Compression exceeded target - duration: 200ms (target: 150ms)
|
||||
```
|
||||
|
||||
### 4. Error Logging
|
||||
|
||||
Comprehensive error capture:
|
||||
```
|
||||
[ERROR] [Stop] Analytics generation failed - error: connection_timeout
|
||||
[ERROR] [Stop] Fallback: basic session cleanup activated
|
||||
```
|
||||
|
||||
## Performance Implications
|
||||
|
||||
### 1. Logging Overhead
|
||||
|
||||
#### Synchronous Logging Impact
|
||||
- **Per Log Entry**: <1ms overhead (within target)
|
||||
- **File I/O**: Batched writes for efficiency
|
||||
- **String Processing**: Minimal formatting overhead
|
||||
|
||||
#### Performance Monitoring
|
||||
- **Overhead Tracking**: Monitors logging performance impact
|
||||
- **Threshold Alerts**: Warns when overhead exceeds 1ms target
|
||||
- **Auto-Adjustment**: Can reduce logging verbosity if performance degrades
|
||||
|
||||
### 2. Storage Impact
|
||||
|
||||
#### Log File Sizes
|
||||
- **Typical Session**: 50-200KB log data
|
||||
- **Daily Logs**: 1-10MB depending on activity
|
||||
- **Storage Growth**: ~300MB per month with 30-day retention
|
||||
|
||||
#### Disk I/O Impact
|
||||
- **Write Operations**: Minimal impact through batching
|
||||
- **Log Rotation**: Daily rotation minimizes individual file sizes
|
||||
- **Cleanup**: Automatic cleanup prevents storage bloat
|
||||
|
||||
### 3. Memory Impact
|
||||
|
||||
#### Log Buffer Management
|
||||
- **Buffer Size**: 10KB typical buffer size
|
||||
- **Flush Strategy**: Regular flushes prevent memory buildup
|
||||
- **Memory Usage**: <5MB memory overhead for logging system
|
||||
|
||||
## Configuration Best Practices
|
||||
|
||||
### 1. Production Configuration
|
||||
```yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: "INFO"
|
||||
privacy:
|
||||
sanitize_user_content: true
|
||||
exclude_sensitive_data: true
|
||||
performance:
|
||||
max_overhead_ms: 1
|
||||
```
|
||||
|
||||
**Recommendations**:
|
||||
- Use INFO level for production (balances information with performance)
|
||||
- Always enable privacy protection in production
|
||||
- Maintain 1ms overhead limit for performance
|
||||
|
||||
### 2. Development Configuration
|
||||
```yaml
|
||||
logging:
|
||||
level: "DEBUG"
|
||||
development:
|
||||
verbose_errors: true
|
||||
debug_mode: true
|
||||
privacy:
|
||||
sanitize_user_content: false # Only for development
|
||||
```
|
||||
|
||||
**Development Settings**:
|
||||
- DEBUG level for detailed troubleshooting
|
||||
- Verbose errors for comprehensive debugging
|
||||
- Reduced privacy restrictions (development only)
|
||||
|
||||
### 3. Performance-Critical Configuration
|
||||
```yaml
|
||||
logging:
|
||||
level: "ERROR"
|
||||
hook_logging:
|
||||
log_timing: false
|
||||
performance:
|
||||
max_overhead_ms: 0.5
|
||||
```
|
||||
|
||||
**Optimization Settings**:
|
||||
- ERROR level only for minimal overhead
|
||||
- Disable timing logs for performance
|
||||
- Stricter overhead limits
|
||||
|
||||
### 4. Debugging Configuration
|
||||
```yaml
|
||||
logging:
|
||||
level: "DEBUG"
|
||||
hook_logging:
|
||||
log_lifecycle: true
|
||||
log_decisions: true
|
||||
log_timing: true
|
||||
development:
|
||||
verbose_errors: true
|
||||
include_stack_traces: true
|
||||
```
|
||||
|
||||
**Debug Settings**:
|
||||
- Maximum verbosity for troubleshooting
|
||||
- All logging features enabled
|
||||
- Stack traces for deep debugging
|
||||
|
||||
## Log File Structure
|
||||
|
||||
### 1. Log Entry Format
|
||||
```
|
||||
[TIMESTAMP] [LEVEL] [HOOK_NAME] Message - context_key: value
|
||||
```
|
||||
|
||||
**Example**:
|
||||
```
|
||||
[2024-12-15T14:30:22Z] [INFO] [PreToolUse] MCP routing completed - server: serena, confidence: 0.85, duration: 125ms
|
||||
```
|
||||
|
||||
### 2. Log Directory Structure
|
||||
```
|
||||
cache/logs/
|
||||
├── superclaude-hooks-2024-12-15.log
|
||||
├── superclaude-hooks-2024-12-14.log
|
||||
├── superclaude-hooks-2024-12-13.log
|
||||
└── archived/
|
||||
└── older-logs...
|
||||
```
|
||||
|
||||
### 3. Log Rotation Management
|
||||
- **Daily Files**: New log file each day
|
||||
- **Automatic Cleanup**: Removes files older than retention period
|
||||
- **Archive Option**: Can archive old logs instead of deletion
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Logging Issues
|
||||
|
||||
#### No Logs Generated
|
||||
- **Check**: Logging enabled in configuration
|
||||
- **Verify**: Log directory permissions and existence
|
||||
- **Test**: Hook execution and error handling
|
||||
- **Debug**: Basic logging functionality
|
||||
|
||||
#### Performance Impact
|
||||
- **Symptoms**: Slow hook execution, high overhead
|
||||
- **Solutions**: Reduce log level, disable timing logs
|
||||
- **Monitoring**: Track logging overhead metrics
|
||||
- **Optimization**: Adjust performance settings
|
||||
|
||||
#### Log File Issues
|
||||
- **Symptoms**: Missing logs, rotation problems
|
||||
- **Solutions**: Check file permissions, disk space
|
||||
- **Prevention**: Monitor log directory size
|
||||
- **Maintenance**: Regular log cleanup
|
||||
|
||||
#### Privacy Concerns
|
||||
- **Symptoms**: User data in logs, sensitive information exposure
|
||||
- **Solutions**: Enable sanitization, review privacy settings
|
||||
- **Validation**: Audit log content for sensitive data
|
||||
- **Compliance**: Ensure privacy settings meet requirements
|
||||
|
||||
### Log Analysis
|
||||
|
||||
#### Performance Analysis
|
||||
```bash
|
||||
# Analyze hook execution times
|
||||
grep "duration:" superclaude-hooks-*.log | sort -k5 -n
|
||||
|
||||
# Find performance outliers
|
||||
grep "exceeded target" superclaude-hooks-*.log
|
||||
```
|
||||
|
||||
#### Error Analysis
|
||||
```bash
|
||||
# Review error patterns
|
||||
grep "ERROR" superclaude-hooks-*.log
|
||||
|
||||
# Analyze fallback activation frequency
|
||||
grep "Fallback activated" superclaude-hooks-*.log
|
||||
```
|
||||
|
||||
#### Effectiveness Analysis
|
||||
```bash
|
||||
# Monitor MCP server selection patterns
|
||||
grep "MCP server selected" superclaude-hooks-*.log
|
||||
|
||||
# Track mode activation patterns
|
||||
grep "Mode activated" superclaude-hooks-*.log
|
||||
```
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Hook Implementation**: See individual hook documentation for specific logging patterns
|
||||
- **Performance Configuration**: Reference `performance.yaml.md` for performance monitoring integration
|
||||
- **Privacy Guidelines**: Review framework privacy standards for logging compliance
|
||||
- **Development Support**: See development configuration for debugging techniques
|
||||
|
||||
## Version History
|
||||
|
||||
- **v1.0.0**: Initial logging configuration
|
||||
- Simple, performance-focused logging system
|
||||
- Comprehensive privacy protection
|
||||
- Hook-specific logging customization
|
||||
- Development and production configuration support
|
||||
@@ -1,73 +0,0 @@
|
||||
# MCP Orchestration Configuration (`mcp_orchestration.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `mcp_orchestration.yaml` file configures MCP (Model Context Protocol) server coordination, intelligent routing, and optimization strategies for the SuperClaude-Lite framework.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
This configuration provides:
|
||||
- **MCP Server Routing**: Intelligent selection of MCP servers based on context
|
||||
- **Server Coordination**: Multi-server coordination and fallback strategies
|
||||
- **Performance Optimization**: Caching, load balancing, and resource management
|
||||
- **Context Awareness**: Operation-specific server selection and configuration
|
||||
|
||||
## Key Configuration Areas
|
||||
|
||||
### 1. Server Selection Patterns
|
||||
- **Context-Based Routing**: Route requests to appropriate MCP servers based on operation type
|
||||
- **Confidence Thresholds**: Minimum confidence levels for server selection
|
||||
- **Fallback Chains**: Backup server selection when primary servers unavailable
|
||||
- **Performance-Based Selection**: Choose servers based on historical performance
|
||||
|
||||
### 2. Multi-Server Coordination
|
||||
- **Parallel Execution**: Coordinate multiple servers for complex operations
|
||||
- **Result Aggregation**: Combine results from multiple servers intelligently
|
||||
- **Conflict Resolution**: Handle conflicting recommendations from different servers
|
||||
- **Load Distribution**: Balance requests across available servers
|
||||
|
||||
### 3. Performance Optimization
|
||||
- **Response Caching**: Cache server responses to reduce latency
|
||||
- **Connection Pooling**: Manage persistent connections to MCP servers
|
||||
- **Request Batching**: Batch similar requests for efficiency
|
||||
- **Timeout Management**: Handle server timeouts gracefully
|
||||
|
||||
### 4. Context Intelligence
|
||||
- **Operation Type Detection**: Identify operation types for optimal server selection
|
||||
- **Project Context Awareness**: Route based on detected project characteristics
|
||||
- **User Preference Integration**: Consider user preferences in server selection
|
||||
- **Historical Performance**: Learn from past server performance
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
The file typically includes:
|
||||
- Server capability mappings (which servers handle which operations)
|
||||
- Routing rules and decision trees
|
||||
- Performance thresholds and optimization settings
|
||||
- Fallback and error handling strategies
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Hook Integration
|
||||
- **Pre-Tool Use**: Server selection and preparation
|
||||
- **Post-Tool Use**: Performance tracking and result validation
|
||||
- **Session Start**: Server availability checking and initialization
|
||||
|
||||
### Framework Integration
|
||||
- Works with mode detection to optimize server selection
|
||||
- Integrates with performance monitoring for optimization
|
||||
- Coordinates with user experience settings for personalization
|
||||
|
||||
## Usage Guidelines
|
||||
|
||||
This configuration controls how the framework routes operations to different MCP servers. Key considerations:
|
||||
- **Server Availability**: Configure appropriate fallback chains
|
||||
- **Performance Tuning**: Adjust timeout and caching settings for your environment
|
||||
- **Context Mapping**: Ensure operation types map to appropriate servers
|
||||
- **Load Management**: Configure load balancing for high-usage scenarios
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Hook Coordination**: `hook_coordination.yaml.md` for execution patterns
|
||||
- **Performance**: `performance.yaml.md` for performance monitoring
|
||||
- **User Experience**: `user_experience.yaml.md` for user-focused routing
|
||||
@@ -1,169 +0,0 @@
|
||||
# Modes Configuration (`modes.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `modes.yaml` file defines mode detection patterns for the SuperClaude-Lite framework. This configuration controls trigger patterns and activation thresholds for behavioral mode detection.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
The modes configuration provides:
|
||||
- **Pattern-Based Detection**: Regex and keyword patterns for automatic mode activation
|
||||
- **Confidence Thresholds**: Minimum confidence levels required for mode activation
|
||||
- **Auto-Activation Control**: Enable/disable automatic mode detection
|
||||
- **Performance Tuning**: File count and complexity thresholds for task management mode
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### Basic Structure
|
||||
|
||||
```yaml
|
||||
mode_detection:
|
||||
[mode_name]:
|
||||
enabled: true/false
|
||||
trigger_patterns: [list of patterns]
|
||||
confidence_threshold: 0.0-1.0
|
||||
auto_activate: true/false
|
||||
```
|
||||
|
||||
### 1. Brainstorming Mode
|
||||
|
||||
```yaml
|
||||
brainstorming:
|
||||
enabled: true
|
||||
trigger_patterns:
|
||||
- "I want to build"
|
||||
- "thinking about"
|
||||
- "not sure"
|
||||
- "maybe.*could"
|
||||
- "brainstorm"
|
||||
- "explore"
|
||||
- "figure out"
|
||||
- "unclear.*requirements"
|
||||
- "ambiguous.*needs"
|
||||
confidence_threshold: 0.7
|
||||
auto_activate: true
|
||||
```
|
||||
|
||||
**Purpose**: Detects exploration and requirement discovery needs
|
||||
**Patterns**: Matches uncertain language and exploration keywords
|
||||
**Threshold**: 70% confidence required for activation
|
||||
|
||||
### 2. Task Management Mode
|
||||
|
||||
```yaml
|
||||
task_management:
|
||||
enabled: true
|
||||
trigger_patterns:
|
||||
- "multiple.*tasks"
|
||||
- "complex.*system"
|
||||
- "build.*comprehensive"
|
||||
- "coordinate.*work"
|
||||
- "large-scale.*operation"
|
||||
- "manage.*operations"
|
||||
- "comprehensive.*refactoring"
|
||||
- "authentication.*system"
|
||||
confidence_threshold: 0.7
|
||||
auto_activate: true
|
||||
auto_activation_thresholds:
|
||||
file_count: 3
|
||||
complexity_score: 0.4
|
||||
```
|
||||
|
||||
**Purpose**: Detects complex, multi-step operations requiring coordination
|
||||
**Patterns**: Matches system-level and coordination keywords
|
||||
**Thresholds**: 70% confidence, 3+ files, 0.4+ complexity score
|
||||
|
||||
### 3. Token Efficiency Mode
|
||||
|
||||
```yaml
|
||||
token_efficiency:
|
||||
enabled: true
|
||||
trigger_patterns:
|
||||
- "brief"
|
||||
- "concise"
|
||||
- "compressed"
|
||||
- "efficient.*output"
|
||||
- "token.*optimization"
|
||||
- "short.*response"
|
||||
- "running.*low.*context"
|
||||
confidence_threshold: 0.75
|
||||
auto_activate: true
|
||||
```
|
||||
|
||||
**Purpose**: Detects requests for compressed or efficient output
|
||||
**Patterns**: Matches brevity and efficiency requests
|
||||
**Threshold**: 75% confidence required for activation
|
||||
|
||||
### 4. Introspection Mode
|
||||
|
||||
```yaml
|
||||
introspection:
|
||||
enabled: true
|
||||
trigger_patterns:
|
||||
- "analyze.*reasoning"
|
||||
- "examine.*decision"
|
||||
- "reflect.*on"
|
||||
- "meta.*cognitive"
|
||||
- "thinking.*process"
|
||||
- "reasoning.*process"
|
||||
- "decision.*made"
|
||||
confidence_threshold: 0.6
|
||||
auto_activate: true
|
||||
```
|
||||
|
||||
**Purpose**: Detects requests for meta-cognitive analysis
|
||||
**Patterns**: Matches reasoning and analysis language
|
||||
**Threshold**: 60% confidence (lower threshold for broader detection)
|
||||
|
||||
## Configuration Guidelines
|
||||
|
||||
### Pattern Design
|
||||
- Use regex patterns for flexible matching
|
||||
- Include variations of key concepts
|
||||
- Balance specificity with coverage
|
||||
- Test patterns against common user inputs
|
||||
|
||||
### Threshold Tuning
|
||||
- **Higher thresholds** (0.8+): Reduce false positives, increase precision
|
||||
- **Lower thresholds** (0.5-0.6): Increase detection, may include false positives
|
||||
- **Balanced thresholds** (0.7): Good default for most use cases
|
||||
|
||||
### Performance Considerations
|
||||
- Pattern matching adds ~10-50ms per mode evaluation
|
||||
- More complex regex patterns increase processing time
|
||||
- Consider disabling unused modes to improve performance
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Hook Integration
|
||||
- **Session Start**: Mode detection runs during session initialization
|
||||
- **Pre-Tool Use**: Mode coordination affects tool selection
|
||||
- **Post-Tool Use**: Mode effectiveness tracking and validation
|
||||
|
||||
### MCP Server Coordination
|
||||
- Detected modes influence MCP server routing
|
||||
- Mode-specific optimization strategies applied
|
||||
- Performance profiles adapted based on active modes
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Mode Not Activating
|
||||
- **Check pattern matching**: Test patterns against actual user input
|
||||
- **Lower threshold**: Reduce confidence threshold for broader detection
|
||||
- **Add patterns**: Include additional trigger patterns for edge cases
|
||||
|
||||
### Wrong Mode Activating
|
||||
- **Increase threshold**: Raise confidence threshold for more selective activation
|
||||
- **Refine patterns**: Make patterns more specific to reduce false matches
|
||||
- **Pattern conflicts**: Check for overlapping patterns between modes
|
||||
|
||||
### Performance Issues
|
||||
- **Disable unused modes**: Set `enabled: false` for unused modes
|
||||
- **Simplify patterns**: Use simpler regex patterns for better performance
|
||||
- **Monitor timing**: Track mode detection overhead in logs
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Mode Implementation**: See individual mode documentation (MODE_*.md files)
|
||||
- **Hook Integration**: Reference `session_start.py` for mode initialization
|
||||
- **Performance Configuration**: See `performance.yaml.md` for performance monitoring
|
||||
@@ -1,530 +0,0 @@
|
||||
# Orchestrator Configuration (`orchestrator.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `orchestrator.yaml` file defines intelligent routing patterns and coordination strategies for the SuperClaude-Lite framework. This configuration implements the ORCHESTRATOR.md patterns through automated MCP server selection, hybrid intelligence coordination, and performance optimization strategies.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
The orchestrator configuration serves as:
|
||||
- **Intelligent Routing Engine**: Automatically selects optimal MCP servers based on task characteristics
|
||||
- **Hybrid Intelligence Coordinator**: Manages coordination between Morphllm and Serena for optimal editing strategies
|
||||
- **Performance Optimizer**: Implements caching, parallel processing, and resource management strategies
|
||||
- **Fallback Manager**: Provides graceful degradation when preferred servers are unavailable
|
||||
- **Learning Coordinator**: Tracks routing effectiveness and adapts selection strategies
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### 1. MCP Server Routing Patterns (`routing_patterns`)
|
||||
|
||||
#### UI Components Routing
|
||||
```yaml
|
||||
ui_components:
|
||||
triggers: ["component", "button", "form", "modal", "dialog", "card", "input", "design", "frontend", "ui", "interface"]
|
||||
mcp_server: "magic"
|
||||
persona: "frontend-specialist"
|
||||
confidence_threshold: 0.8
|
||||
priority: "high"
|
||||
performance_profile: "standard"
|
||||
capabilities: ["ui_generation", "design_systems", "component_patterns"]
|
||||
```
|
||||
|
||||
**Purpose**: Routes UI-related requests to Magic MCP server with frontend persona activation
|
||||
**Triggers**: Comprehensive UI terminology detection
|
||||
**Performance**: Standard performance profile with high priority routing
|
||||
|
||||
#### Deep Analysis Routing
|
||||
```yaml
|
||||
deep_analysis:
|
||||
triggers: ["analyze", "complex", "system-wide", "architecture", "debug", "troubleshoot", "investigate", "root cause"]
|
||||
mcp_server: "sequential"
|
||||
thinking_mode: "--think-hard"
|
||||
confidence_threshold: 0.75
|
||||
priority: "high"
|
||||
performance_profile: "intensive"
|
||||
capabilities: ["complex_reasoning", "systematic_analysis", "hypothesis_testing"]
|
||||
context_expansion: true
|
||||
```
|
||||
|
||||
**Purpose**: Routes complex analysis requests to Sequential with enhanced thinking modes
|
||||
**Thinking Integration**: Automatically activates `--think-hard` for systematic analysis
|
||||
**Context Expansion**: Enables broader context analysis for complex problems
|
||||
|
||||
#### Library Documentation Routing
|
||||
```yaml
|
||||
library_documentation:
|
||||
triggers: ["library", "framework", "package", "import", "dependency", "documentation", "docs", "api", "reference"]
|
||||
mcp_server: "context7"
|
||||
persona: "architect"
|
||||
confidence_threshold: 0.85
|
||||
priority: "medium"
|
||||
performance_profile: "standard"
|
||||
capabilities: ["documentation_access", "framework_patterns", "best_practices"]
|
||||
```
|
||||
|
||||
**Purpose**: Routes documentation requests to Context7 with architect persona
|
||||
**High Confidence**: 85% threshold ensures precise documentation routing
|
||||
**Best Practices**: Integrates framework patterns and best practices into responses
|
||||
|
||||
#### Testing Automation Routing
|
||||
```yaml
|
||||
testing_automation:
|
||||
triggers: ["test", "testing", "e2e", "end-to-end", "browser", "automation", "validation", "verify"]
|
||||
mcp_server: "playwright"
|
||||
confidence_threshold: 0.8
|
||||
priority: "medium"
|
||||
performance_profile: "intensive"
|
||||
capabilities: ["browser_automation", "testing_frameworks", "performance_testing"]
|
||||
```
|
||||
|
||||
**Purpose**: Routes testing requests to Playwright for browser automation
|
||||
**Manual Preference**: No auto-activation, prefers manual confirmation for testing operations
|
||||
**Intensive Profile**: Uses intensive performance profile for testing workloads
|
||||
|
||||
#### Intelligent Editing Routing
|
||||
```yaml
|
||||
intelligent_editing:
|
||||
triggers: ["edit", "modify", "refactor", "update", "change", "fix", "improve"]
|
||||
mcp_server: "morphllm"
|
||||
confidence_threshold: 0.7
|
||||
priority: "medium"
|
||||
performance_profile: "lightweight"
|
||||
capabilities: ["pattern_application", "fast_apply", "intelligent_editing"]
|
||||
complexity_threshold: 0.6
|
||||
file_count_threshold: 10
|
||||
```
|
||||
|
||||
**Purpose**: Routes editing requests to Morphllm for fast, intelligent modifications
|
||||
**Thresholds**: Complexity ≤0.6 and file count ≤10 for optimal Morphllm performance
|
||||
**Lightweight Profile**: Optimized for speed and efficiency
|
||||
|
||||
#### Semantic Analysis Routing
|
||||
```yaml
|
||||
semantic_analysis:
|
||||
triggers: ["semantic", "symbol", "reference", "find", "search", "navigate", "explore"]
|
||||
mcp_server: "serena"
|
||||
confidence_threshold: 0.8
|
||||
priority: "high"
|
||||
performance_profile: "standard"
|
||||
capabilities: ["semantic_understanding", "project_context", "memory_management"]
|
||||
```
|
||||
|
||||
**Purpose**: Routes semantic analysis to Serena for deep project understanding
|
||||
**High Priority**: Essential for project navigation and context management
|
||||
**Symbol Operations**: Optimal for symbol-level operations and refactoring
|
||||
|
||||
#### Multi-File Operations Routing
|
||||
```yaml
|
||||
multi_file_operations:
|
||||
triggers: ["multiple files", "batch", "bulk", "project-wide", "codebase", "entire"]
|
||||
mcp_server: "serena"
|
||||
confidence_threshold: 0.9
|
||||
priority: "high"
|
||||
performance_profile: "intensive"
|
||||
capabilities: ["multi_file_coordination", "project_analysis"]
|
||||
```
|
||||
|
||||
**Purpose**: Routes large-scale operations to Serena for comprehensive project handling
|
||||
**High Confidence**: 90% threshold ensures accurate detection of multi-file operations
|
||||
**Intensive Profile**: Resources allocated for complex project-wide operations
|
||||
|
||||
### 2. Hybrid Intelligence Selection (`hybrid_intelligence`)
|
||||
|
||||
#### Morphllm vs Serena Decision Matrix
|
||||
```yaml
|
||||
morphllm_vs_serena:
|
||||
decision_factors:
|
||||
- file_count
|
||||
- complexity_score
|
||||
- operation_type
|
||||
- symbol_operations_required
|
||||
- project_size
|
||||
|
||||
morphllm_criteria:
|
||||
file_count_max: 10
|
||||
complexity_max: 0.6
|
||||
preferred_operations: ["edit", "modify", "update", "pattern_application"]
|
||||
optimization_focus: "token_efficiency"
|
||||
|
||||
serena_criteria:
|
||||
file_count_min: 5
|
||||
complexity_min: 0.4
|
||||
preferred_operations: ["analyze", "refactor", "navigate", "symbol_operations"]
|
||||
optimization_focus: "semantic_understanding"
|
||||
|
||||
fallback_strategy:
|
||||
- try_primary_choice
|
||||
- fallback_to_alternative
|
||||
- use_native_tools
|
||||
```
|
||||
|
||||
**Decision Logic**: Multi-factor analysis determines optimal server selection
|
||||
**Clear Boundaries**: Morphllm for simple edits, Serena for complex analysis
|
||||
**Fallback Chain**: Graceful degradation through alternative servers to native tools
|
||||
|
||||
### 3. Auto-Activation Rules (`auto_activation`)
|
||||
|
||||
#### Complexity Thresholds
|
||||
```yaml
|
||||
complexity_thresholds:
|
||||
enable_sequential:
|
||||
complexity_score: 0.6
|
||||
file_count: 5
|
||||
operation_types: ["analyze", "debug", "complex"]
|
||||
|
||||
enable_delegation:
|
||||
file_count: 3
|
||||
directory_count: 2
|
||||
complexity_score: 0.4
|
||||
|
||||
enable_validation:
|
||||
is_production: true
|
||||
risk_level: ["high", "critical"]
|
||||
operation_types: ["deploy", "refactor", "delete"]
|
||||
```
|
||||
|
||||
**Sequential Activation**: Complex operations with 0.6+ complexity or 5+ files
|
||||
**Delegation Triggers**: Multi-file operations exceeding thresholds
|
||||
**Validation Requirements**: Production and high-risk operations
|
||||
|
||||
### 4. Performance Optimization (`performance_optimization`)
|
||||
|
||||
#### Parallel Execution Strategy
|
||||
```yaml
|
||||
parallel_execution:
|
||||
file_threshold: 3
|
||||
estimated_speedup_min: 1.4
|
||||
max_concurrency: 7
|
||||
```
|
||||
|
||||
**Threshold Management**: 3+ files required for parallel processing
|
||||
**Performance Guarantee**: Minimum 1.4x speedup required for activation
|
||||
**Concurrency Limits**: Maximum 7 concurrent operations for resource management
|
||||
|
||||
#### Caching Strategy
|
||||
```yaml
|
||||
caching_strategy:
|
||||
enable_for_operations: ["documentation_lookup", "analysis_results", "pattern_matching"]
|
||||
cache_duration_minutes: 30
|
||||
max_cache_size_mb: 100
|
||||
```
|
||||
|
||||
**Selective Caching**: Focuses on high-benefit operations
|
||||
**Duration Management**: 30-minute cache lifetime balances freshness with performance
|
||||
**Size Limits**: 100MB cache prevents excessive memory usage
|
||||
|
||||
#### Resource Management
|
||||
```yaml
|
||||
resource_management:
|
||||
memory_threshold_percent: 85
|
||||
token_threshold_percent: 75
|
||||
fallback_to_lightweight: true
|
||||
```
|
||||
|
||||
**Memory Protection**: 85% memory threshold triggers resource optimization
|
||||
**Token Management**: 75% token usage threshold activates efficiency mode
|
||||
**Automatic Fallback**: Switches to lightweight alternatives under pressure
|
||||
|
||||
### 5. Quality Gates Integration (`quality_gates`)
|
||||
|
||||
#### Validation Levels
|
||||
```yaml
|
||||
validation_levels:
|
||||
basic: ["syntax_validation"]
|
||||
standard: ["syntax_validation", "type_analysis", "code_quality"]
|
||||
comprehensive: ["syntax_validation", "type_analysis", "code_quality", "security_assessment", "performance_analysis"]
|
||||
production: ["syntax_validation", "type_analysis", "code_quality", "security_assessment", "performance_analysis", "integration_testing", "deployment_validation"]
|
||||
```
|
||||
|
||||
**Progressive Validation**: Escalating validation complexity based on operation risk
|
||||
**Production Standards**: Comprehensive 7-step validation for production operations
|
||||
|
||||
#### Trigger Conditions
|
||||
```yaml
|
||||
trigger_conditions:
|
||||
comprehensive:
|
||||
- is_production: true
|
||||
- complexity_score: ">0.7"
|
||||
- operation_types: ["refactor", "architecture"]
|
||||
|
||||
production:
|
||||
- is_production: true
|
||||
- operation_types: ["deploy", "release"]
|
||||
```
|
||||
|
||||
**Comprehensive Triggers**: Production context or high complexity operations
|
||||
**Production Triggers**: Deploy and release operations receive maximum validation
|
||||
|
||||
### 6. Fallback Strategies (`fallback_strategies`)
|
||||
|
||||
#### MCP Server Unavailable
|
||||
```yaml
|
||||
mcp_server_unavailable:
|
||||
context7: ["web_search", "cached_documentation", "native_analysis"]
|
||||
sequential: ["native_step_by_step", "basic_analysis"]
|
||||
magic: ["manual_component_generation", "template_suggestions"]
|
||||
playwright: ["manual_testing_suggestions", "test_case_generation"]
|
||||
morphllm: ["native_edit_tools", "manual_editing"]
|
||||
serena: ["basic_file_operations", "simple_search"]
|
||||
```
|
||||
|
||||
**Graceful Degradation**: Each server has specific fallback alternatives
|
||||
**Functionality Preservation**: Maintains core functionality even with server failures
|
||||
**User Guidance**: Provides manual alternatives when automation unavailable
|
||||
|
||||
#### Performance Degradation
|
||||
```yaml
|
||||
performance_degradation:
|
||||
high_latency: ["reduce_analysis_depth", "enable_caching", "parallel_processing"]
|
||||
resource_constraints: ["lightweight_alternatives", "compression_mode", "minimal_features"]
|
||||
```
|
||||
|
||||
**Latency Management**: Reduces analysis depth and increases caching
|
||||
**Resource Protection**: Switches to lightweight alternatives and compression
|
||||
|
||||
#### Quality Issues
|
||||
```yaml
|
||||
quality_issues:
|
||||
validation_failures: ["increase_validation_depth", "manual_review", "rollback_capability"]
|
||||
error_rates_high: ["enable_pre_validation", "reduce_complexity", "step_by_step_execution"]
|
||||
```
|
||||
|
||||
**Quality Recovery**: Increases validation and enables manual review
|
||||
**Error Prevention**: Pre-validation and complexity reduction strategies
|
||||
|
||||
### 7. Learning Integration (`learning_integration`)
|
||||
|
||||
#### Effectiveness Tracking
|
||||
```yaml
|
||||
effectiveness_tracking:
|
||||
track_server_performance: true
|
||||
track_routing_decisions: true
|
||||
track_user_satisfaction: true
|
||||
```
|
||||
|
||||
**Performance Monitoring**: Tracks server performance and routing accuracy
|
||||
**User Feedback**: Incorporates user satisfaction into learning algorithms
|
||||
**Decision Analysis**: Analyzes routing decision effectiveness over time
|
||||
|
||||
#### Adaptation Triggers
|
||||
```yaml
|
||||
adaptation_triggers:
|
||||
effectiveness_threshold: 0.6
|
||||
confidence_threshold: 0.7
|
||||
usage_count_min: 3
|
||||
```
|
||||
|
||||
**Effectiveness Gates**: 60% effectiveness threshold triggers adaptation
|
||||
**Confidence Requirements**: 70% confidence required for routing changes
|
||||
**Statistical Significance**: Minimum 3 usage instances for pattern recognition
|
||||
|
||||
#### Optimization Feedback
|
||||
```yaml
|
||||
optimization_feedback:
|
||||
performance_degradation: "adjust_routing_weights"
|
||||
user_preference_detected: "update_server_priorities"
|
||||
error_patterns_found: "enhance_fallback_strategies"
|
||||
```
|
||||
|
||||
**Dynamic Optimization**: Adjusts routing weights based on performance
|
||||
**Personalization**: Updates priorities based on user preferences
|
||||
**Error Learning**: Enhances fallback strategies based on error patterns
|
||||
|
||||
### 8. Mode Integration (`mode_integration`)
|
||||
|
||||
#### Brainstorming Mode
|
||||
```yaml
|
||||
brainstorming:
|
||||
preferred_servers: ["sequential", "context7"]
|
||||
thinking_modes: ["--think", "--think-hard"]
|
||||
```
|
||||
|
||||
**Server Preference**: Sequential for reasoning, Context7 for documentation
|
||||
**Enhanced Thinking**: Activates thinking modes for deeper analysis
|
||||
|
||||
#### Task Management Mode
|
||||
```yaml
|
||||
task_management:
|
||||
coordination_servers: ["serena", "morphllm"]
|
||||
delegation_strategies: ["files", "folders", "auto"]
|
||||
```
|
||||
|
||||
**Coordination Focus**: Serena for analysis, Morphllm for execution
|
||||
**Delegation Options**: Multiple strategies for different operation types
|
||||
|
||||
#### Token Efficiency Mode
|
||||
```yaml
|
||||
token_efficiency:
|
||||
optimization_servers: ["morphllm"]
|
||||
compression_strategies: ["symbol_systems", "abbreviations"]
|
||||
```
|
||||
|
||||
**Efficiency Focus**: Morphllm for token-optimized operations
|
||||
**Compression Integration**: Symbol systems and abbreviation strategies
|
||||
|
||||
## Performance Implications
|
||||
|
||||
### 1. Routing Decision Overhead
|
||||
|
||||
#### Decision Time Analysis
|
||||
- **Pattern Matching**: 10-50ms per routing pattern evaluation
|
||||
- **Confidence Calculation**: 5-20ms per server option
|
||||
- **Total Routing Decision**: 50-200ms for complete routing analysis
|
||||
|
||||
#### Memory Usage
|
||||
- **Pattern Storage**: 20-50KB for all routing patterns
|
||||
- **Decision State**: 10-20KB during routing evaluation
|
||||
- **Cache Storage**: Up to 100MB for cached results
|
||||
|
||||
### 2. MCP Server Coordination
|
||||
|
||||
#### Server Communication
|
||||
- **Activation Time**: 100-500ms per MCP server activation
|
||||
- **Coordination Overhead**: 50-200ms for multi-server operations
|
||||
- **Fallback Detection**: 100-300ms to detect and switch to fallback
|
||||
|
||||
#### Resource Allocation
|
||||
- **Memory Per Server**: 50-200MB depending on server type
|
||||
- **CPU Usage**: 20-60% during intensive server operations
|
||||
- **Network Usage**: Varies by server, cached where possible
|
||||
|
||||
### 3. Learning System Impact
|
||||
|
||||
#### Learning Overhead
|
||||
- **Effectiveness Tracking**: 5-20ms per operation for metrics collection
|
||||
- **Pattern Analysis**: 100-500ms for pattern recognition updates
|
||||
- **Adaptation Application**: 200ms-2s for routing weight adjustments
|
||||
|
||||
#### Storage Requirements
|
||||
- **Learning Data**: 500KB-2MB per session for effectiveness tracking
|
||||
- **Pattern Storage**: 100KB-1MB for persistent patterns
|
||||
- **Cache Data**: Up to 100MB for performance optimization
|
||||
|
||||
## Configuration Best Practices
|
||||
|
||||
### 1. Production Orchestrator Configuration
|
||||
```yaml
|
||||
# Optimize for reliability and performance
|
||||
routing_patterns:
|
||||
ui_components:
|
||||
confidence_threshold: 0.9 # Higher confidence for production
|
||||
auto_activation:
|
||||
enable_validation:
|
||||
is_production: true
|
||||
risk_level: ["medium", "high", "critical"] # More conservative
|
||||
```
|
||||
|
||||
### 2. Development Orchestrator Configuration
|
||||
```yaml
|
||||
# Enable more experimentation and learning
|
||||
learning_integration:
|
||||
adaptation_triggers:
|
||||
effectiveness_threshold: 0.4 # More aggressive learning
|
||||
usage_count_min: 1 # Learn from fewer samples
|
||||
```
|
||||
|
||||
### 3. Performance-Optimized Configuration
|
||||
```yaml
|
||||
# Minimize overhead for performance-critical environments
|
||||
performance_optimization:
|
||||
parallel_execution:
|
||||
file_threshold: 5 # Higher threshold to reduce overhead
|
||||
caching_strategy:
|
||||
cache_duration_minutes: 60 # Longer cache for better performance
|
||||
```
|
||||
|
||||
### 4. Learning-Optimized Configuration
|
||||
```yaml
|
||||
# Maximum learning and adaptation
|
||||
learning_integration:
|
||||
effectiveness_tracking:
|
||||
detailed_analytics: true
|
||||
user_interaction_tracking: true
|
||||
optimization_feedback:
|
||||
continuous_adaptation: true
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Orchestration Issues
|
||||
|
||||
#### Wrong Server Selected
|
||||
- **Symptoms**: Suboptimal server choice for task type
|
||||
- **Analysis**: Review trigger patterns and confidence thresholds
|
||||
- **Solution**: Adjust routing patterns or increase confidence thresholds
|
||||
- **Testing**: Test routing with sample inputs and monitor effectiveness
|
||||
|
||||
#### Server Unavailable Issues
|
||||
- **Symptoms**: Frequent fallback activation, degraded functionality
|
||||
- **Diagnosis**: Check MCP server availability and network connectivity
|
||||
- **Resolution**: Verify server configurations and fallback strategies
|
||||
- **Prevention**: Implement server health monitoring
|
||||
|
||||
#### Performance Degradation
|
||||
- **Symptoms**: Slow routing decisions, high overhead
|
||||
- **Analysis**: Profile routing decision time and resource usage
|
||||
- **Optimization**: Adjust confidence thresholds, enable caching
|
||||
- **Monitoring**: Track routing performance metrics
|
||||
|
||||
#### Fallback Chain Failures
|
||||
- **Symptoms**: Complete functionality loss when primary server fails
|
||||
- **Investigation**: Review fallback strategy completeness
|
||||
- **Enhancement**: Add more fallback options and manual alternatives
|
||||
- **Testing**: Test fallback chains under various failure scenarios
|
||||
|
||||
### Learning System Troubleshooting
|
||||
|
||||
#### No Learning Observed
|
||||
- **Check**: Learning integration enabled and collecting data
|
||||
- **Verify**: Effectiveness metrics being calculated and stored
|
||||
- **Debug**: Review adaptation trigger thresholds
|
||||
- **Fix**: Ensure learning data persistence and pattern recognition
|
||||
|
||||
#### Poor Routing Decisions
|
||||
- **Analysis**: Review routing effectiveness metrics and user feedback
|
||||
- **Adjustment**: Modify confidence thresholds and trigger patterns
|
||||
- **Validation**: Test routing decisions with controlled scenarios
|
||||
- **Monitoring**: Track long-term routing accuracy trends
|
||||
|
||||
#### Resource Usage Issues
|
||||
- **Monitoring**: Track memory and CPU usage during orchestration
|
||||
- **Optimization**: Adjust cache sizes and parallel processing limits
|
||||
- **Tuning**: Optimize resource thresholds and fallback triggers
|
||||
- **Balancing**: Balance learning sophistication with resource constraints
|
||||
|
||||
## Integration with Other Configurations
|
||||
|
||||
### 1. MCP Server Coordination
|
||||
The orchestrator configuration works closely with:
|
||||
- **superclaude-config.json**: MCP server definitions and capabilities
|
||||
- **performance.yaml**: Performance targets and optimization strategies
|
||||
- **modes.yaml**: Mode-specific server preferences and coordination
|
||||
|
||||
### 2. Hook Integration
|
||||
Orchestrator patterns are implemented through:
|
||||
- **Pre-Tool Use Hook**: Server selection and routing decisions
|
||||
- **Post-Tool Use Hook**: Effectiveness tracking and learning
|
||||
- **Session Start Hook**: Initial server availability assessment
|
||||
|
||||
### 3. Quality Gates Coordination
|
||||
Quality validation levels integrate with:
|
||||
- **validation.yaml**: Specific validation rules and standards
|
||||
- **Trigger conditions for comprehensive and production validation
|
||||
- **Performance monitoring for validation effectiveness
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **ORCHESTRATOR.md**: Framework orchestration patterns and principles
|
||||
- **MCP Server Documentation**: Individual server capabilities and integration
|
||||
- **Hook Documentation**: Implementation details for orchestration hooks
|
||||
- **Performance Configuration**: Performance targets and optimization strategies
|
||||
|
||||
## Version History
|
||||
|
||||
- **v1.0.0**: Initial orchestrator configuration
|
||||
- Comprehensive MCP server routing with 6 server types
|
||||
- Hybrid intelligence coordination between Morphllm and Serena
|
||||
- Multi-level quality gates integration with production safeguards
|
||||
- Learning system integration with effectiveness tracking
|
||||
- Performance optimization with caching and parallel processing
|
||||
- Robust fallback strategies for graceful degradation
|
||||
@@ -1,699 +0,0 @@
|
||||
# Performance Configuration (`performance.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `performance.yaml` file defines comprehensive performance targets, thresholds, and optimization strategies for the SuperClaude-Lite framework. This configuration establishes performance standards across all hooks, MCP servers, modes, and system components while providing monitoring and optimization guidance.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
The performance configuration serves as:
|
||||
- **Performance Standards Definition**: Establishes specific targets for all framework components
|
||||
- **Threshold Management**: Defines warning and critical thresholds for proactive optimization
|
||||
- **Optimization Strategy Guide**: Provides systematic approaches to performance improvement
|
||||
- **Monitoring Framework**: Enables comprehensive performance tracking and alerting
|
||||
- **Resource Management**: Balances system resources across competing framework demands
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### 1. Hook Performance Targets (`hook_targets`)
|
||||
|
||||
#### Session Start Hook
|
||||
```yaml
|
||||
session_start:
|
||||
target_ms: 50
|
||||
warning_threshold_ms: 75
|
||||
critical_threshold_ms: 100
|
||||
optimization_priority: "critical"
|
||||
```
|
||||
|
||||
**Purpose**: Fastest initialization for immediate user engagement
|
||||
**Rationale**: Session start is user-facing and sets performance expectations
|
||||
**Optimization Priority**: Critical due to user experience impact
|
||||
|
||||
#### Pre-Tool Use Hook
|
||||
```yaml
|
||||
pre_tool_use:
|
||||
target_ms: 200
|
||||
warning_threshold_ms: 300
|
||||
critical_threshold_ms: 500
|
||||
optimization_priority: "high"
|
||||
```
|
||||
|
||||
**Purpose**: MCP routing and orchestration decisions
|
||||
**Complexity**: Higher target accommodates intelligent routing analysis
|
||||
**Priority**: High due to frequency of execution
|
||||
|
||||
#### Post-Tool Use Hook
|
||||
```yaml
|
||||
post_tool_use:
|
||||
target_ms: 100
|
||||
warning_threshold_ms: 150
|
||||
critical_threshold_ms: 250
|
||||
optimization_priority: "medium"
|
||||
```
|
||||
|
||||
**Purpose**: Quality validation and rule compliance
|
||||
**Balance**: Moderate target balances thoroughness with responsiveness
|
||||
**Priority**: Medium due to quality importance vs. frequency
|
||||
|
||||
#### Pre-Compact Hook
|
||||
```yaml
|
||||
pre_compact:
|
||||
target_ms: 150
|
||||
warning_threshold_ms: 200
|
||||
critical_threshold_ms: 300
|
||||
optimization_priority: "high"
|
||||
```
|
||||
|
||||
**Purpose**: Token efficiency analysis and compression decisions
|
||||
**Complexity**: Moderate target for compression analysis
|
||||
**Priority**: High due to token efficiency impact on overall performance
|
||||
|
||||
#### Notification Hook
|
||||
```yaml
|
||||
notification:
|
||||
target_ms: 100
|
||||
warning_threshold_ms: 150
|
||||
critical_threshold_ms: 200
|
||||
optimization_priority: "medium"
|
||||
```
|
||||
|
||||
**Purpose**: Documentation loading and pattern updates
|
||||
**Efficiency**: Fast target for notification processing
|
||||
**Priority**: Medium due to background nature of operation
|
||||
|
||||
#### Stop Hook
|
||||
```yaml
|
||||
stop:
|
||||
target_ms: 200
|
||||
warning_threshold_ms: 300
|
||||
critical_threshold_ms: 500
|
||||
optimization_priority: "low"
|
||||
```
|
||||
|
||||
**Purpose**: Session analytics and cleanup
|
||||
**Tolerance**: Higher target acceptable for session termination
|
||||
**Priority**: Low due to end-of-session timing flexibility
|
||||
|
||||
#### Subagent Stop Hook
|
||||
```yaml
|
||||
subagent_stop:
|
||||
target_ms: 150
|
||||
warning_threshold_ms: 200
|
||||
critical_threshold_ms: 300
|
||||
optimization_priority: "medium"
|
||||
```
|
||||
|
||||
**Purpose**: Task management analytics and coordination cleanup
|
||||
**Balance**: Moderate target for coordination analysis
|
||||
**Priority**: Medium due to task management efficiency impact
|
||||
|
||||
### 2. System Performance Targets (`system_targets`)
|
||||
|
||||
#### Overall Efficiency Targets
|
||||
```yaml
|
||||
overall_session_efficiency: 0.75
|
||||
mcp_coordination_efficiency: 0.70
|
||||
compression_effectiveness: 0.50
|
||||
learning_adaptation_rate: 0.80
|
||||
user_satisfaction_target: 0.75
|
||||
```
|
||||
|
||||
**Session Efficiency**: 75% overall efficiency across all operations
|
||||
**MCP Coordination**: 70% efficiency in server selection and coordination
|
||||
**Compression**: 50% token reduction through intelligent compression
|
||||
**Learning Rate**: 80% successful adaptation based on feedback
|
||||
**User Satisfaction**: 75% positive user experience target
|
||||
|
||||
#### Resource Utilization Targets
|
||||
```yaml
|
||||
resource_utilization:
|
||||
memory_target_mb: 100
|
||||
memory_warning_mb: 150
|
||||
memory_critical_mb: 200
|
||||
|
||||
cpu_target_percent: 40
|
||||
cpu_warning_percent: 60
|
||||
cpu_critical_percent: 80
|
||||
|
||||
token_efficiency_target: 0.40
|
||||
token_warning_threshold: 0.20
|
||||
token_critical_threshold: 0.10
|
||||
```
|
||||
|
||||
**Memory Management**: Progressive thresholds for memory optimization
|
||||
**CPU Utilization**: Conservative targets to prevent system impact
|
||||
**Token Efficiency**: Aggressive efficiency targets for context optimization
|
||||
|
||||
### 3. MCP Server Performance (`mcp_server_performance`)
|
||||
|
||||
#### Context7 Performance
|
||||
```yaml
|
||||
context7:
|
||||
activation_target_ms: 150
|
||||
response_target_ms: 500
|
||||
cache_hit_ratio_target: 0.70
|
||||
quality_score_target: 0.90
|
||||
```
|
||||
|
||||
**Purpose**: Documentation lookup and framework patterns
|
||||
**Cache Strategy**: 70% cache hit ratio for documentation efficiency
|
||||
**Quality Assurance**: 90% quality score for documentation accuracy
|
||||
|
||||
#### Sequential Performance
|
||||
```yaml
|
||||
sequential:
|
||||
activation_target_ms: 200
|
||||
response_target_ms: 1000
|
||||
analysis_depth_target: 0.80
|
||||
reasoning_quality_target: 0.85
|
||||
```
|
||||
|
||||
**Purpose**: Complex reasoning and systematic analysis
|
||||
**Analysis Depth**: 80% comprehensive analysis coverage
|
||||
**Quality Focus**: 85% reasoning quality for reliable analysis
|
||||
|
||||
#### Magic Performance
|
||||
```yaml
|
||||
magic:
|
||||
activation_target_ms: 120
|
||||
response_target_ms: 800
|
||||
component_quality_target: 0.85
|
||||
generation_speed_target: 0.75
|
||||
```
|
||||
|
||||
**Purpose**: UI component generation and design systems
|
||||
**Component Quality**: 85% quality for generated UI components
|
||||
**Generation Speed**: 75% efficiency in component creation
|
||||
|
||||
#### Playwright Performance
|
||||
```yaml
|
||||
playwright:
|
||||
activation_target_ms: 300
|
||||
response_target_ms: 2000
|
||||
test_reliability_target: 0.90
|
||||
automation_efficiency_target: 0.80
|
||||
```
|
||||
|
||||
**Purpose**: Browser automation and testing
|
||||
**Test Reliability**: 90% reliable test execution
|
||||
**Automation Efficiency**: 80% successful automation operations
|
||||
|
||||
#### Morphllm Performance
|
||||
```yaml
|
||||
morphllm:
|
||||
activation_target_ms: 80
|
||||
response_target_ms: 400
|
||||
edit_accuracy_target: 0.95
|
||||
processing_efficiency_target: 0.85
|
||||
```
|
||||
|
||||
**Purpose**: Intelligent editing with fast apply
|
||||
**Edit Accuracy**: 95% accurate edits for reliable modifications
|
||||
**Processing Efficiency**: 85% efficient processing for speed optimization
|
||||
|
||||
#### Serena Performance
|
||||
```yaml
|
||||
serena:
|
||||
activation_target_ms: 100
|
||||
response_target_ms: 600
|
||||
semantic_accuracy_target: 0.90
|
||||
memory_efficiency_target: 0.80
|
||||
```
|
||||
|
||||
**Purpose**: Semantic analysis and memory management
|
||||
**Semantic Accuracy**: 90% accurate semantic understanding
|
||||
**Memory Efficiency**: 80% efficient memory operations
|
||||
|
||||
### 4. Compression Performance (`compression_performance`)
|
||||
|
||||
#### Core Compression Targets
|
||||
```yaml
|
||||
target_compression_ratio: 0.50
|
||||
quality_preservation_minimum: 0.95
|
||||
processing_speed_target_chars_per_ms: 100
|
||||
```
|
||||
|
||||
**Compression Ratio**: 50% token reduction target across all compression operations
|
||||
**Quality Preservation**: 95% minimum information preservation
|
||||
**Processing Speed**: 100 characters per millisecond processing target
|
||||
|
||||
#### Level-Specific Targets
|
||||
```yaml
|
||||
level_targets:
|
||||
minimal:
|
||||
compression_ratio: 0.15
|
||||
quality_preservation: 0.98
|
||||
processing_time_factor: 1.0
|
||||
|
||||
efficient:
|
||||
compression_ratio: 0.40
|
||||
quality_preservation: 0.95
|
||||
processing_time_factor: 1.2
|
||||
|
||||
compressed:
|
||||
compression_ratio: 0.60
|
||||
quality_preservation: 0.90
|
||||
processing_time_factor: 1.5
|
||||
|
||||
critical:
|
||||
compression_ratio: 0.75
|
||||
quality_preservation: 0.85
|
||||
processing_time_factor: 1.8
|
||||
|
||||
emergency:
|
||||
compression_ratio: 0.85
|
||||
quality_preservation: 0.80
|
||||
processing_time_factor: 2.0
|
||||
```
|
||||
|
||||
**Progressive Compression**: Higher compression with acceptable quality and time trade-offs
|
||||
**Time Factors**: Processing time scales predictably with compression level
|
||||
**Quality Preservation**: Maintains minimum quality standards at all levels
|
||||
|
||||
### 5. Learning Engine Performance (`learning_performance`)
|
||||
|
||||
#### Core Learning Targets
|
||||
```yaml
|
||||
adaptation_response_time_ms: 200
|
||||
pattern_detection_accuracy: 0.80
|
||||
effectiveness_prediction_accuracy: 0.75
|
||||
```
|
||||
|
||||
**Adaptation Speed**: 200ms response time for learning adaptations
|
||||
**Pattern Accuracy**: 80% accurate pattern detection for reliable learning
|
||||
**Prediction Accuracy**: 75% accurate effectiveness predictions
|
||||
|
||||
#### Learning Rate Targets
|
||||
```yaml
|
||||
learning_rates:
|
||||
user_preference_learning: 0.85
|
||||
operation_pattern_learning: 0.80
|
||||
performance_optimization_learning: 0.75
|
||||
error_recovery_learning: 0.90
|
||||
```
|
||||
|
||||
**User Preferences**: 85% successful learning of user patterns
|
||||
**Operation Patterns**: 80% successful operation pattern recognition
|
||||
**Performance Learning**: 75% successful performance optimization
|
||||
**Error Recovery**: 90% successful error pattern learning
|
||||
|
||||
#### Memory Efficiency
|
||||
```yaml
|
||||
memory_efficiency:
|
||||
learning_data_compression_ratio: 0.30
|
||||
memory_cleanup_efficiency: 0.90
|
||||
cache_hit_ratio: 0.70
|
||||
```
|
||||
|
||||
**Data Compression**: 30% compression of learning data for storage efficiency
|
||||
**Cleanup Efficiency**: 90% effective memory cleanup operations
|
||||
**Cache Performance**: 70% cache hit ratio for learning data access
|
||||
|
||||
### 6. Quality Gate Performance (`quality_gate_performance`)
|
||||
|
||||
#### Validation Speed Targets
|
||||
```yaml
|
||||
validation_speed_targets:
|
||||
syntax_validation_ms: 50
|
||||
type_analysis_ms: 100
|
||||
code_quality_ms: 150
|
||||
security_assessment_ms: 200
|
||||
performance_analysis_ms: 250
|
||||
```
|
||||
|
||||
**Progressive Timing**: Validation complexity increases with analysis depth
|
||||
**Fast Basics**: Quick syntax and type validation for immediate feedback
|
||||
**Comprehensive Analysis**: Longer time allowance for security and performance
|
||||
|
||||
#### Accuracy Targets
|
||||
```yaml
|
||||
accuracy_targets:
|
||||
rule_compliance_detection: 0.95
|
||||
principle_alignment_assessment: 0.90
|
||||
quality_scoring_accuracy: 0.85
|
||||
security_vulnerability_detection: 0.98
|
||||
```
|
||||
|
||||
**Rule Compliance**: 95% accurate rule violation detection
|
||||
**Principle Alignment**: 90% accurate principle assessment
|
||||
**Quality Scoring**: 85% accurate quality assessment
|
||||
**Security Detection**: 98% accurate security vulnerability detection
|
||||
|
||||
### 7. Task Management Performance (`task_management_performance`)
|
||||
|
||||
#### Delegation Efficiency Targets
|
||||
```yaml
|
||||
delegation_efficiency_targets:
|
||||
file_based_delegation: 0.65
|
||||
folder_based_delegation: 0.70
|
||||
auto_delegation: 0.75
|
||||
```
|
||||
|
||||
**Progressive Efficiency**: Auto-delegation provides highest efficiency
|
||||
**File-Based**: 65% efficiency for individual file delegation
|
||||
**Folder-Based**: 70% efficiency for directory-level delegation
|
||||
**Auto-Delegation**: 75% efficiency through intelligent strategy selection
|
||||
|
||||
#### Wave Orchestration Targets
|
||||
```yaml
|
||||
wave_orchestration_targets:
|
||||
coordination_overhead_max: 0.20
|
||||
wave_synchronization_efficiency: 0.85
|
||||
parallel_execution_speedup: 1.50
|
||||
```
|
||||
|
||||
**Coordination Overhead**: Maximum 20% overhead for coordination
|
||||
**Synchronization**: 85% efficient wave synchronization
|
||||
**Parallel Speedup**: Minimum 1.5x speedup from parallel execution
|
||||
|
||||
#### Task Completion Targets
|
||||
```yaml
|
||||
task_completion_targets:
|
||||
success_rate: 0.90
|
||||
quality_score: 0.80
|
||||
time_efficiency: 0.75
|
||||
```
|
||||
|
||||
**Success Rate**: 90% successful task completion
|
||||
**Quality Score**: 80% quality standard maintenance
|
||||
**Time Efficiency**: 75% time efficiency compared to baseline
|
||||
|
||||
### 8. Mode-Specific Performance (`mode_performance`)
|
||||
|
||||
#### Brainstorming Mode
|
||||
```yaml
|
||||
brainstorming:
|
||||
dialogue_response_time_ms: 300
|
||||
convergence_efficiency: 0.80
|
||||
brief_generation_quality: 0.85
|
||||
user_satisfaction_target: 0.85
|
||||
```
|
||||
|
||||
**Dialogue Speed**: 300ms response time for interactive dialogue
|
||||
**Convergence**: 80% efficient convergence to requirements
|
||||
**Brief Quality**: 85% quality in generated briefs
|
||||
**User Experience**: 85% user satisfaction target
|
||||
|
||||
#### Task Management Mode
|
||||
```yaml
|
||||
task_management:
|
||||
coordination_overhead_max: 0.15
|
||||
delegation_efficiency: 0.70
|
||||
parallel_execution_benefit: 1.40
|
||||
analytics_generation_time_ms: 500
|
||||
```
|
||||
|
||||
**Coordination Efficiency**: Maximum 15% coordination overhead
|
||||
**Delegation**: 70% delegation efficiency across operations
|
||||
**Parallel Benefit**: Minimum 1.4x benefit from parallel execution
|
||||
**Analytics Speed**: 500ms for analytics generation
|
||||
|
||||
#### Token Efficiency Mode
|
||||
```yaml
|
||||
token_efficiency:
|
||||
compression_processing_time_ms: 150
|
||||
efficiency_gain_target: 0.40
|
||||
quality_preservation_target: 0.95
|
||||
user_acceptance_rate: 0.80
|
||||
```
|
||||
|
||||
**Processing Speed**: 150ms compression processing time
|
||||
**Efficiency Gain**: 40% token efficiency improvement
|
||||
**Quality Preservation**: 95% information preservation
|
||||
**User Acceptance**: 80% user acceptance of compressed content
|
||||
|
||||
#### Introspection Mode
|
||||
```yaml
|
||||
introspection:
|
||||
analysis_depth_target: 0.80
|
||||
insight_quality_target: 0.75
|
||||
transparency_effectiveness: 0.85
|
||||
learning_value_target: 0.70
|
||||
```
|
||||
|
||||
**Analysis Depth**: 80% comprehensive analysis coverage
|
||||
**Insight Quality**: 75% quality of generated insights
|
||||
**Transparency**: 85% effective transparency in analysis
|
||||
**Learning Value**: 70% learning value from introspection
|
||||
|
||||
### 9. Performance Monitoring (`performance_monitoring`)
|
||||
|
||||
#### Real-Time Tracking
|
||||
```yaml
|
||||
real_time_tracking:
|
||||
enabled: true
|
||||
sampling_interval_ms: 100
|
||||
metric_aggregation_window_s: 60
|
||||
alert_threshold_breaches: 3
|
||||
```
|
||||
|
||||
**Monitoring Frequency**: 100ms sampling for responsive monitoring
|
||||
**Aggregation Window**: 60-second windows for trend analysis
|
||||
**Alert Sensitivity**: 3 threshold breaches trigger alerts
|
||||
|
||||
#### Metrics Collection
|
||||
```yaml
|
||||
metrics_collection:
|
||||
execution_times: true
|
||||
resource_utilization: true
|
||||
quality_scores: true
|
||||
user_satisfaction: true
|
||||
error_rates: true
|
||||
```
|
||||
|
||||
**Comprehensive Coverage**: All key performance dimensions tracked
|
||||
**Quality Focus**: Quality scores and user satisfaction prioritized
|
||||
**Error Tracking**: Error rates monitored for reliability
|
||||
|
||||
#### Alerting Configuration
|
||||
```yaml
|
||||
alerting:
|
||||
performance_degradation: true
|
||||
resource_exhaustion: true
|
||||
quality_threshold_breach: true
|
||||
user_satisfaction_drop: true
|
||||
```
|
||||
|
||||
**Proactive Alerting**: Early warning for performance issues
|
||||
**Resource Protection**: Alerts prevent resource exhaustion
|
||||
**Quality Assurance**: Quality threshold breaches trigger immediate attention
|
||||
|
||||
### 10. Performance Thresholds (`performance_thresholds`)
|
||||
|
||||
#### Green Zone (0-70% resource usage)
|
||||
```yaml
|
||||
green_zone:
|
||||
all_optimizations_available: true
|
||||
proactive_caching: true
|
||||
full_feature_set: true
|
||||
normal_verbosity: true
|
||||
```
|
||||
|
||||
**Optimal Operation**: All features and optimizations available
|
||||
**Proactive Measures**: Caching and optimization enabled
|
||||
**Full Functionality**: Complete feature set accessible
|
||||
|
||||
#### Yellow Zone (70-85% resource usage)
|
||||
```yaml
|
||||
yellow_zone:
|
||||
efficiency_mode_activation: true
|
||||
cache_optimization: true
|
||||
reduced_verbosity: true
|
||||
non_critical_feature_deferral: true
|
||||
```
|
||||
|
||||
**Efficiency Focus**: Activates efficiency optimizations
|
||||
**Resource Conservation**: Reduces non-essential features
|
||||
**Performance Priority**: Prioritizes core functionality
|
||||
|
||||
#### Orange Zone (85-95% resource usage)
|
||||
```yaml
|
||||
orange_zone:
|
||||
aggressive_optimization: true
|
||||
compression_activation: true
|
||||
feature_reduction: true
|
||||
essential_operations_only: true
|
||||
```
|
||||
|
||||
**Aggressive Measures**: Activates all optimization strategies
|
||||
**Feature Limitation**: Reduces to essential operations only
|
||||
**Compression**: Activates token efficiency for resource relief
|
||||
|
||||
#### Red Zone (95%+ resource usage)
|
||||
```yaml
|
||||
red_zone:
|
||||
emergency_mode: true
|
||||
maximum_compression: true
|
||||
minimal_features: true
|
||||
critical_operations_only: true
|
||||
```
|
||||
|
||||
**Emergency Response**: Activates emergency resource management
|
||||
**Maximum Optimization**: All optimization strategies active
|
||||
**Critical Only**: Only critical operations permitted
|
||||
|
||||
## Performance Implications
|
||||
|
||||
### 1. Target Achievement Rates
|
||||
|
||||
#### Hook Performance Achievement
|
||||
- **Session Start**: 95% operations under 50ms target
|
||||
- **Pre-Tool Use**: 90% operations under 200ms target
|
||||
- **Post-Tool Use**: 92% operations under 100ms target
|
||||
- **Pre-Compact**: 88% operations under 150ms target
|
||||
|
||||
#### MCP Server Performance Achievement
|
||||
- **Context7**: 85% cache hit ratio, 92% quality score achievement
|
||||
- **Sequential**: 78% analysis depth achievement, 83% reasoning quality
|
||||
- **Magic**: 82% component quality, 73% generation speed target
|
||||
- **Morphllm**: 96% edit accuracy, 87% processing efficiency
|
||||
|
||||
### 2. Resource Usage Patterns
|
||||
|
||||
#### Memory Utilization
|
||||
- **Typical Usage**: 80-120MB across all hooks and servers
|
||||
- **Peak Usage**: 150-200MB during complex operations
|
||||
- **Critical Threshold**: 200MB triggers resource optimization
|
||||
|
||||
#### CPU Utilization
|
||||
- **Average Usage**: 30-50% during active operations
|
||||
- **Peak Usage**: 60-80% during intensive analysis or parallel operations
|
||||
- **Critical Threshold**: 80% triggers efficiency mode activation
|
||||
|
||||
#### Token Efficiency Impact
|
||||
- **Compression Effectiveness**: 45-55% token reduction achieved
|
||||
- **Quality Preservation**: 96% average information preservation
|
||||
- **Processing Overhead**: 120-180ms average compression time
|
||||
|
||||
### 3. Learning System Performance Impact
|
||||
|
||||
#### Learning Overhead
|
||||
- **Metrics Collection**: 2-8ms per operation overhead
|
||||
- **Pattern Analysis**: 50-200ms for pattern updates
|
||||
- **Adaptation Application**: 100-500ms for parameter adjustments
|
||||
|
||||
#### Effectiveness Improvement
|
||||
- **User Preference Learning**: 12% improvement in satisfaction over 30 days
|
||||
- **Operation Optimization**: 18% improvement in efficiency over time
|
||||
- **Error Recovery**: 25% reduction in repeated errors through learning
|
||||
|
||||
## Configuration Best Practices
|
||||
|
||||
### 1. Production Performance Configuration
|
||||
```yaml
|
||||
# Conservative targets for reliability
|
||||
hook_targets:
|
||||
session_start:
|
||||
target_ms: 75 # Slightly relaxed for stability
|
||||
critical_threshold_ms: 150
|
||||
system_targets:
|
||||
user_satisfaction_target: 0.80 # Higher satisfaction requirement
|
||||
```
|
||||
|
||||
### 2. Development Performance Configuration
|
||||
```yaml
|
||||
# Relaxed targets for development flexibility
|
||||
hook_targets:
|
||||
session_start:
|
||||
target_ms: 100 # More relaxed for development
|
||||
warning_threshold_ms: 150
|
||||
performance_monitoring:
|
||||
real_time_tracking:
|
||||
sampling_interval_ms: 500 # Less frequent sampling
|
||||
```
|
||||
|
||||
### 3. High-Performance Configuration
|
||||
```yaml
|
||||
# Aggressive targets for performance-critical environments
|
||||
hook_targets:
|
||||
session_start:
|
||||
target_ms: 25 # Very aggressive target
|
||||
optimization_priority: "critical"
|
||||
performance_thresholds:
|
||||
yellow_zone:
|
||||
threshold: 60 # Earlier efficiency activation
|
||||
```
|
||||
|
||||
### 4. Resource-Constrained Configuration
|
||||
```yaml
|
||||
# Conservative resource usage
|
||||
system_targets:
|
||||
memory_target_mb: 50 # Lower memory target
|
||||
cpu_target_percent: 25 # Lower CPU target
|
||||
performance_thresholds:
|
||||
orange_zone:
|
||||
threshold: 70 # Earlier aggressive optimization
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Performance Issues
|
||||
|
||||
#### Hook Performance Degradation
|
||||
- **Symptoms**: Hooks consistently exceeding target times
|
||||
- **Analysis**: Review execution logs and identify bottlenecks
|
||||
- **Solutions**: Optimize configuration loading, enable caching, reduce feature complexity
|
||||
- **Monitoring**: Track performance trends and identify patterns
|
||||
|
||||
#### MCP Server Latency
|
||||
- **Symptoms**: High response times from MCP servers
|
||||
- **Diagnosis**: Check server availability, network connectivity, resource constraints
|
||||
- **Optimization**: Enable caching, implement server health monitoring
|
||||
- **Fallbacks**: Ensure fallback strategies are effective
|
||||
|
||||
#### Resource Exhaustion
|
||||
- **Symptoms**: High memory or CPU usage, frequent threshold breaches
|
||||
- **Immediate Response**: Activate efficiency mode, reduce feature set
|
||||
- **Long-term Solutions**: Optimize resource usage, implement better cleanup
|
||||
- **Prevention**: Monitor trends and adjust thresholds proactively
|
||||
|
||||
#### Quality vs Performance Trade-offs
|
||||
- **Symptoms**: Quality targets missed due to performance constraints
|
||||
- **Analysis**: Review quality-performance balance in configuration
|
||||
- **Adjustment**: Find optimal balance for specific use case requirements
|
||||
- **Monitoring**: Track both quality and performance metrics continuously
|
||||
|
||||
### Performance Optimization Strategies
|
||||
|
||||
#### Caching Optimization
|
||||
```yaml
|
||||
# Optimize caching for better performance
|
||||
caching_strategy:
|
||||
enable_for_operations: ["all_frequent_operations"]
|
||||
cache_duration_minutes: 60 # Longer cache duration
|
||||
max_cache_size_mb: 200 # Larger cache size
|
||||
```
|
||||
|
||||
#### Resource Management Optimization
|
||||
```yaml
|
||||
# More aggressive resource management
|
||||
performance_thresholds:
|
||||
green_zone: 60 # Smaller green zone for earlier optimization
|
||||
yellow_zone: 75 # Earlier efficiency activation
|
||||
```
|
||||
|
||||
#### Learning System Optimization
|
||||
```yaml
|
||||
# Balance learning with performance
|
||||
learning_performance:
|
||||
adaptation_response_time_ms: 100 # Faster adaptations
|
||||
pattern_detection_accuracy: 0.85 # Higher accuracy requirement
|
||||
```
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Hook Documentation**: See individual hook documentation for performance implementation details
|
||||
- **MCP Server Performance**: Reference MCP server documentation for server-specific optimization
|
||||
- **Mode Performance**: Review mode documentation for mode-specific performance characteristics
|
||||
- **Monitoring Integration**: See logging configuration for performance monitoring implementation
|
||||
|
||||
## Version History
|
||||
|
||||
- **v1.0.0**: Initial performance configuration
|
||||
- Comprehensive performance targets across all framework components
|
||||
- Progressive threshold management with zone-based optimization
|
||||
- MCP server performance standards with quality targets
|
||||
- Mode-specific performance profiles and optimization strategies
|
||||
- Real-time monitoring with proactive alerting
|
||||
- Learning system performance integration with effectiveness tracking
|
||||
@@ -1,75 +0,0 @@
|
||||
# Performance Intelligence Configuration (`performance_intelligence.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `performance_intelligence.yaml` file configures intelligent performance monitoring, optimization patterns, and adaptive performance management for the SuperClaude-Lite framework.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
This configuration provides:
|
||||
- **Performance Pattern Recognition**: Learn from performance trends and patterns
|
||||
- **Adaptive Optimization**: Automatically adjust settings based on performance data
|
||||
- **Resource Intelligence**: Smart resource allocation and management
|
||||
- **Predictive Performance**: Anticipate performance issues before they occur
|
||||
|
||||
## Key Configuration Areas
|
||||
|
||||
### 1. Performance Pattern Learning
|
||||
- **Metric Tracking**: Track execution times, resource usage, and success rates
|
||||
- **Pattern Recognition**: Identify performance patterns across operations
|
||||
- **Trend Analysis**: Detect performance degradation or improvement trends
|
||||
- **Correlation Analysis**: Understand relationships between different performance factors
|
||||
|
||||
### 2. Adaptive Optimization
|
||||
- **Dynamic Thresholds**: Adjust performance targets based on system capabilities
|
||||
- **Auto-Optimization**: Automatically enable optimizations when performance degrades
|
||||
- **Resource Scaling**: Scale resource allocation based on demand patterns
|
||||
- **Configuration Adaptation**: Modify settings to maintain performance targets
|
||||
|
||||
### 3. Predictive Intelligence
|
||||
- **Performance Forecasting**: Predict future performance based on current trends
|
||||
- **Bottleneck Prediction**: Identify potential bottlenecks before they impact users
|
||||
- **Capacity Planning**: Recommend resource adjustments for optimal performance
|
||||
- **Proactive Optimization**: Apply optimizations before performance issues occur
|
||||
|
||||
### 4. Intelligent Monitoring
|
||||
- **Context-Aware Monitoring**: Monitor different metrics based on operation context
|
||||
- **Anomaly Detection**: Identify unusual performance patterns
|
||||
- **Health Scoring**: Generate overall system health scores
|
||||
- **Performance Alerting**: Intelligent alerting based on pattern analysis
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
The file includes:
|
||||
- Performance learning algorithms and parameters
|
||||
- Adaptive optimization triggers and thresholds
|
||||
- Predictive modeling configuration
|
||||
- Monitoring and alerting rules
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Framework Integration
|
||||
- Works with all hooks to collect performance data
|
||||
- Integrates with hook coordination for optimization
|
||||
- Provides input to user experience optimization
|
||||
- Coordinates with resource management systems
|
||||
|
||||
### Learning Integration
|
||||
- Feeds performance patterns to intelligence systems
|
||||
- Learns from user behavior and performance preferences
|
||||
- Adapts to project-specific performance characteristics
|
||||
- Improves optimization strategies over time
|
||||
|
||||
## Usage Guidelines
|
||||
|
||||
This configuration controls the intelligent performance monitoring and optimization capabilities:
|
||||
- **Monitoring Depth**: Balance monitoring detail with performance overhead
|
||||
- **Learning Speed**: Configure how quickly the system adapts to performance changes
|
||||
- **Optimization Aggressiveness**: Control how aggressively optimizations are applied
|
||||
- **Prediction Accuracy**: Tune predictive models for your use patterns
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Performance Configuration**: `performance.yaml.md` for basic performance settings
|
||||
- **Intelligence Patterns**: `intelligence_patterns.yaml.md` for core learning patterns
|
||||
- **Hook Coordination**: `hook_coordination.yaml.md` for performance-aware execution
|
||||
@@ -1,716 +0,0 @@
|
||||
# Session Configuration (`session.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `session.yaml` file defines session lifecycle management and analytics configuration for the SuperClaude-Lite framework. This configuration controls session initialization, termination, project detection, intelligence activation, and comprehensive session analytics across the framework.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
The session configuration serves as:
|
||||
- **Session Lifecycle Manager**: Controls initialization and termination patterns for optimal user experience
|
||||
- **Project Intelligence Engine**: Automatically detects project types and activates appropriate framework features
|
||||
- **Mode Activation Coordinator**: Manages intelligent activation of behavioral modes based on context
|
||||
- **Analytics and Learning System**: Tracks session effectiveness and enables continuous framework improvement
|
||||
- **Performance Optimizer**: Manages session-level performance targets and resource utilization
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### 1. Session Lifecycle Configuration (`session_lifecycle`)
|
||||
|
||||
#### Initialization Settings
|
||||
```yaml
|
||||
initialization:
|
||||
performance_target_ms: 50
|
||||
auto_project_detection: true
|
||||
context_loading_strategy: "selective"
|
||||
framework_exclusion_enabled: true
|
||||
|
||||
default_modes:
|
||||
- "adaptive_intelligence"
|
||||
- "performance_monitoring"
|
||||
|
||||
intelligence_activation:
|
||||
pattern_detection: true
|
||||
mcp_routing: true
|
||||
learning_integration: true
|
||||
compression_optimization: true
|
||||
```
|
||||
|
||||
**Performance Target**: 50ms initialization for immediate user engagement
|
||||
**Selective Loading**: Loads only necessary context for fast startup
|
||||
**Framework Exclusion**: Protects framework content from modification
|
||||
**Default Modes**: Activates adaptive intelligence and performance monitoring by default
|
||||
|
||||
#### Termination Settings
|
||||
```yaml
|
||||
termination:
|
||||
performance_target_ms: 200
|
||||
analytics_generation: true
|
||||
learning_consolidation: true
|
||||
session_persistence: true
|
||||
cleanup_optimization: true
|
||||
```
|
||||
|
||||
**Analytics Generation**: Creates comprehensive session analytics on termination
|
||||
**Learning Consolidation**: Consolidates session learnings for future improvement
|
||||
**Session Persistence**: Saves session state for potential recovery
|
||||
**Cleanup Optimization**: Optimizes resource cleanup for performance
|
||||
|
||||
### 2. Project Type Detection (`project_detection`)
|
||||
|
||||
#### File Indicators
|
||||
```yaml
|
||||
file_indicators:
|
||||
nodejs:
|
||||
- "package.json"
|
||||
- "node_modules/"
|
||||
- "yarn.lock"
|
||||
- "pnpm-lock.yaml"
|
||||
|
||||
python:
|
||||
- "pyproject.toml"
|
||||
- "setup.py"
|
||||
- "requirements.txt"
|
||||
- "__pycache__/"
|
||||
- ".py"
|
||||
|
||||
rust:
|
||||
- "Cargo.toml"
|
||||
- "Cargo.lock"
|
||||
- "src/main.rs"
|
||||
- "src/lib.rs"
|
||||
|
||||
go:
|
||||
- "go.mod"
|
||||
- "go.sum"
|
||||
- "main.go"
|
||||
|
||||
web_frontend:
|
||||
- "index.html"
|
||||
- "public/"
|
||||
- "dist/"
|
||||
- "build/"
|
||||
- "src/components/"
|
||||
```
|
||||
|
||||
**Purpose**: Automatically detects project type based on characteristic files
|
||||
**Multi-Language Support**: Supports major programming languages and frameworks
|
||||
**Progressive Detection**: Multiple indicators increase detection confidence
|
||||
|
||||
#### Framework Detection
|
||||
```yaml
|
||||
framework_detection:
|
||||
react:
|
||||
- "react"
|
||||
- "next.js"
|
||||
- "@types/react"
|
||||
|
||||
vue:
|
||||
- "vue"
|
||||
- "nuxt"
|
||||
- "@vue/cli"
|
||||
|
||||
angular:
|
||||
- "@angular/core"
|
||||
- "angular.json"
|
||||
|
||||
express:
|
||||
- "express"
|
||||
- "app.js"
|
||||
- "server.js"
|
||||
```
|
||||
|
||||
**Framework Intelligence**: Detects specific frameworks within project types
|
||||
**Package Analysis**: Analyzes package.json and similar files for framework indicators
|
||||
**Enhanced Context**: Framework detection enables specialized optimizations
|
||||
|
||||
### 3. Intelligence Activation Rules (`intelligence_activation`)
|
||||
|
||||
#### Mode Detection Patterns
|
||||
```yaml
|
||||
mode_detection:
|
||||
brainstorming:
|
||||
triggers:
|
||||
- "new project"
|
||||
- "not sure"
|
||||
- "thinking about"
|
||||
- "explore"
|
||||
- "brainstorm"
|
||||
confidence_threshold: 0.7
|
||||
auto_activate: true
|
||||
|
||||
task_management:
|
||||
triggers:
|
||||
- "multiple files"
|
||||
- "complex operation"
|
||||
- "system-wide"
|
||||
- "comprehensive"
|
||||
file_count_threshold: 3
|
||||
complexity_threshold: 0.4
|
||||
auto_activate: true
|
||||
|
||||
token_efficiency:
|
||||
triggers:
|
||||
- "resource constraint"
|
||||
- "brevity"
|
||||
- "compressed"
|
||||
- "efficient"
|
||||
resource_threshold_percent: 75
|
||||
conversation_length_threshold: 100
|
||||
auto_activate: true
|
||||
```
|
||||
|
||||
**Automatic Mode Activation**: Intelligent detection and activation based on user patterns
|
||||
**Confidence Thresholds**: Ensures accurate mode selection
|
||||
**Context-Aware**: Considers project characteristics and resource constraints
|
||||
|
||||
#### MCP Server Activation
|
||||
```yaml
|
||||
mcp_server_activation:
|
||||
context7:
|
||||
triggers:
|
||||
- "library"
|
||||
- "documentation"
|
||||
- "framework"
|
||||
- "api reference"
|
||||
project_indicators:
|
||||
- "external_dependencies"
|
||||
- "framework_detected"
|
||||
auto_activate: true
|
||||
|
||||
sequential:
|
||||
triggers:
|
||||
- "analyze"
|
||||
- "debug"
|
||||
- "complex"
|
||||
- "systematic"
|
||||
complexity_threshold: 0.6
|
||||
auto_activate: true
|
||||
|
||||
magic:
|
||||
triggers:
|
||||
- "component"
|
||||
- "ui"
|
||||
- "frontend"
|
||||
- "design"
|
||||
project_type_match: ["web_frontend", "react", "vue", "angular"]
|
||||
auto_activate: true
|
||||
|
||||
serena:
|
||||
triggers:
|
||||
- "navigate"
|
||||
- "find"
|
||||
- "search"
|
||||
- "analyze"
|
||||
file_count_min: 5
|
||||
complexity_min: 0.4
|
||||
auto_activate: true
|
||||
```
|
||||
|
||||
**Intelligent Server Selection**: Automatic MCP server activation based on task requirements
|
||||
**Project Context**: Server selection considers project type and characteristics
|
||||
**Threshold Management**: Prevents unnecessary server activation through intelligent thresholds
|
||||
|
||||
### 4. Session Analytics Configuration (`session_analytics`)
|
||||
|
||||
#### Performance Tracking
|
||||
```yaml
|
||||
performance_tracking:
|
||||
enabled: true
|
||||
metrics:
|
||||
- "operation_count"
|
||||
- "tool_usage_patterns"
|
||||
- "mcp_server_effectiveness"
|
||||
- "error_rates"
|
||||
- "completion_times"
|
||||
- "resource_utilization"
|
||||
```
|
||||
|
||||
**Comprehensive Metrics**: Tracks all key performance dimensions
|
||||
**Usage Patterns**: Analyzes tool and server usage for optimization
|
||||
**Error Tracking**: Monitors error rates for reliability improvement
|
||||
|
||||
#### Effectiveness Measurement
|
||||
```yaml
|
||||
effectiveness_measurement:
|
||||
enabled: true
|
||||
factors:
|
||||
productivity: "weight: 0.4"
|
||||
quality: "weight: 0.3"
|
||||
user_satisfaction: "weight: 0.2"
|
||||
learning_value: "weight: 0.1"
|
||||
```
|
||||
|
||||
**Weighted Effectiveness**: Balanced assessment across multiple factors
|
||||
**Productivity Focus**: Highest weight on productivity outcomes
|
||||
**Quality Assurance**: Significant weight on quality maintenance
|
||||
**User Experience**: Important consideration for user satisfaction
|
||||
**Learning Value**: Tracks framework learning and improvement
|
||||
|
||||
#### Learning Consolidation
|
||||
```yaml
|
||||
learning_consolidation:
|
||||
enabled: true
|
||||
pattern_detection: true
|
||||
adaptation_creation: true
|
||||
effectiveness_feedback: true
|
||||
insight_generation: true
|
||||
```
|
||||
|
||||
**Pattern Learning**: Identifies successful patterns for replication
|
||||
**Adaptive Improvement**: Creates adaptations based on session outcomes
|
||||
**Feedback Integration**: Incorporates effectiveness feedback into learning
|
||||
**Insight Generation**: Generates actionable insights for framework improvement
|
||||
|
||||
### 5. Session Persistence (`session_persistence`)
|
||||
|
||||
#### Storage Strategy
|
||||
```yaml
|
||||
enabled: true
|
||||
storage_strategy: "intelligent_compression"
|
||||
retention_policy:
|
||||
session_data_days: 90
|
||||
analytics_data_days: 365
|
||||
learning_data_persistent: true
|
||||
|
||||
compression_settings:
|
||||
session_metadata: "efficient" # 40-70% compression
|
||||
analytics_data: "compressed" # 70-85% compression
|
||||
learning_data: "minimal" # Preserve learning quality
|
||||
```
|
||||
|
||||
**Intelligent Compression**: Applies appropriate compression based on data type
|
||||
**Retention Management**: Balances storage with analytical value
|
||||
**Learning Preservation**: Maintains high fidelity for learning data
|
||||
|
||||
#### Cleanup Automation
|
||||
```yaml
|
||||
cleanup_automation:
|
||||
enabled: true
|
||||
old_session_cleanup: true
|
||||
max_sessions_retained: 50
|
||||
storage_optimization: true
|
||||
```
|
||||
|
||||
**Automatic Cleanup**: Prevents storage bloat through automated cleanup
|
||||
**Session Limits**: Maintains reasonable number of retained sessions
|
||||
**Storage Optimization**: Continuously optimizes storage usage
|
||||
|
||||
### 6. Notification Processing (`notifications`)
|
||||
|
||||
#### Core Notification Settings
|
||||
```yaml
|
||||
enabled: true
|
||||
just_in_time_loading: true
|
||||
pattern_updates: true
|
||||
intelligence_updates: true
|
||||
|
||||
priority_handling:
|
||||
critical: "immediate_processing"
|
||||
high: "fast_track_processing"
|
||||
medium: "standard_processing"
|
||||
low: "background_processing"
|
||||
```
|
||||
|
||||
**Just-in-Time Loading**: Loads documentation and patterns as needed
|
||||
**Priority Processing**: Handles notifications based on priority levels
|
||||
**Intelligence Updates**: Updates framework intelligence based on new patterns
|
||||
|
||||
#### Caching Strategy
|
||||
```yaml
|
||||
caching_strategy:
|
||||
documentation_cache_minutes: 30
|
||||
pattern_cache_minutes: 60
|
||||
intelligence_cache_minutes: 15
|
||||
```
|
||||
|
||||
**Documentation Caching**: 30-minute cache for documentation lookup
|
||||
**Pattern Caching**: 60-minute cache for pattern recognition
|
||||
**Intelligence Caching**: 15-minute cache for intelligence updates
|
||||
|
||||
### 7. Task Management Integration (`task_management`)
|
||||
|
||||
#### Delegation Strategies
|
||||
```yaml
|
||||
enabled: true
|
||||
delegation_strategies:
|
||||
files: "file_based_delegation"
|
||||
folders: "directory_based_delegation"
|
||||
auto: "intelligent_auto_detection"
|
||||
|
||||
wave_orchestration:
|
||||
enabled: true
|
||||
complexity_threshold: 0.4
|
||||
file_count_threshold: 3
|
||||
operation_types_threshold: 2
|
||||
```
|
||||
|
||||
**Multi-Strategy Support**: Supports file, folder, and auto-delegation strategies
|
||||
**Wave Orchestration**: Enables complex multi-step operation coordination
|
||||
**Intelligent Thresholds**: Activates advanced features based on operation complexity
|
||||
|
||||
#### Performance Optimization
|
||||
```yaml
|
||||
performance_optimization:
|
||||
parallel_execution: true
|
||||
resource_management: true
|
||||
coordination_efficiency: true
|
||||
```
|
||||
|
||||
**Parallel Processing**: Enables parallel execution for performance
|
||||
**Resource Management**: Optimizes resource allocation across tasks
|
||||
**Coordination**: Efficient coordination of multiple operations
|
||||
|
||||
### 8. User Experience Configuration (`user_experience`)
|
||||
|
||||
#### Session Feedback
|
||||
```yaml
|
||||
session_feedback:
|
||||
enabled: true
|
||||
satisfaction_tracking: true
|
||||
improvement_suggestions: true
|
||||
```
|
||||
|
||||
**Satisfaction Tracking**: Monitors user satisfaction throughout session
|
||||
**Improvement Suggestions**: Provides suggestions for enhanced experience
|
||||
|
||||
#### Personalization
|
||||
```yaml
|
||||
personalization:
|
||||
enabled: true
|
||||
preference_learning: true
|
||||
adaptation_application: true
|
||||
context_awareness: true
|
||||
```
|
||||
|
||||
**Preference Learning**: Learns user preferences over time
|
||||
**Adaptive Application**: Applies learned preferences to improve experience
|
||||
**Context Awareness**: Considers context in personalization decisions
|
||||
|
||||
#### Progressive Enhancement
|
||||
```yaml
|
||||
progressive_enhancement:
|
||||
enabled: true
|
||||
capability_discovery: true
|
||||
feature_introduction: true
|
||||
learning_curve_optimization: true
|
||||
```
|
||||
|
||||
**Capability Discovery**: Gradually discovers and introduces new capabilities
|
||||
**Feature Introduction**: Introduces features at appropriate times
|
||||
**Learning Curve**: Optimizes learning curve for user adoption
|
||||
|
||||
### 9. Performance Targets (`performance_targets`)
|
||||
|
||||
#### Session Performance
|
||||
```yaml
|
||||
session_start_ms: 50
|
||||
session_stop_ms: 200
|
||||
context_loading_ms: 500
|
||||
analytics_generation_ms: 1000
|
||||
```
|
||||
|
||||
**Fast Startup**: 50ms session start for immediate engagement
|
||||
**Efficient Termination**: 200ms session stop with analytics
|
||||
**Context Loading**: 500ms context loading for comprehensive initialization
|
||||
**Analytics**: 1000ms analytics generation for comprehensive insights
|
||||
|
||||
#### Efficiency Targets
|
||||
```yaml
|
||||
efficiency_targets:
|
||||
productivity_score: 0.7
|
||||
quality_score: 0.8
|
||||
satisfaction_score: 0.7
|
||||
learning_value: 0.6
|
||||
```
|
||||
|
||||
**Productivity**: 70% productivity score target
|
||||
**Quality**: 80% quality score maintenance
|
||||
**Satisfaction**: 70% user satisfaction target
|
||||
**Learning**: 60% learning value extraction
|
||||
|
||||
#### Resource Utilization
|
||||
```yaml
|
||||
resource_utilization:
|
||||
memory_efficient: true
|
||||
cpu_optimization: true
|
||||
token_management: true
|
||||
storage_optimization: true
|
||||
```
|
||||
|
||||
**Comprehensive Optimization**: Optimizes all resource dimensions
|
||||
**Token Management**: Intelligent token usage optimization
|
||||
**Storage Efficiency**: Efficient storage utilization and cleanup
|
||||
|
||||
### 10. Error Handling and Recovery (`error_handling`)
|
||||
|
||||
#### Core Error Handling
|
||||
```yaml
|
||||
graceful_degradation: true
|
||||
fallback_strategies: true
|
||||
error_learning: true
|
||||
recovery_optimization: true
|
||||
```
|
||||
|
||||
**Graceful Degradation**: Maintains functionality during errors
|
||||
**Fallback Strategies**: Multiple fallback options for resilience
|
||||
**Error Learning**: Learns from errors to prevent recurrence
|
||||
|
||||
#### Session Recovery
|
||||
```yaml
|
||||
session_recovery:
|
||||
auto_recovery: true
|
||||
state_preservation: true
|
||||
context_restoration: true
|
||||
learning_retention: true
|
||||
```
|
||||
|
||||
**Automatic Recovery**: Attempts automatic recovery from errors
|
||||
**State Preservation**: Preserves session state during recovery
|
||||
**Context Restoration**: Restores context after recovery
|
||||
**Learning Retention**: Maintains learning data through recovery
|
||||
|
||||
#### Error Pattern Detection
|
||||
```yaml
|
||||
error_patterns:
|
||||
detection: true
|
||||
prevention: true
|
||||
learning_integration: true
|
||||
adaptation_triggers: true
|
||||
```
|
||||
|
||||
**Pattern Detection**: Identifies recurring error patterns
|
||||
**Prevention**: Implements prevention strategies for known patterns
|
||||
**Learning Integration**: Integrates error learning with overall framework learning
|
||||
|
||||
## Integration Points
|
||||
|
||||
### 1. Hook Integration (`integration`)
|
||||
|
||||
#### MCP Server Coordination
|
||||
```yaml
|
||||
mcp_servers:
|
||||
coordination: "seamless"
|
||||
fallback_handling: "automatic"
|
||||
performance_monitoring: "continuous"
|
||||
```
|
||||
|
||||
**Seamless Coordination**: Smooth integration across all MCP servers
|
||||
**Automatic Fallbacks**: Automatic fallback handling for server issues
|
||||
**Continuous Monitoring**: Real-time performance monitoring
|
||||
|
||||
#### Learning Engine Integration
|
||||
```yaml
|
||||
learning_engine:
|
||||
session_learning: true
|
||||
pattern_recognition: true
|
||||
effectiveness_tracking: true
|
||||
adaptation_application: true
|
||||
```
|
||||
|
||||
**Session Learning**: Comprehensive learning from session patterns
|
||||
**Pattern Recognition**: Identifies successful session patterns
|
||||
**Effectiveness Tracking**: Tracks session effectiveness over time
|
||||
**Adaptation**: Applies learned patterns to improve future sessions
|
||||
|
||||
#### Quality Gates Integration
|
||||
```yaml
|
||||
quality_gates:
|
||||
session_validation: true
|
||||
analytics_verification: true
|
||||
learning_quality_assurance: true
|
||||
```
|
||||
|
||||
**Session Validation**: Validates session outcomes against quality standards
|
||||
**Analytics Verification**: Ensures analytics accuracy and completeness
|
||||
**Learning QA**: Quality assurance for learning data and insights
|
||||
|
||||
### 2. Development Support (`development_support`)
|
||||
|
||||
```yaml
|
||||
session_debugging: true
|
||||
performance_profiling: true
|
||||
analytics_validation: true
|
||||
learning_verification: true
|
||||
|
||||
metrics_collection:
|
||||
detailed_timing: true
|
||||
resource_tracking: true
|
||||
effectiveness_measurement: true
|
||||
quality_assessment: true
|
||||
```
|
||||
|
||||
**Debugging Support**: Enhanced debugging capabilities for development
|
||||
**Performance Profiling**: Detailed performance analysis tools
|
||||
**Metrics Collection**: Comprehensive metrics for analysis and optimization
|
||||
|
||||
## Performance Implications
|
||||
|
||||
### 1. Session Lifecycle Performance
|
||||
|
||||
#### Initialization Impact
|
||||
- **Startup Time**: 45-55ms typical session initialization
|
||||
- **Context Loading**: 400-600ms for selective context loading
|
||||
- **Memory Usage**: 50-100MB initial memory allocation
|
||||
- **CPU Usage**: 20-40% CPU during initialization
|
||||
|
||||
#### Termination Impact
|
||||
- **Analytics Generation**: 800ms-1.2s for comprehensive analytics
|
||||
- **Learning Consolidation**: 200-500ms for learning data processing
|
||||
- **Cleanup Operations**: 100-300ms for resource cleanup
|
||||
- **Storage Operations**: 50-200ms for session persistence
|
||||
|
||||
### 2. Project Detection Performance
|
||||
|
||||
#### Detection Speed
|
||||
- **File System Scanning**: 10-50ms for project type detection
|
||||
- **Framework Analysis**: 20-100ms for framework detection
|
||||
- **Dependency Analysis**: 50-200ms for dependency graph analysis
|
||||
- **Total Detection**: 100-400ms for complete project analysis
|
||||
|
||||
#### Memory Impact
|
||||
- **Detection Data**: 10-50KB for project detection information
|
||||
- **Framework Metadata**: 20-100KB for framework-specific data
|
||||
- **Dependency Cache**: 100KB-1MB for dependency information
|
||||
|
||||
### 3. Analytics and Learning Performance
|
||||
|
||||
#### Analytics Generation
|
||||
- **Metrics Collection**: 50-200ms for comprehensive metrics gathering
|
||||
- **Effectiveness Calculation**: 100-500ms for effectiveness analysis
|
||||
- **Pattern Analysis**: 200ms-1s for pattern recognition
|
||||
- **Insight Generation**: 300ms-2s for actionable insights
|
||||
|
||||
#### Learning System Impact
|
||||
- **Pattern Learning**: 100-500ms for pattern updates
|
||||
- **Adaptation Creation**: 200ms-1s for adaptation generation
|
||||
- **Effectiveness Feedback**: 50-200ms for feedback integration
|
||||
- **Storage Updates**: 100-400ms for learning data persistence
|
||||
|
||||
## Configuration Best Practices
|
||||
|
||||
### 1. Production Session Configuration
|
||||
```yaml
|
||||
# Optimize for reliability and performance
|
||||
session_lifecycle:
|
||||
initialization:
|
||||
performance_target_ms: 75 # Slightly relaxed for stability
|
||||
framework_exclusion_enabled: true # Always protect framework
|
||||
|
||||
session_analytics:
|
||||
performance_tracking:
|
||||
enabled: true # Essential for production monitoring
|
||||
|
||||
session_persistence:
|
||||
retention_policy:
|
||||
session_data_days: 30 # Shorter retention for production
|
||||
analytics_data_days: 180 # Sufficient for trend analysis
|
||||
```
|
||||
|
||||
### 2. Development Session Configuration
|
||||
```yaml
|
||||
# Enhanced debugging and learning
|
||||
development_support:
|
||||
session_debugging: true
|
||||
performance_profiling: true
|
||||
detailed_timing: true
|
||||
|
||||
session_analytics:
|
||||
learning_consolidation:
|
||||
effectiveness_feedback: true
|
||||
adaptation_creation: true # Enable aggressive learning
|
||||
```
|
||||
|
||||
### 3. Performance-Optimized Configuration
|
||||
```yaml
|
||||
# Minimize overhead for performance-critical environments
|
||||
session_lifecycle:
|
||||
initialization:
|
||||
performance_target_ms: 25 # Aggressive target
|
||||
context_loading_strategy: "minimal" # Minimal context loading
|
||||
|
||||
session_analytics:
|
||||
performance_tracking:
|
||||
metrics: ["operation_count", "completion_times"] # Essential metrics only
|
||||
```
|
||||
|
||||
### 4. Learning-Optimized Configuration
|
||||
```yaml
|
||||
# Maximum learning and adaptation
|
||||
session_analytics:
|
||||
learning_consolidation:
|
||||
enabled: true
|
||||
pattern_detection: true
|
||||
adaptation_creation: true
|
||||
insight_generation: true
|
||||
|
||||
user_experience:
|
||||
personalization:
|
||||
preference_learning: true
|
||||
adaptation_application: true
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Session Issues
|
||||
|
||||
#### Slow Session Initialization
|
||||
- **Symptoms**: Session startup exceeds 50ms target consistently
|
||||
- **Analysis**: Check project detection performance, context loading strategy
|
||||
- **Solutions**: Optimize project detection patterns, reduce initial context loading
|
||||
- **Monitoring**: Track initialization components and identify bottlenecks
|
||||
|
||||
#### Project Detection Failures
|
||||
- **Symptoms**: Incorrect project type detection or missing framework detection
|
||||
- **Diagnosis**: Review project indicators and framework patterns
|
||||
- **Resolution**: Add missing patterns, adjust detection confidence thresholds
|
||||
- **Validation**: Test detection with various project structures
|
||||
|
||||
#### Analytics Generation Issues
|
||||
- **Symptoms**: Slow or incomplete analytics generation at session end
|
||||
- **Investigation**: Check metrics collection performance and data completeness
|
||||
- **Optimization**: Reduce analytics complexity, optimize metrics calculation
|
||||
- **Quality**: Ensure analytics accuracy while maintaining performance
|
||||
|
||||
#### Learning System Problems
|
||||
- **Symptoms**: No learning observed, ineffective adaptations
|
||||
- **Analysis**: Review learning data collection and pattern recognition
|
||||
- **Enhancement**: Adjust learning thresholds, improve pattern detection
|
||||
- **Validation**: Test learning effectiveness with controlled scenarios
|
||||
|
||||
### Performance Troubleshooting
|
||||
|
||||
#### Memory Usage Issues
|
||||
- **Monitoring**: Track session memory usage patterns and growth
|
||||
- **Optimization**: Optimize context loading, implement better cleanup
|
||||
- **Limits**: Set appropriate memory limits and cleanup triggers
|
||||
- **Analysis**: Profile memory usage during different session phases
|
||||
|
||||
#### CPU Usage Problems
|
||||
- **Identification**: Monitor CPU usage during session operations
|
||||
- **Optimization**: Optimize project detection, reduce analytics complexity
|
||||
- **Balancing**: Balance functionality with CPU usage requirements
|
||||
- **Profiling**: Use profiling tools to identify CPU bottlenecks
|
||||
|
||||
#### Storage and Persistence Issues
|
||||
- **Management**: Monitor storage usage and cleanup effectiveness
|
||||
- **Optimization**: Optimize compression settings, adjust retention policies
|
||||
- **Maintenance**: Implement regular cleanup and optimization routines
|
||||
- **Analysis**: Track storage growth patterns and optimize accordingly
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Session Lifecycle**: See SESSION_LIFECYCLE.md for comprehensive session management patterns
|
||||
- **Hook Integration**: Reference hook documentation for session-hook coordination
|
||||
- **Analytics and Learning**: Review learning system documentation for detailed analytics
|
||||
- **Performance Monitoring**: See performance.yaml.md for performance targets and monitoring
|
||||
|
||||
## Version History
|
||||
|
||||
- **v1.0.0**: Initial session configuration
|
||||
- Comprehensive session lifecycle management with 50ms initialization target
|
||||
- Multi-language project detection with framework intelligence
|
||||
- Automatic mode and MCP server activation based on context
|
||||
- Session analytics with effectiveness measurement and learning consolidation
|
||||
- User experience optimization with personalization and progressive enhancement
|
||||
- Error handling and recovery with pattern detection and prevention
|
||||
@@ -1,244 +0,0 @@
|
||||
# Hook Settings Configuration (`settings.json`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `settings.json` file defines the Claude Code hook configuration settings for the SuperClaude-Lite framework. This file registers all framework hooks with Claude Code and specifies their execution parameters.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
This configuration provides:
|
||||
- **Hook Registration**: Registers all 7 SuperClaude hooks with Claude Code
|
||||
- **Execution Configuration**: Defines command paths, timeouts, and execution patterns
|
||||
- **Universal Matching**: Applies hooks to all operations through `"matcher": "*"`
|
||||
- **Timeout Management**: Establishes execution time limits for each hook
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### Basic Pattern
|
||||
```json
|
||||
{
|
||||
"hooks": {
|
||||
"HookName": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/script.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Hook Definitions
|
||||
|
||||
The actual configuration registers these hooks:
|
||||
|
||||
#### SessionStart Hook
|
||||
```json
|
||||
"SessionStart": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/session_start.py",
|
||||
"timeout": 10
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Purpose**: Initialize sessions and detect project context
|
||||
**Timeout**: 10 seconds for session initialization
|
||||
**Execution**: Runs at the start of every Claude Code session
|
||||
|
||||
#### PreToolUse Hook
|
||||
```json
|
||||
"PreToolUse": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/pre_tool_use.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Purpose**: Pre-process tool usage and provide intelligent routing
|
||||
**Timeout**: 15 seconds for analysis and routing decisions
|
||||
**Execution**: Runs before every tool use operation
|
||||
|
||||
#### PostToolUse Hook
|
||||
```json
|
||||
"PostToolUse": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/post_tool_use.py",
|
||||
"timeout": 10
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Purpose**: Post-process tool results and apply quality gates
|
||||
**Timeout**: 10 seconds for result analysis and validation
|
||||
**Execution**: Runs after every tool use operation
|
||||
|
||||
#### PreCompact Hook
|
||||
```json
|
||||
"PreCompact": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/pre_compact.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Purpose**: Apply intelligent compression before context compaction
|
||||
**Timeout**: 15 seconds for compression analysis and application
|
||||
**Execution**: Runs before Claude Code compacts conversation context
|
||||
|
||||
#### Notification Hook
|
||||
```json
|
||||
"Notification": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/notification.py",
|
||||
"timeout": 10
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Purpose**: Handle notifications and update learning patterns
|
||||
**Timeout**: 10 seconds for notification processing
|
||||
**Execution**: Runs when Claude Code sends notifications
|
||||
|
||||
#### Stop Hook
|
||||
```json
|
||||
"Stop": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/stop.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Purpose**: Session cleanup and analytics generation
|
||||
**Timeout**: 15 seconds for cleanup and analysis
|
||||
**Execution**: Runs when Claude Code session ends
|
||||
|
||||
#### SubagentStop Hook
|
||||
```json
|
||||
"SubagentStop": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/subagent_stop.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
**Purpose**: Subagent coordination and task management analytics
|
||||
**Timeout**: 15 seconds for subagent cleanup
|
||||
**Execution**: Runs when Claude Code subagent sessions end
|
||||
|
||||
## Key Configuration Elements
|
||||
|
||||
### Universal Matcher
|
||||
- **Pattern**: `"matcher": "*"`
|
||||
- **Effect**: All hooks apply to every operation
|
||||
- **Purpose**: Ensures consistent framework behavior across all interactions
|
||||
|
||||
### Command Type
|
||||
- **Type**: `"command"`
|
||||
- **Execution**: Runs external Python scripts
|
||||
- **Environment**: Uses system Python 3 installation
|
||||
|
||||
### File Paths
|
||||
- **Location**: `~/.claude/hooks/`
|
||||
- **Naming**: Matches hook names in snake_case (e.g., `session_start.py`)
|
||||
- **Permissions**: Scripts must be executable
|
||||
|
||||
### Timeout Values
|
||||
- **SessionStart**: 10 seconds (session initialization)
|
||||
- **PreToolUse**: 15 seconds (analysis and routing)
|
||||
- **PostToolUse**: 10 seconds (result processing)
|
||||
- **PreCompact**: 15 seconds (compression)
|
||||
- **Notification**: 10 seconds (notification handling)
|
||||
- **Stop**: 15 seconds (cleanup and analytics)
|
||||
- **SubagentStop**: 15 seconds (subagent coordination)
|
||||
|
||||
## Installation Requirements
|
||||
|
||||
### File Installation
|
||||
The framework installation process must:
|
||||
1. Copy Python hook scripts to `~/.claude/hooks/`
|
||||
2. Set executable permissions on all hook scripts
|
||||
3. Install this `settings.json` file for Claude Code to read
|
||||
4. Verify Python 3 is available in the system PATH
|
||||
|
||||
### Dependencies
|
||||
- Python 3.7+ installation
|
||||
- Required Python packages (see hook implementations)
|
||||
- Read/write access to `~/.claude/hooks/` directory
|
||||
- Network access for MCP server communication (if used)
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Hook Not Executing
|
||||
- **Check file paths**: Verify scripts exist at specified locations
|
||||
- **Check permissions**: Ensure scripts are executable
|
||||
- **Check Python**: Verify Python 3 is available in PATH
|
||||
- **Check timeouts**: Increase timeout if hooks are timing out
|
||||
|
||||
### Performance Issues
|
||||
- **Timeout Tuning**: Adjust timeout values for your system performance
|
||||
- **Hook Optimization**: Review hook configuration files for performance settings
|
||||
- **Parallel Execution**: Some hooks can be optimized for parallel execution
|
||||
|
||||
### Path Issues
|
||||
- **Absolute Paths**: Use absolute paths if relative paths cause issues
|
||||
- **User Directory**: Ensure `~/.claude/hooks/` expands correctly in your environment
|
||||
- **File Permissions**: Verify both read and execute permissions on hook files
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Hook Implementation**: Individual hook Python files for specific behavior
|
||||
- **Configuration Files**: YAML configuration files for hook behavior tuning
|
||||
- **Installation Guide**: Framework installation and setup documentation
|
||||
@@ -1,350 +0,0 @@
|
||||
# SuperClaude Master Configuration (`superclaude-config.json`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `superclaude-config.json` file serves as the master configuration file for the SuperClaude-Lite framework. This comprehensive JSON configuration controls all aspects of hook execution, MCP server integration, mode coordination, and quality gates within the framework.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
The master configuration file acts as the central control system for:
|
||||
- **Hook Configuration Management**: Defines behavior and settings for all 7 framework hooks
|
||||
- **MCP Server Integration**: Coordinates intelligent routing and fallback strategies across servers
|
||||
- **Mode Orchestration**: Manages behavioral mode activation and coordination patterns
|
||||
- **Quality Gate Enforcement**: Implements the 8-step validation cycle throughout operations
|
||||
- **Performance Monitoring**: Establishes targets and thresholds for optimization
|
||||
- **Learning System Integration**: Enables cross-hook learning and adaptation
|
||||
|
||||
## File Structure and Organization
|
||||
|
||||
### 1. Framework Metadata
|
||||
```json
|
||||
{
|
||||
"superclaude": {
|
||||
"description": "SuperClaude-Lite Framework Configuration",
|
||||
"version": "1.0.0",
|
||||
"framework": "superclaude-lite",
|
||||
"enabled": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Purpose**: Identifies framework version and overall enablement status.
|
||||
|
||||
### 2. Hook Configurations (`hook_configurations`)
|
||||
|
||||
The master configuration defines settings for all 7 SuperClaude hooks:
|
||||
|
||||
#### Session Start Hook
|
||||
- **Performance Target**: 50ms initialization
|
||||
- **Features**: Smart project context loading, automatic mode detection, MCP intelligence routing
|
||||
- **Configuration**: Auto-detection, framework exclusion, intelligence activation
|
||||
- **Error Handling**: Graceful fallback with context preservation
|
||||
|
||||
#### Pre-Tool Use Hook
|
||||
- **Performance Target**: 200ms routing decision
|
||||
- **Features**: Intelligent tool routing, MCP server selection, real-time adaptation
|
||||
- **Integration**: All 6 MCP servers with quality gates and learning engine
|
||||
- **Configuration**: Pattern detection, learning adaptations, fallback strategies
|
||||
|
||||
#### Post-Tool Use Hook
|
||||
- **Performance Target**: 100ms validation
|
||||
- **Features**: Quality validation, rules compliance, effectiveness measurement
|
||||
- **Validation Levels**: Basic → Standard → Comprehensive → Production
|
||||
- **Configuration**: Rules validation, principles alignment, learning integration
|
||||
|
||||
#### Pre-Compact Hook
|
||||
- **Performance Target**: 150ms compression decision
|
||||
- **Features**: Intelligent compression strategy selection, selective content preservation
|
||||
- **Compression Levels**: Minimal (0-40%) → Emergency (95%+)
|
||||
- **Configuration**: Framework protection, quality preservation target (95%)
|
||||
|
||||
#### Notification Hook
|
||||
- **Performance Target**: 100ms processing
|
||||
- **Features**: Just-in-time documentation loading, dynamic pattern updates
|
||||
- **Caching**: Documentation (30min), patterns (60min), intelligence (15min)
|
||||
- **Configuration**: Real-time learning, performance optimization through caching
|
||||
|
||||
#### Stop Hook
|
||||
- **Performance Target**: 200ms analytics generation
|
||||
- **Features**: Comprehensive session analytics, learning consolidation
|
||||
- **Analytics**: Performance metrics, effectiveness measurement, optimization recommendations
|
||||
- **Configuration**: Session persistence, performance tracking, recommendation generation
|
||||
|
||||
#### Subagent Stop Hook
|
||||
- **Performance Target**: 150ms coordination analytics
|
||||
- **Features**: Subagent performance analytics, delegation effectiveness measurement
|
||||
- **Task Management**: Wave orchestration, parallel coordination, performance optimization
|
||||
- **Configuration**: Delegation analytics, coordination measurement, learning integration
|
||||
|
||||
### 3. Global Configuration (`global_configuration`)
|
||||
|
||||
#### Framework Integration
|
||||
- **SuperClaude Compliance**: Ensures adherence to framework standards
|
||||
- **YAML-Driven Logic**: Hot-reload configuration capability
|
||||
- **Cross-Hook Coordination**: Enables hooks to share context and learnings
|
||||
|
||||
#### Performance Monitoring
|
||||
- **Real-Time Tracking**: Continuous performance measurement
|
||||
- **Target Enforcement**: Automatic optimization when targets are missed
|
||||
- **Analytics**: Performance trend analysis and optimization suggestions
|
||||
|
||||
#### Learning System
|
||||
- **Cross-Hook Learning**: Shared knowledge across hook executions
|
||||
- **Adaptation Application**: Real-time improvement based on effectiveness
|
||||
- **Pattern Recognition**: Identifies successful operational patterns
|
||||
|
||||
#### Security
|
||||
- **Input Validation**: Protects against malicious input
|
||||
- **Path Traversal Protection**: Prevents unauthorized file access
|
||||
- **Resource Limits**: Prevents resource exhaustion attacks
|
||||
|
||||
### 4. MCP Server Integration (`mcp_server_integration`)
|
||||
|
||||
Defines integration patterns for all 6 MCP servers:
|
||||
|
||||
#### Server Definitions
|
||||
- **Context7**: Library documentation and framework patterns (standard profile)
|
||||
- **Sequential**: Multi-step reasoning and complex analysis (intensive profile)
|
||||
- **Magic**: UI component generation and design systems (standard profile)
|
||||
- **Playwright**: Browser automation and testing (intensive profile)
|
||||
- **Morphllm**: Intelligent editing with fast apply (lightweight profile)
|
||||
- **Serena**: Semantic analysis and memory management (standard profile)
|
||||
|
||||
#### Coordination Settings
|
||||
- **Intelligent Routing**: Automatic server selection based on task requirements
|
||||
- **Fallback Strategies**: Graceful degradation when servers are unavailable
|
||||
- **Performance Optimization**: Load balancing and resource management
|
||||
- **Learning Adaptation**: Real-time improvement of routing decisions
|
||||
|
||||
### 5. Mode Integration (`mode_integration`)
|
||||
|
||||
#### Supported Modes
|
||||
- **Brainstorming**: Interactive requirements discovery (sequential, context7)
|
||||
- **Task Management**: Multi-layer task orchestration (serena, morphllm)
|
||||
- **Token Efficiency**: Intelligent token optimization (morphllm)
|
||||
- **Introspection**: Meta-cognitive analysis (sequential)
|
||||
|
||||
#### Mode-Hook Coordination
|
||||
Each mode specifies which hooks it integrates with and which MCP servers it prefers.
|
||||
|
||||
### 6. Quality Gates (`quality_gates`)
|
||||
|
||||
Implements the 8-step validation cycle:
|
||||
1. **Syntax Validation**: Language-specific syntax checking
|
||||
2. **Type Analysis**: Type compatibility and inference
|
||||
3. **Code Quality**: Linting rules and quality standards
|
||||
4. **Security Assessment**: Vulnerability scanning and OWASP compliance
|
||||
5. **Testing Validation**: Test coverage and quality assurance
|
||||
6. **Performance Analysis**: Performance benchmarking and optimization
|
||||
7. **Documentation Verification**: Documentation completeness and accuracy
|
||||
8. **Integration Testing**: End-to-end validation and deployment readiness
|
||||
|
||||
#### Hook Integration
|
||||
- **Pre-Tool Use**: Steps 1-2 (validation preparation)
|
||||
- **Post-Tool Use**: Steps 3-5 (comprehensive validation)
|
||||
- **Stop**: Steps 6-8 (final validation and analytics)
|
||||
|
||||
### 7. Cache Configuration (`cache_configuration`)
|
||||
|
||||
#### Cache Settings
|
||||
- **Cache Directory**: `./cache` for all cached data
|
||||
- **Retention Policies**: Learning data (90 days), session data (30 days), performance data (365 days)
|
||||
- **Automatic Cleanup**: Prevents cache bloat through scheduled cleanup
|
||||
|
||||
### 8. Logging Configuration (`logging_configuration`)
|
||||
|
||||
#### Logging Levels
|
||||
- **Log Level**: INFO (configurable: ERROR, WARNING, INFO, DEBUG)
|
||||
- **Specialized Logging**: Performance, error, learning, and hook execution logging
|
||||
- **Privacy**: Sanitizes user content while preserving correlation data
|
||||
|
||||
### 9. Development Support (`development_support`)
|
||||
|
||||
#### Development Features
|
||||
- **Debugging**: Optional debugging mode (disabled by default)
|
||||
- **Performance Profiling**: Optional profiling capabilities
|
||||
- **Verbose Logging**: Enhanced logging for development
|
||||
- **Test Mode**: Specialized testing configuration
|
||||
|
||||
## Key Configuration Sections
|
||||
|
||||
### Performance Targets
|
||||
Each hook has specific performance targets:
|
||||
- **Session Start**: 50ms (critical priority)
|
||||
- **Pre-Tool Use**: 200ms (high priority)
|
||||
- **Post-Tool Use**: 100ms (medium priority)
|
||||
- **Pre-Compact**: 150ms (high priority)
|
||||
- **Notification**: 100ms (medium priority)
|
||||
- **Stop**: 200ms (low priority)
|
||||
- **Subagent Stop**: 150ms (medium priority)
|
||||
|
||||
### Default Values and Meanings
|
||||
|
||||
#### Hook Enablement
|
||||
All hooks are enabled by default (`"enabled": true`) to provide full framework functionality.
|
||||
|
||||
#### Performance Monitoring
|
||||
Real-time tracking is enabled with target enforcement and optimization suggestions.
|
||||
|
||||
#### Learning System
|
||||
Cross-hook learning is enabled to continuously improve framework effectiveness.
|
||||
|
||||
#### Security Settings
|
||||
All security features are enabled by default for production-ready security.
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### Configuration Loading
|
||||
Hooks load configuration through the shared YAML loader system, enabling:
|
||||
- **Hot Reload**: Configuration changes without restart
|
||||
- **Environment-Specific**: Different configs for development/production
|
||||
- **Validation**: Configuration validation before application
|
||||
|
||||
### Cross-Hook Communication
|
||||
The configuration enables hooks to:
|
||||
- **Share Context**: Pass relevant information between hooks
|
||||
- **Coordinate Actions**: Avoid conflicts through intelligent coordination
|
||||
- **Learn Together**: Share effectiveness insights across hook executions
|
||||
|
||||
## Performance Implications
|
||||
|
||||
### Memory Usage
|
||||
- **Configuration Size**: ~50KB typical configuration
|
||||
- **Cache Impact**: Up to 100MB cache with automatic cleanup
|
||||
- **Learning Data**: Persistent learning data with compression
|
||||
|
||||
### Processing Overhead
|
||||
- **Configuration Loading**: <10ms initial load
|
||||
- **Validation**: <5ms per configuration access
|
||||
- **Hot Reload**: <50ms configuration refresh
|
||||
|
||||
### Network Impact
|
||||
- **MCP Coordination**: Intelligent caching reduces network calls
|
||||
- **Documentation Loading**: Just-in-time loading minimizes bandwidth usage
|
||||
|
||||
## Configuration Best Practices
|
||||
|
||||
### 1. Performance Tuning
|
||||
```json
|
||||
{
|
||||
"hook_configurations": {
|
||||
"session_start": {
|
||||
"performance_target_ms": 50,
|
||||
"configuration": {
|
||||
"auto_project_detection": true,
|
||||
"performance_monitoring": true
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Recommendation**: Keep performance targets aggressive but achievable for your environment.
|
||||
|
||||
### 2. Security Hardening
|
||||
```json
|
||||
{
|
||||
"global_configuration": {
|
||||
"security": {
|
||||
"input_validation": true,
|
||||
"path_traversal_protection": true,
|
||||
"timeout_protection": true,
|
||||
"resource_limits": true
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Recommendation**: Never disable security features in production environments.
|
||||
|
||||
### 3. Learning Optimization
|
||||
```json
|
||||
{
|
||||
"global_configuration": {
|
||||
"learning_system": {
|
||||
"enabled": true,
|
||||
"cross_hook_learning": true,
|
||||
"effectiveness_tracking": true,
|
||||
"pattern_recognition": true
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Recommendation**: Enable learning system for continuous improvement, but monitor resource usage.
|
||||
|
||||
### 4. Mode Configuration
|
||||
```json
|
||||
{
|
||||
"mode_integration": {
|
||||
"enabled": true,
|
||||
"modes": {
|
||||
"token_efficiency": {
|
||||
"hooks": ["pre_compact", "session_start"],
|
||||
"mcp_servers": ["morphllm"]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Recommendation**: Configure modes based on your primary use cases and available MCP servers.
|
||||
|
||||
### 5. Cache Management
|
||||
```json
|
||||
{
|
||||
"cache_configuration": {
|
||||
"learning_data_retention_days": 90,
|
||||
"session_data_retention_days": 30,
|
||||
"automatic_cleanup": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Recommendation**: Balance retention periods with storage requirements and privacy needs.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Configuration Issues
|
||||
|
||||
#### Performance Degradation
|
||||
- **Symptoms**: Hooks exceeding performance targets
|
||||
- **Solutions**: Adjust performance targets, enable caching, reduce feature complexity
|
||||
- **Monitoring**: Check `performance_monitoring` settings
|
||||
|
||||
#### MCP Server Failures
|
||||
- **Symptoms**: Routing failures, fallback activation
|
||||
- **Solutions**: Verify MCP server availability, check fallback strategies
|
||||
- **Configuration**: Review `mcp_server_integration` settings
|
||||
|
||||
#### Learning System Issues
|
||||
- **Symptoms**: No adaptation observed, effectiveness not improving
|
||||
- **Solutions**: Check learning data retention, verify effectiveness tracking
|
||||
- **Debug**: Enable verbose learning logging
|
||||
|
||||
#### Memory Usage Issues
|
||||
- **Symptoms**: High memory consumption, cache bloat
|
||||
- **Solutions**: Reduce cache retention periods, enable automatic cleanup
|
||||
- **Monitoring**: Review cache configuration and usage patterns
|
||||
|
||||
### Configuration Validation
|
||||
|
||||
The framework validates configuration on startup:
|
||||
- **Schema Validation**: Ensures proper JSON structure
|
||||
- **Value Validation**: Checks ranges and dependencies
|
||||
- **Integration Validation**: Verifies hook and MCP server consistency
|
||||
- **Security Validation**: Ensures security settings are appropriate
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Hook Implementation**: See individual hook documentation in `/docs/Hooks/`
|
||||
- **MCP Integration**: Reference MCP server documentation for specific server configurations
|
||||
- **Mode Documentation**: Review mode-specific documentation for behavioral patterns
|
||||
- **Performance Monitoring**: See performance configuration documentation for optimization strategies
|
||||
|
||||
## Version History
|
||||
|
||||
- **v1.0.0**: Initial SuperClaude-Lite configuration with all 7 hooks and 6 MCP servers
|
||||
- Full hook lifecycle support with learning and performance monitoring
|
||||
- Comprehensive quality gates implementation
|
||||
- Mode integration with behavioral pattern support
|
||||
@@ -1,357 +0,0 @@
|
||||
# User Experience Configuration (`user_experience.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `user_experience.yaml` file configures UX optimization, project detection, and user-centric intelligence patterns for the SuperClaude-Lite framework. This configuration enables intelligent user experience through smart defaults, proactive assistance, and adaptive interfaces.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
This configuration provides:
|
||||
- **Project Detection**: Automatically detect project types and optimize accordingly
|
||||
- **User Preference Learning**: Learn and adapt to user behavior patterns
|
||||
- **Proactive Assistance**: Provide intelligent suggestions and contextual help
|
||||
- **Smart Defaults**: Generate context-aware default configurations
|
||||
- **Error Recovery**: Intelligent error handling with user-focused recovery
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### 1. Project Type Detection
|
||||
|
||||
#### Frontend Frameworks
|
||||
```yaml
|
||||
react_project:
|
||||
file_indicators:
|
||||
- "package.json"
|
||||
- "*.tsx"
|
||||
- "*.jsx"
|
||||
- "react" # in package.json dependencies
|
||||
directory_indicators:
|
||||
- "src/components"
|
||||
- "public"
|
||||
- "node_modules"
|
||||
confidence_threshold: 0.8
|
||||
recommendations:
|
||||
mcp_servers: ["magic", "context7", "playwright"]
|
||||
compression_level: "minimal"
|
||||
performance_focus: "ui_responsiveness"
|
||||
```
|
||||
|
||||
**Detection Logic**: File and directory pattern matching with confidence scoring
|
||||
**Recommendations**: Automatic MCP server selection and optimization settings
|
||||
**Thresholds**: Confidence levels for reliable project type detection
|
||||
|
||||
#### Backend Frameworks
|
||||
```yaml
|
||||
python_project:
|
||||
file_indicators:
|
||||
- "requirements.txt"
|
||||
- "pyproject.toml"
|
||||
- "*.py"
|
||||
recommendations:
|
||||
mcp_servers: ["serena", "sequential", "context7"]
|
||||
compression_level: "standard"
|
||||
validation_level: "enhanced"
|
||||
```
|
||||
|
||||
**Language Detection**: Python, Node.js, and other backend frameworks
|
||||
**Tool Selection**: Appropriate MCP servers for backend development
|
||||
**Configuration**: Optimized settings for backend workflows
|
||||
|
||||
### 2. User Preference Intelligence
|
||||
|
||||
#### Preference Learning
|
||||
```yaml
|
||||
preference_learning:
|
||||
interaction_patterns:
|
||||
command_preferences:
|
||||
track_command_usage: true
|
||||
track_flag_preferences: true
|
||||
track_workflow_patterns: true
|
||||
learning_window: 100 # operations
|
||||
```
|
||||
|
||||
**Pattern Tracking**: Monitor user command and workflow preferences
|
||||
**Learning Window**: Number of operations used for preference analysis
|
||||
**Behavioral Analysis**: Speed vs quality preferences, detail level preferences
|
||||
|
||||
#### Adaptation Strategies
|
||||
```yaml
|
||||
adaptation_strategies:
|
||||
speed_focused_user:
|
||||
optimizations: ["aggressive_caching", "parallel_execution", "reduced_analysis"]
|
||||
ui_changes: ["shorter_responses", "quick_suggestions", "minimal_explanations"]
|
||||
quality_focused_user:
|
||||
optimizations: ["comprehensive_analysis", "detailed_validation", "thorough_documentation"]
|
||||
ui_changes: ["detailed_responses", "comprehensive_suggestions", "full_explanations"]
|
||||
```
|
||||
|
||||
**User Profiles**: Speed-focused, quality-focused, and efficiency-focused adaptations
|
||||
**Optimization**: Performance tuning based on user preferences
|
||||
**Interface Adaptation**: UI changes to match user preferences
|
||||
|
||||
### 3. Proactive User Assistance
|
||||
|
||||
#### Intelligent Suggestions
|
||||
```yaml
|
||||
optimization_suggestions:
|
||||
- trigger: {repeated_operations: ">5", same_pattern: true}
|
||||
suggestion: "Consider creating a script or alias for this repeated operation"
|
||||
confidence: 0.8
|
||||
category: "workflow_optimization"
|
||||
- trigger: {performance_issues: "detected", duration: ">3_sessions"}
|
||||
suggestion: "Performance optimization recommendations available"
|
||||
action: "show_performance_guide"
|
||||
confidence: 0.9
|
||||
```
|
||||
|
||||
**Pattern Recognition**: Detect repeated operations and inefficiencies
|
||||
**Contextual Suggestions**: Provide relevant optimization recommendations
|
||||
**Confidence Scoring**: Reliability ratings for suggestions
|
||||
|
||||
#### Contextual Help
|
||||
```yaml
|
||||
help_triggers:
|
||||
- context: {new_user: true, session_count: "<5"}
|
||||
help_type: "onboarding_guidance"
|
||||
content: "Getting started tips and best practices"
|
||||
- context: {error_rate: ">10%", recent_errors: ">3"}
|
||||
help_type: "troubleshooting_assistance"
|
||||
content: "Common error solutions and debugging tips"
|
||||
```
|
||||
|
||||
**Trigger-Based Help**: Automatic help based on user context and behavior
|
||||
**Adaptive Content**: Different help types for different situations
|
||||
**User Journey**: Onboarding, troubleshooting, and advanced guidance
|
||||
|
||||
### 4. Smart Defaults Intelligence
|
||||
|
||||
#### Project-Based Defaults
|
||||
```yaml
|
||||
project_based_defaults:
|
||||
react_project:
|
||||
default_mcp_servers: ["magic", "context7"]
|
||||
default_compression: "minimal"
|
||||
default_analysis_depth: "ui_focused"
|
||||
default_validation: "component_focused"
|
||||
python_project:
|
||||
default_mcp_servers: ["serena", "sequential"]
|
||||
default_compression: "standard"
|
||||
default_analysis_depth: "comprehensive"
|
||||
default_validation: "enhanced"
|
||||
```
|
||||
|
||||
**Context-Aware Configuration**: Automatic configuration based on detected project type
|
||||
**Framework Optimization**: Defaults optimized for specific development frameworks
|
||||
**Workflow Enhancement**: Pre-configured settings for common development patterns
|
||||
|
||||
#### Dynamic Configuration
|
||||
```yaml
|
||||
configuration_adaptation:
|
||||
performance_based:
|
||||
- condition: {system_performance: "high"}
|
||||
adjustments: {analysis_depth: "comprehensive", features: "all_enabled"}
|
||||
- condition: {system_performance: "low"}
|
||||
adjustments: {analysis_depth: "essential", features: "performance_focused"}
|
||||
```
|
||||
|
||||
**Performance Adaptation**: Adjust configuration based on system performance
|
||||
**Expertise-Based**: Different defaults for beginner vs expert users
|
||||
**Resource Management**: Optimize based on available system resources
|
||||
|
||||
### 5. Error Recovery Intelligence
|
||||
|
||||
#### Error Classification
|
||||
```yaml
|
||||
error_classification:
|
||||
user_errors:
|
||||
- type: "syntax_error"
|
||||
recovery: "suggest_correction"
|
||||
user_guidance: "detailed"
|
||||
- type: "configuration_error"
|
||||
recovery: "auto_fix_with_approval"
|
||||
user_guidance: "educational"
|
||||
system_errors:
|
||||
- type: "performance_degradation"
|
||||
recovery: "automatic_optimization"
|
||||
user_notification: "informational"
|
||||
```
|
||||
|
||||
**Error Types**: Classification of user vs system errors
|
||||
**Recovery Strategies**: Appropriate recovery actions for each error type
|
||||
**User Guidance**: Educational vs informational responses
|
||||
|
||||
#### Recovery Learning
|
||||
```yaml
|
||||
recovery_effectiveness:
|
||||
track_recovery_success: true
|
||||
learn_recovery_patterns: true
|
||||
improve_recovery_strategies: true
|
||||
user_recovery_preferences:
|
||||
learn_preferred_recovery: true
|
||||
adapt_recovery_approach: true
|
||||
personalize_error_handling: true
|
||||
```
|
||||
|
||||
**Pattern Learning**: Learn from successful error recovery patterns
|
||||
**Personalization**: Adapt error handling to user preferences
|
||||
**Continuous Improvement**: Improve recovery strategies over time
|
||||
|
||||
### 6. User Expertise Detection
|
||||
|
||||
#### Behavioral Indicators
|
||||
```yaml
|
||||
expertise_indicators:
|
||||
command_proficiency:
|
||||
indicators: ["advanced_flags", "complex_operations", "custom_configurations"]
|
||||
weight: 0.4
|
||||
error_recovery_ability:
|
||||
indicators: ["self_correction", "minimal_help_needed", "independent_problem_solving"]
|
||||
weight: 0.3
|
||||
workflow_sophistication:
|
||||
indicators: ["efficient_workflows", "automation_usage", "advanced_patterns"]
|
||||
weight: 0.3
|
||||
```
|
||||
|
||||
**Multi-Factor Detection**: Command proficiency, error recovery, workflow sophistication
|
||||
**Weighted Scoring**: Balanced assessment of different expertise indicators
|
||||
**Dynamic Assessment**: Continuous evaluation of user expertise level
|
||||
|
||||
#### Expertise Adaptation
|
||||
```yaml
|
||||
beginner_adaptations:
|
||||
interface: ["detailed_explanations", "step_by_step_guidance", "comprehensive_warnings"]
|
||||
defaults: ["safe_options", "guided_workflows", "educational_mode"]
|
||||
expert_adaptations:
|
||||
interface: ["minimal_explanations", "advanced_options", "efficiency_focused"]
|
||||
defaults: ["maximum_automation", "performance_optimization", "minimal_interruptions"]
|
||||
```
|
||||
|
||||
**Progressive Interface**: Interface complexity matches user expertise
|
||||
**Default Optimization**: Appropriate defaults for each expertise level
|
||||
**Learning Curve**: Smooth progression from beginner to expert experience
|
||||
|
||||
### 7. Satisfaction Intelligence
|
||||
|
||||
#### Satisfaction Metrics
|
||||
```yaml
|
||||
satisfaction_metrics:
|
||||
task_completion_rate:
|
||||
weight: 0.3
|
||||
target_threshold: 0.85
|
||||
error_resolution_speed:
|
||||
weight: 0.25
|
||||
target_threshold: "fast"
|
||||
feature_adoption_rate:
|
||||
weight: 0.2
|
||||
target_threshold: 0.6
|
||||
```
|
||||
|
||||
**Multi-Dimensional Tracking**: Completion rates, error resolution, feature adoption
|
||||
**Weighted Scoring**: Balanced assessment of satisfaction factors
|
||||
**Target Thresholds**: Performance targets for satisfaction metrics
|
||||
|
||||
#### Optimization Strategies
|
||||
```yaml
|
||||
optimization_strategies:
|
||||
low_satisfaction_triggers:
|
||||
- trigger: {completion_rate: "<0.7"}
|
||||
action: "simplify_workflows"
|
||||
priority: "high"
|
||||
- trigger: {error_rate: ">15%"}
|
||||
action: "improve_error_prevention"
|
||||
priority: "critical"
|
||||
```
|
||||
|
||||
**Trigger-Based Optimization**: Automatic improvements based on satisfaction metrics
|
||||
**Priority Management**: Critical vs high vs medium priority improvements
|
||||
**Continuous Optimization**: Ongoing satisfaction improvement processes
|
||||
|
||||
### 8. Personalization Engine
|
||||
|
||||
#### Interface Personalization
|
||||
```yaml
|
||||
interface_personalization:
|
||||
layout_preferences:
|
||||
learn_preferred_layouts: true
|
||||
adapt_information_density: true
|
||||
customize_interaction_patterns: true
|
||||
content_personalization:
|
||||
learn_content_preferences: true
|
||||
adapt_explanation_depth: true
|
||||
customize_suggestion_types: true
|
||||
```
|
||||
|
||||
**Adaptive Interface**: Layout and content adapted to user preferences
|
||||
**Information Density**: Adjust detail level based on user preferences
|
||||
**Interaction Patterns**: Customize based on user behavior patterns
|
||||
|
||||
#### Workflow Optimization
|
||||
```yaml
|
||||
personal_workflow_learning:
|
||||
common_task_patterns: true
|
||||
workflow_efficiency_analysis: true
|
||||
personalized_shortcuts: true
|
||||
workflow_recommendations:
|
||||
suggest_workflow_improvements: true
|
||||
recommend_automation_opportunities: true
|
||||
provide_efficiency_insights: true
|
||||
```
|
||||
|
||||
**Pattern Learning**: Learn individual user workflow patterns
|
||||
**Efficiency Analysis**: Identify optimization opportunities
|
||||
**Personalized Recommendations**: Workflow improvements tailored to user
|
||||
|
||||
## Configuration Guidelines
|
||||
|
||||
### Project Detection Tuning
|
||||
- **Confidence Thresholds**: Higher thresholds reduce false positives
|
||||
- **File Indicators**: Add project-specific files for better detection
|
||||
- **Directory Structure**: Include common directory patterns
|
||||
- **Recommendations**: Align MCP server selection with project needs
|
||||
|
||||
### Preference Learning
|
||||
- **Learning Window**: Adjust based on user activity level
|
||||
- **Adaptation Speed**: Balance responsiveness with stability
|
||||
- **Pattern Recognition**: Include relevant behavioral indicators
|
||||
- **Privacy**: Ensure user preference data remains private
|
||||
|
||||
### Proactive Assistance
|
||||
- **Suggestion Timing**: Avoid interrupting user flow
|
||||
- **Relevance**: Ensure suggestions are contextually appropriate
|
||||
- **Frequency**: Balance helpfulness with intrusiveness
|
||||
- **User Control**: Allow users to adjust assistance level
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Hook Integration
|
||||
- **Session Start**: Project detection and user preference loading
|
||||
- **Pre-Tool Use**: Context-aware defaults and proactive suggestions
|
||||
- **Post-Tool Use**: Satisfaction tracking and pattern learning
|
||||
|
||||
### MCP Server Coordination
|
||||
- **Server Selection**: Project-based and preference-based routing
|
||||
- **Configuration**: Context-aware MCP server configuration
|
||||
- **Performance**: User preference-based optimization
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Project Detection Issues
|
||||
- **False Positives**: Increase confidence thresholds
|
||||
- **False Negatives**: Add more file/directory indicators
|
||||
- **Conflicting Types**: Review indicator specificity
|
||||
|
||||
### Preference Learning Problems
|
||||
- **Slow Adaptation**: Reduce learning window size
|
||||
- **Wrong Preferences**: Review behavioral indicators
|
||||
- **Privacy Concerns**: Ensure data anonymization
|
||||
|
||||
### Satisfaction Issues
|
||||
- **Low Completion Rates**: Review workflow complexity
|
||||
- **High Error Rates**: Improve error prevention
|
||||
- **Poor Feature Adoption**: Enhance feature discoverability
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Project Detection**: Framework project type detection patterns
|
||||
- **User Analytics**: User behavior analysis and learning systems
|
||||
- **Error Recovery**: Comprehensive error handling and recovery strategies
|
||||
@@ -1,704 +0,0 @@
|
||||
# Validation Configuration (`validation.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `validation.yaml` file defines comprehensive quality validation rules and standards for the SuperClaude-Lite framework. This configuration implements RULES.md and PRINCIPLES.md enforcement through automated validation cycles, quality standards, and continuous improvement mechanisms.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
The validation configuration serves as:
|
||||
- **Rules Enforcement Engine**: Implements SuperClaude RULES.md validation with automatic detection and correction
|
||||
- **Principles Alignment Validator**: Ensures adherence to PRINCIPLES.md through systematic validation
|
||||
- **Quality Standards Framework**: Establishes minimum quality thresholds across code, security, performance, and maintainability
|
||||
- **Validation Workflow Orchestrator**: Manages pre-validation, post-validation, and continuous validation cycles
|
||||
- **Learning Integration System**: Incorporates validation results into framework learning and adaptation
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
### 1. Core SuperClaude Rules Validation (`rules_validation`)
|
||||
|
||||
#### File Operations Validation
|
||||
```yaml
|
||||
file_operations:
|
||||
read_before_write:
|
||||
enabled: true
|
||||
severity: "error"
|
||||
message: "RULES violation: No Read operation detected before Write/Edit"
|
||||
check_recent_tools: 3
|
||||
exceptions: ["new_file_creation"]
|
||||
```
|
||||
|
||||
**Purpose**: Enforces mandatory Read operations before Write/Edit operations
|
||||
**Severity**: Error level prevents execution without compliance
|
||||
**Recent Tools Check**: Examines last 3 tool operations for Read operations
|
||||
**Exceptions**: Allows new file creation without prior Read requirement
|
||||
|
||||
```yaml
|
||||
absolute_paths_only:
|
||||
enabled: true
|
||||
severity: "error"
|
||||
message: "RULES violation: Relative path used"
|
||||
path_parameters: ["file_path", "path", "directory", "output_path"]
|
||||
allowed_prefixes: ["http://", "https://", "/"]
|
||||
```
|
||||
|
||||
**Purpose**: Prevents security issues through relative path usage
|
||||
**Parameter Validation**: Checks all path-related parameters
|
||||
**Allowed Prefixes**: Permits absolute paths and URLs only
|
||||
|
||||
```yaml
|
||||
validate_before_execution:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "RULES recommendation: High-risk operation should include validation"
|
||||
high_risk_operations: ["delete", "refactor", "deploy", "migrate"]
|
||||
complexity_threshold: 0.7
|
||||
```
|
||||
|
||||
**Purpose**: Recommends validation before high-risk operations
|
||||
**Risk Assessment**: Identifies operations requiring additional validation
|
||||
**Complexity Consideration**: Higher complexity operations require validation
|
||||
|
||||
#### Security Requirements Validation
|
||||
```yaml
|
||||
security_requirements:
|
||||
input_validation:
|
||||
enabled: true
|
||||
severity: "error"
|
||||
message: "RULES violation: User input handling without validation"
|
||||
check_patterns: ["user_input", "external_data", "api_input"]
|
||||
|
||||
no_hardcoded_secrets:
|
||||
enabled: true
|
||||
severity: "critical"
|
||||
message: "RULES violation: Hardcoded sensitive information detected"
|
||||
patterns: ["password", "api_key", "secret", "token"]
|
||||
|
||||
production_safety:
|
||||
enabled: true
|
||||
severity: "error"
|
||||
message: "RULES violation: Unsafe operation in production context"
|
||||
production_indicators: ["is_production", "prod_env", "production"]
|
||||
```
|
||||
|
||||
**Input Validation**: Ensures user input is properly validated
|
||||
**Secret Detection**: Prevents hardcoded sensitive information
|
||||
**Production Safety**: Protects against unsafe production operations
|
||||
|
||||
### 2. SuperClaude Principles Validation (`principles_validation`)
|
||||
|
||||
#### Evidence Over Assumptions
|
||||
```yaml
|
||||
evidence_over_assumptions:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "PRINCIPLES: Provide evidence to support assumptions"
|
||||
check_for_assumptions: true
|
||||
require_evidence: true
|
||||
confidence_threshold: 0.7
|
||||
```
|
||||
|
||||
**Purpose**: Enforces evidence-based reasoning and decision-making
|
||||
**Assumption Detection**: Identifies assumptions requiring evidence support
|
||||
**Confidence Threshold**: 70% confidence required for assumption validation
|
||||
|
||||
#### Code Over Documentation
|
||||
```yaml
|
||||
code_over_documentation:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "PRINCIPLES: Documentation should follow working code, not precede it"
|
||||
documentation_operations: ["document", "readme", "guide"]
|
||||
require_working_code: true
|
||||
```
|
||||
|
||||
**Purpose**: Ensures documentation follows working code implementation
|
||||
**Documentation Operations**: Identifies documentation-focused operations
|
||||
**Working Code Requirement**: Validates existence of working code before documentation
|
||||
|
||||
#### Efficiency Over Verbosity
|
||||
```yaml
|
||||
efficiency_over_verbosity:
|
||||
enabled: true
|
||||
severity: "suggestion"
|
||||
message: "PRINCIPLES: Consider token efficiency techniques for large outputs"
|
||||
output_size_threshold: 5000
|
||||
verbosity_indicators: ["repetitive_content", "unnecessary_detail"]
|
||||
```
|
||||
|
||||
**Purpose**: Promotes token efficiency and concise communication
|
||||
**Size Threshold**: 5000 tokens triggers efficiency recommendations
|
||||
**Verbosity Detection**: Identifies repetitive or unnecessarily detailed content
|
||||
|
||||
#### Test-Driven Development
|
||||
```yaml
|
||||
test_driven_development:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "PRINCIPLES: Logic changes should include tests"
|
||||
logic_operations: ["write", "edit", "generate", "implement"]
|
||||
test_file_patterns: ["*test*", "*spec*", "test_*", "*_test.*"]
|
||||
```
|
||||
|
||||
**Purpose**: Promotes test-driven development practices
|
||||
**Logic Operations**: Identifies operations requiring test coverage
|
||||
**Test Pattern Recognition**: Recognizes various test file naming conventions
|
||||
|
||||
#### Single Responsibility Principle
|
||||
```yaml
|
||||
single_responsibility:
|
||||
enabled: true
|
||||
severity: "suggestion"
|
||||
message: "PRINCIPLES: Functions/classes should have single responsibility"
|
||||
complexity_indicators: ["multiple_purposes", "large_function", "many_parameters"]
|
||||
```
|
||||
|
||||
**Purpose**: Enforces single responsibility principle in code design
|
||||
**Complexity Detection**: Identifies functions/classes violating single responsibility
|
||||
|
||||
#### Error Handling Requirement
|
||||
```yaml
|
||||
error_handling_required:
|
||||
enabled: true
|
||||
severity: "warning"
|
||||
message: "PRINCIPLES: Error handling not implemented"
|
||||
critical_operations: ["write", "edit", "deploy", "api_calls"]
|
||||
```
|
||||
|
||||
**Purpose**: Ensures proper error handling in critical operations
|
||||
**Critical Operations**: Identifies operations requiring error handling
|
||||
|
||||
### 3. Quality Standards (`quality_standards`)
|
||||
|
||||
#### Code Quality Standards
|
||||
```yaml
|
||||
code_quality:
|
||||
minimum_score: 0.7
|
||||
factors:
|
||||
- syntax_correctness
|
||||
- logical_consistency
|
||||
- error_handling_presence
|
||||
- documentation_adequacy
|
||||
- test_coverage
|
||||
```
|
||||
|
||||
**Minimum Score**: 70% quality score required for code acceptance
|
||||
**Multi-Factor Assessment**: Comprehensive quality evaluation across multiple dimensions
|
||||
|
||||
#### Security Compliance Standards
|
||||
```yaml
|
||||
security_compliance:
|
||||
minimum_score: 0.8
|
||||
checks:
|
||||
- input_validation
|
||||
- output_sanitization
|
||||
- authentication_checks
|
||||
- authorization_verification
|
||||
- secure_communication
|
||||
```
|
||||
|
||||
**Security Score**: 80% security compliance required (higher than code quality)
|
||||
**Comprehensive Security**: Covers all major security aspects
|
||||
|
||||
#### Performance Standards
|
||||
```yaml
|
||||
performance_standards:
|
||||
response_time_threshold_ms: 2000
|
||||
resource_efficiency_min: 0.6
|
||||
optimization_indicators:
|
||||
- algorithm_efficiency
|
||||
- memory_usage
|
||||
- processing_speed
|
||||
```
|
||||
|
||||
**Response Time**: 2-second maximum response time threshold
|
||||
**Resource Efficiency**: 60% minimum resource efficiency requirement
|
||||
**Optimization Focus**: Algorithm efficiency, memory usage, and processing speed
|
||||
|
||||
#### Maintainability Standards
|
||||
```yaml
|
||||
maintainability:
|
||||
minimum_score: 0.6
|
||||
factors:
|
||||
- code_clarity
|
||||
- documentation_quality
|
||||
- modular_design
|
||||
- consistent_style
|
||||
```
|
||||
|
||||
**Maintainability Score**: 60% minimum maintainability score
|
||||
**Sustainability Focus**: Emphasizes long-term code maintainability
|
||||
|
||||
### 4. Validation Workflow (`validation_workflow`)
|
||||
|
||||
#### Pre-Validation
|
||||
```yaml
|
||||
pre_validation:
|
||||
enabled: true
|
||||
quick_checks:
|
||||
- syntax_validation
|
||||
- basic_security_scan
|
||||
- rule_compliance_check
|
||||
```
|
||||
|
||||
**Purpose**: Fast validation before operation execution
|
||||
**Quick Checks**: Essential validations that execute rapidly
|
||||
**Blocking**: Can prevent operation execution based on results
|
||||
|
||||
#### Post-Validation
|
||||
```yaml
|
||||
post_validation:
|
||||
enabled: true
|
||||
comprehensive_checks:
|
||||
- quality_assessment
|
||||
- principle_alignment
|
||||
- effectiveness_measurement
|
||||
- learning_opportunity_detection
|
||||
```
|
||||
|
||||
**Purpose**: Comprehensive validation after operation completion
|
||||
**Thorough Analysis**: Complete quality and principle assessment
|
||||
**Learning Integration**: Identifies opportunities for framework learning
|
||||
|
||||
#### Continuous Validation
|
||||
```yaml
|
||||
continuous_validation:
|
||||
enabled: true
|
||||
real_time_monitoring:
|
||||
- pattern_violation_detection
|
||||
- quality_degradation_alerts
|
||||
- performance_regression_detection
|
||||
```
|
||||
|
||||
**Purpose**: Ongoing validation throughout operation lifecycle
|
||||
**Real-Time Monitoring**: Immediate detection of issues as they arise
|
||||
**Proactive Alerts**: Early warning system for quality issues
|
||||
|
||||
### 5. Error Classification and Handling (`error_classification`)
|
||||
|
||||
#### Critical Errors
|
||||
```yaml
|
||||
critical_errors:
|
||||
severity_level: "critical"
|
||||
block_execution: true
|
||||
examples:
|
||||
- security_vulnerabilities
|
||||
- data_corruption_risk
|
||||
- system_instability
|
||||
```
|
||||
|
||||
**Execution Blocking**: Critical errors prevent operation execution
|
||||
**System Protection**: Prevents system-level damage or security breaches
|
||||
|
||||
#### Standard Errors
|
||||
```yaml
|
||||
standard_errors:
|
||||
severity_level: "error"
|
||||
block_execution: false
|
||||
require_acknowledgment: true
|
||||
examples:
|
||||
- rule_violations
|
||||
- quality_failures
|
||||
- incomplete_implementation
|
||||
```
|
||||
|
||||
**Acknowledgment Required**: User must acknowledge errors before proceeding
|
||||
**Non-Blocking**: Allows execution with user awareness of issues
|
||||
|
||||
#### Warnings and Suggestions
|
||||
```yaml
|
||||
warnings:
|
||||
severity_level: "warning"
|
||||
block_execution: false
|
||||
examples:
|
||||
- principle_deviations
|
||||
- optimization_opportunities
|
||||
- best_practice_suggestions
|
||||
|
||||
suggestions:
|
||||
severity_level: "suggestion"
|
||||
informational: true
|
||||
examples:
|
||||
- code_improvements
|
||||
- efficiency_enhancements
|
||||
- learning_recommendations
|
||||
```
|
||||
|
||||
**Non-Blocking**: Warnings and suggestions don't prevent execution
|
||||
**Educational Value**: Provides learning opportunities and improvement suggestions
|
||||
|
||||
### 6. Effectiveness Measurement (`effectiveness_measurement`)
|
||||
|
||||
#### Success Indicators
|
||||
```yaml
|
||||
success_indicators:
|
||||
task_completion: "weight: 0.4"
|
||||
quality_achievement: "weight: 0.3"
|
||||
user_satisfaction: "weight: 0.2"
|
||||
learning_value: "weight: 0.1"
|
||||
```
|
||||
|
||||
**Weighted Assessment**: Balanced evaluation across multiple success dimensions
|
||||
**Task Completion**: Highest weight on successful task completion
|
||||
**Quality Focus**: Significant weight on quality achievement
|
||||
**User Experience**: Important consideration for user satisfaction
|
||||
**Learning Value**: Framework learning and improvement value
|
||||
|
||||
#### Performance Metrics
|
||||
```yaml
|
||||
performance_metrics:
|
||||
execution_time: "target: <2000ms"
|
||||
resource_efficiency: "target: >0.6"
|
||||
error_rate: "target: <0.1"
|
||||
validation_accuracy: "target: >0.9"
|
||||
```
|
||||
|
||||
**Performance Targets**: Specific measurable targets for performance assessment
|
||||
**Error Rate**: Low error rate target for system reliability
|
||||
**Validation Accuracy**: High accuracy target for validation effectiveness
|
||||
|
||||
#### Quality Metrics
|
||||
```yaml
|
||||
quality_metrics:
|
||||
code_quality_score: "target: >0.7"
|
||||
security_compliance: "target: >0.8"
|
||||
principle_alignment: "target: >0.7"
|
||||
rule_compliance: "target: >0.9"
|
||||
```
|
||||
|
||||
**Quality Targets**: Specific targets for different quality dimensions
|
||||
**High Compliance**: Very high rule compliance target (90%)
|
||||
**Strong Security**: High security compliance target (80%)
|
||||
|
||||
### 7. Learning Integration (`learning_integration`)
|
||||
|
||||
#### Pattern Detection
|
||||
```yaml
|
||||
pattern_detection:
|
||||
success_patterns: true
|
||||
failure_patterns: true
|
||||
optimization_patterns: true
|
||||
user_preference_patterns: true
|
||||
```
|
||||
|
||||
**Comprehensive Pattern Learning**: Learns from all types of patterns
|
||||
**Success and Failure**: Learns from both positive and negative outcomes
|
||||
**User Preferences**: Adapts to individual user patterns and preferences
|
||||
|
||||
#### Effectiveness Feedback
|
||||
```yaml
|
||||
effectiveness_feedback:
|
||||
real_time_collection: true
|
||||
user_satisfaction_tracking: true
|
||||
quality_trend_analysis: true
|
||||
adaptation_triggers: true
|
||||
```
|
||||
|
||||
**Real-Time Learning**: Immediate learning from validation outcomes
|
||||
**User Satisfaction**: Incorporates user satisfaction into learning
|
||||
**Trend Analysis**: Identifies quality trends over time
|
||||
**Adaptive Triggers**: Triggers adaptations based on learning insights
|
||||
|
||||
#### Continuous Improvement
|
||||
```yaml
|
||||
continuous_improvement:
|
||||
threshold_adjustment: true
|
||||
rule_refinement: true
|
||||
principle_enhancement: true
|
||||
validation_optimization: true
|
||||
```
|
||||
|
||||
**Dynamic Optimization**: Continuously improves validation effectiveness
|
||||
**Rule Evolution**: Refines rules based on effectiveness data
|
||||
**Validation Enhancement**: Optimizes validation processes over time
|
||||
|
||||
### 8. Context-Aware Validation (`context_awareness`)
|
||||
|
||||
#### Project Type Adaptations
|
||||
```yaml
|
||||
project_type_adaptations:
|
||||
frontend_projects:
|
||||
additional_checks: ["accessibility", "responsive_design", "browser_compatibility"]
|
||||
|
||||
backend_projects:
|
||||
additional_checks: ["api_security", "data_validation", "performance_optimization"]
|
||||
|
||||
full_stack_projects:
|
||||
additional_checks: ["integration_testing", "end_to_end_validation", "deployment_safety"]
|
||||
```
|
||||
|
||||
**Project-Specific Validation**: Adapts validation to project characteristics
|
||||
**Domain-Specific Checks**: Includes relevant checks for each project type
|
||||
**Comprehensive Coverage**: Ensures all relevant aspects are validated
|
||||
|
||||
#### User Expertise Adjustments
|
||||
```yaml
|
||||
user_expertise_adjustments:
|
||||
beginner:
|
||||
validation_verbosity: "high"
|
||||
educational_suggestions: true
|
||||
step_by_step_guidance: true
|
||||
|
||||
intermediate:
|
||||
validation_verbosity: "medium"
|
||||
best_practice_suggestions: true
|
||||
optimization_recommendations: true
|
||||
|
||||
expert:
|
||||
validation_verbosity: "low"
|
||||
advanced_optimization_suggestions: true
|
||||
architectural_guidance: true
|
||||
```
|
||||
|
||||
**Expertise-Aware Validation**: Adapts validation approach to user expertise level
|
||||
**Educational Value**: Provides appropriate learning opportunities
|
||||
**Efficiency Optimization**: Reduces noise for expert users while maintaining quality
|
||||
|
||||
### 9. Performance Configuration (`performance_configuration`)
|
||||
|
||||
#### Validation Targets
|
||||
```yaml
|
||||
validation_targets:
|
||||
processing_time_ms: 100
|
||||
memory_usage_mb: 50
|
||||
cpu_utilization_percent: 30
|
||||
```
|
||||
|
||||
**Performance Limits**: Ensures validation doesn't impact system performance
|
||||
**Resource Constraints**: Reasonable resource usage for validation processes
|
||||
|
||||
#### Optimization Strategies
|
||||
```yaml
|
||||
optimization_strategies:
|
||||
parallel_validation: true
|
||||
cached_results: true
|
||||
incremental_validation: true
|
||||
smart_rule_selection: true
|
||||
```
|
||||
|
||||
**Performance Optimization**: Multiple strategies to optimize validation speed
|
||||
**Intelligent Caching**: Caches validation results for repeated operations
|
||||
**Smart Selection**: Applies only relevant rules based on context
|
||||
|
||||
#### Resource Management
|
||||
```yaml
|
||||
resource_management:
|
||||
max_validation_time_ms: 500
|
||||
memory_limit_mb: 100
|
||||
cpu_limit_percent: 50
|
||||
fallback_on_resource_limit: true
|
||||
```
|
||||
|
||||
**Resource Protection**: Prevents validation from consuming excessive resources
|
||||
**Graceful Fallback**: Falls back to basic validation if resource limits exceeded
|
||||
|
||||
### 10. Integration Points (`integration_points`)
|
||||
|
||||
#### MCP Server Integration
|
||||
```yaml
|
||||
mcp_servers:
|
||||
serena: "semantic_validation_support"
|
||||
morphllm: "edit_validation_coordination"
|
||||
sequential: "complex_validation_analysis"
|
||||
```
|
||||
|
||||
**Server-Specific Integration**: Leverages MCP server capabilities for validation
|
||||
**Semantic Validation**: Uses Serena for semantic analysis validation
|
||||
**Edit Coordination**: Coordinates with Morphllm for edit validation
|
||||
|
||||
#### Learning Engine Integration
|
||||
```yaml
|
||||
learning_engine:
|
||||
effectiveness_tracking: true
|
||||
pattern_learning: true
|
||||
adaptation_feedback: true
|
||||
```
|
||||
|
||||
**Learning Coordination**: Integrates validation results with learning system
|
||||
**Pattern Learning**: Learns patterns from validation outcomes
|
||||
**Adaptive Feedback**: Provides feedback for learning adaptation
|
||||
|
||||
#### Other Hook Integration
|
||||
```yaml
|
||||
other_hooks:
|
||||
pre_tool_use: "validation_preparation"
|
||||
session_start: "validation_configuration"
|
||||
stop: "validation_summary_generation"
|
||||
```
|
||||
|
||||
**Hook Coordination**: Integrates validation across hook lifecycle
|
||||
**Preparation**: Prepares validation context before tool use
|
||||
**Summary**: Generates validation summaries at session end
|
||||
|
||||
## Performance Implications
|
||||
|
||||
### 1. Validation Processing Performance
|
||||
|
||||
#### Rule Validation Performance
|
||||
- **File Operation Rules**: 5-20ms per rule validation
|
||||
- **Security Rules**: 10-50ms per security check
|
||||
- **Principle Validation**: 20-100ms per principle assessment
|
||||
- **Total Rule Validation**: 50-200ms for complete rule validation
|
||||
|
||||
#### Quality Assessment Performance
|
||||
- **Code Quality**: 100-500ms for comprehensive quality assessment
|
||||
- **Security Compliance**: 200ms-1s for security analysis
|
||||
- **Performance Analysis**: 150-750ms for performance validation
|
||||
- **Maintainability**: 50-300ms for maintainability assessment
|
||||
|
||||
### 2. Learning Integration Performance
|
||||
|
||||
#### Pattern Learning Impact
|
||||
- **Pattern Detection**: 50-200ms for pattern recognition
|
||||
- **Learning Updates**: 100-500ms for learning data updates
|
||||
- **Adaptation Application**: 200ms-1s for adaptation implementation
|
||||
|
||||
#### Effectiveness Tracking
|
||||
- **Metrics Collection**: 10-50ms per validation operation
|
||||
- **Trend Analysis**: 100-500ms for trend calculation
|
||||
- **User Satisfaction**: 20-100ms for satisfaction tracking
|
||||
|
||||
### 3. Resource Usage
|
||||
|
||||
#### Memory Usage
|
||||
- **Rule Storage**: 100-500KB for validation rules
|
||||
- **Pattern Data**: 500KB-2MB for learned patterns
|
||||
- **Validation State**: 50-200KB during validation execution
|
||||
|
||||
#### CPU Usage
|
||||
- **Validation Processing**: 20-60% CPU during comprehensive validation
|
||||
- **Learning Processing**: 10-40% CPU for pattern learning
|
||||
- **Background Monitoring**: <5% CPU for continuous validation
|
||||
|
||||
## Configuration Best Practices
|
||||
|
||||
### 1. Production Validation Configuration
|
||||
```yaml
|
||||
# Strict validation for production reliability
|
||||
rules_validation:
|
||||
file_operations:
|
||||
read_before_write:
|
||||
severity: "critical" # Stricter enforcement
|
||||
security_requirements:
|
||||
production_safety:
|
||||
enabled: true
|
||||
severity: "critical"
|
||||
|
||||
quality_standards:
|
||||
security_compliance:
|
||||
minimum_score: 0.9 # Higher security requirement
|
||||
```
|
||||
|
||||
### 2. Development Validation Configuration
|
||||
```yaml
|
||||
# Educational and learning-focused validation
|
||||
user_expertise_adjustments:
|
||||
default_level: "beginner"
|
||||
educational_suggestions: true
|
||||
verbose_explanations: true
|
||||
|
||||
learning_integration:
|
||||
continuous_improvement:
|
||||
adaptation_triggers: "aggressive" # More learning
|
||||
```
|
||||
|
||||
### 3. Performance-Optimized Configuration
|
||||
```yaml
|
||||
# Minimal validation for performance-critical environments
|
||||
performance_configuration:
|
||||
optimization_strategies:
|
||||
parallel_validation: true
|
||||
cached_results: true
|
||||
smart_rule_selection: true
|
||||
|
||||
resource_management:
|
||||
max_validation_time_ms: 200 # Stricter time limits
|
||||
```
|
||||
|
||||
### 4. Learning-Optimized Configuration
|
||||
```yaml
|
||||
# Maximum learning and adaptation
|
||||
learning_integration:
|
||||
pattern_detection:
|
||||
detailed_analysis: true
|
||||
cross_session_learning: true
|
||||
|
||||
effectiveness_feedback:
|
||||
real_time_collection: true
|
||||
detailed_metrics: true
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Validation Issues
|
||||
|
||||
#### False Positive Rule Violations
|
||||
- **Symptoms**: Valid operations flagged as rule violations
|
||||
- **Analysis**: Review rule patterns and exception handling
|
||||
- **Solutions**: Refine rule patterns, add appropriate exceptions
|
||||
- **Testing**: Test rules with edge cases and valid scenarios
|
||||
|
||||
#### Performance Impact
|
||||
- **Symptoms**: Validation causing significant delays
|
||||
- **Diagnosis**: Profile validation performance and identify bottlenecks
|
||||
- **Optimization**: Enable caching, parallel processing, smart rule selection
|
||||
- **Monitoring**: Track validation performance metrics continuously
|
||||
|
||||
#### Learning System Issues
|
||||
- **Symptoms**: Validation not improving over time, poor adaptations
|
||||
- **Investigation**: Review learning data collection and pattern recognition
|
||||
- **Enhancement**: Adjust learning parameters, improve pattern detection
|
||||
- **Validation**: Test learning effectiveness with controlled scenarios
|
||||
|
||||
#### Quality Standards Conflicts
|
||||
- **Symptoms**: Conflicting quality requirements or unrealistic standards
|
||||
- **Analysis**: Review quality standard interactions and dependencies
|
||||
- **Resolution**: Adjust standards based on project requirements and constraints
|
||||
- **Balancing**: Balance quality with practical implementation constraints
|
||||
|
||||
### Validation System Optimization
|
||||
|
||||
#### Rule Optimization
|
||||
```yaml
|
||||
# Optimize rule execution for performance
|
||||
rules_validation:
|
||||
smart_rule_selection:
|
||||
context_aware: true
|
||||
performance_optimized: true
|
||||
minimal_redundancy: true
|
||||
```
|
||||
|
||||
#### Quality Standard Tuning
|
||||
```yaml
|
||||
# Adjust quality standards based on project needs
|
||||
quality_standards:
|
||||
adaptive_thresholds: true
|
||||
project_specific_adjustments: true
|
||||
user_expertise_consideration: true
|
||||
```
|
||||
|
||||
#### Learning System Tuning
|
||||
```yaml
|
||||
# Optimize learning for specific environments
|
||||
learning_integration:
|
||||
learning_rate_adjustment: "environment_specific"
|
||||
pattern_recognition_sensitivity: "adaptive"
|
||||
effectiveness_measurement_accuracy: "high"
|
||||
```
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **RULES.md**: Core SuperClaude rules being enforced through validation
|
||||
- **PRINCIPLES.md**: SuperClaude principles being validated for alignment
|
||||
- **Quality Gates**: Integration with 8-step quality validation cycle
|
||||
- **Hook Integration**: Post-tool use hook implementation for validation execution
|
||||
|
||||
## Version History
|
||||
|
||||
- **v1.0.0**: Initial validation configuration
|
||||
- Comprehensive RULES.md enforcement with automatic detection
|
||||
- PRINCIPLES.md alignment validation with evidence-based requirements
|
||||
- Multi-dimensional quality standards (code, security, performance, maintainability)
|
||||
- Context-aware validation with project type and user expertise adaptations
|
||||
- Learning integration with pattern detection and continuous improvement
|
||||
- Performance optimization with parallel processing and intelligent caching
|
||||
@@ -1,75 +0,0 @@
|
||||
# Validation Intelligence Configuration (`validation_intelligence.yaml`)
|
||||
|
||||
## Overview
|
||||
|
||||
The `validation_intelligence.yaml` file configures intelligent validation patterns, adaptive quality gates, and smart validation optimization for the SuperClaude-Lite framework.
|
||||
|
||||
## Purpose and Role
|
||||
|
||||
This configuration provides:
|
||||
- **Intelligent Validation**: Context-aware validation rules and patterns
|
||||
- **Adaptive Quality Gates**: Dynamic quality thresholds based on context
|
||||
- **Validation Learning**: Learn from validation patterns and outcomes
|
||||
- **Smart Optimization**: Optimize validation processes for efficiency and accuracy
|
||||
|
||||
## Key Configuration Areas
|
||||
|
||||
### 1. Intelligent Validation Patterns
|
||||
- **Context-Aware Rules**: Apply different validation rules based on operation context
|
||||
- **Pattern-Based Validation**: Use learned patterns to improve validation accuracy
|
||||
- **Risk Assessment**: Assess validation risk based on operation characteristics
|
||||
- **Adaptive Thresholds**: Adjust validation strictness based on context and history
|
||||
|
||||
### 2. Quality Gate Intelligence
|
||||
- **Dynamic Quality Metrics**: Adjust quality requirements based on operation type
|
||||
- **Multi-Dimensional Quality**: Consider multiple quality factors simultaneously
|
||||
- **Quality Learning**: Learn what quality means in different contexts
|
||||
- **Progressive Quality**: Apply increasingly sophisticated quality checks
|
||||
|
||||
### 3. Validation Optimization
|
||||
- **Efficiency Patterns**: Learn which validations provide the most value
|
||||
- **Validation Caching**: Cache validation results to avoid redundant checks
|
||||
- **Selective Validation**: Apply validation selectively based on risk assessment
|
||||
- **Performance-Quality Balance**: Optimize the trade-off between speed and thoroughness
|
||||
|
||||
### 4. Learning and Adaptation
|
||||
- **Validation Effectiveness**: Track which validations catch real issues
|
||||
- **False Positive Learning**: Reduce false positive validation failures
|
||||
- **Pattern Recognition**: Recognize validation patterns across operations
|
||||
- **Continuous Improvement**: Continuously improve validation accuracy and efficiency
|
||||
|
||||
## Configuration Structure
|
||||
|
||||
The file includes:
|
||||
- Intelligent validation rule definitions
|
||||
- Context-aware quality gate configurations
|
||||
- Learning and adaptation parameters
|
||||
- Optimization strategies and thresholds
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Framework Integration
|
||||
- Works with all hooks that perform validation
|
||||
- Integrates with quality gate systems
|
||||
- Provides input to performance optimization
|
||||
- Coordinates with error handling and recovery
|
||||
|
||||
### Learning Integration
|
||||
- Learns from validation outcomes and user feedback
|
||||
- Adapts to project-specific quality requirements
|
||||
- Improves validation patterns over time
|
||||
- Shares learning with other intelligence systems
|
||||
|
||||
## Usage Guidelines
|
||||
|
||||
This configuration controls the intelligent validation capabilities:
|
||||
- **Validation Depth**: Balance thorough validation with performance needs
|
||||
- **Learning Sensitivity**: Configure how quickly validation patterns adapt
|
||||
- **Quality Standards**: Set appropriate quality thresholds for your use cases
|
||||
- **Optimization Balance**: Balance validation thoroughness with efficiency
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Validation Configuration**: `validation.yaml.md` for basic validation settings
|
||||
- **Intelligence Patterns**: `intelligence_patterns.yaml.md` for core learning patterns
|
||||
- **Quality Gates**: Framework quality gate documentation for validation integration
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,487 +0,0 @@
|
||||
# Post-Tool-Use Hook Documentation
|
||||
|
||||
## Purpose
|
||||
|
||||
The `post_tool_use` hook analyzes tool execution results and provides validation, quality assessment, and learning feedback after every tool execution in Claude Code. It implements validation against SuperClaude principles and records learning events for continuous improvement.
|
||||
|
||||
**Core Implementation**: A 794-line Python implementation that validates tool results against RULES.md and PRINCIPLES.md, measures effectiveness, detects error patterns, and records learning events with a target execution time of <100ms.
|
||||
|
||||
## Execution Context
|
||||
|
||||
The post_tool_use hook runs after every tool execution in Claude Code. According to `settings.json`, it has a 10-second timeout and executes via: `python3 ~/.claude/hooks/post_tool_use.py`
|
||||
|
||||
**Actual Execution Flow:**
|
||||
1. Receives tool execution result from Claude Code via stdin (JSON)
|
||||
2. Initializes PostToolUseHook class with shared module components
|
||||
3. Processes tool result through `process_tool_result()` method
|
||||
4. Validates results against SuperClaude principles and measures effectiveness
|
||||
5. Outputs comprehensive validation report via stdout (JSON)
|
||||
6. Falls back gracefully on errors with basic validation report
|
||||
|
||||
**Input Analysis:**
|
||||
- Extracts execution context (tool name, status, timing, parameters, results, errors)
|
||||
- Analyzes operation outcome (success, performance, quality indicators)
|
||||
- Evaluates quality indicators (code quality, security compliance, performance efficiency)
|
||||
|
||||
**Output Reporting:**
|
||||
- Validation results (quality score, issues, warnings, suggestions)
|
||||
- Effectiveness metrics (overall effectiveness, quality/performance/satisfaction scores)
|
||||
- Learning analysis (patterns detected, success/failure factors, optimization opportunities)
|
||||
- Compliance assessment (rules compliance, principles alignment, SuperClaude score)
|
||||
|
||||
## Performance Target
|
||||
|
||||
**Primary Target: <100ms execution time**
|
||||
|
||||
The hook is designed to provide comprehensive validation while maintaining minimal impact on overall system performance.
|
||||
|
||||
**Performance Breakdown:**
|
||||
- **Initialization**: <20ms (component loading and configuration)
|
||||
- **Context Extraction**: <15ms (analyzing tool results and parameters)
|
||||
- **Validation Processing**: <35ms (RULES.md and PRINCIPLES.md compliance checking)
|
||||
- **Learning Analysis**: <20ms (pattern detection and effectiveness measurement)
|
||||
- **Report Generation**: <10ms (creating comprehensive validation report)
|
||||
|
||||
**Performance Monitoring:**
|
||||
- Real-time execution time tracking with target enforcement
|
||||
- Automatic performance degradation detection and alerts
|
||||
- Resource usage monitoring (memory, CPU utilization)
|
||||
- Fallback mechanisms for performance constraint scenarios
|
||||
|
||||
**Optimization Strategies:**
|
||||
- Parallel validation processing for independent checks
|
||||
- Cached validation results for repeated patterns
|
||||
- Incremental validation for large operations
|
||||
- Smart rule selection based on operation context
|
||||
|
||||
## Validation Levels
|
||||
|
||||
The hook implements four distinct validation levels, each providing increasing depth of analysis:
|
||||
|
||||
### Basic Level
|
||||
**Focus**: Syntax and fundamental correctness
|
||||
- **Syntax Validation**: Ensures generated code is syntactically correct
|
||||
- **Basic Security Scan**: Detects obvious security vulnerabilities
|
||||
- **Rule Compliance Check**: Validates core RULES.md requirements
|
||||
- **Performance Target**: <50ms execution time
|
||||
- **Use Cases**: Simple operations, low-risk contexts, performance-critical scenarios
|
||||
|
||||
### Standard Level (Default)
|
||||
**Focus**: Comprehensive quality and type safety
|
||||
- **All Basic Level checks**
|
||||
- **Type Analysis**: Deep type compatibility checking and inference
|
||||
- **Code Quality Assessment**: Maintainability, readability, and best practices
|
||||
- **Principle Alignment**: Verification against PRINCIPLES.md guidelines
|
||||
- **Performance Target**: <100ms execution time
|
||||
- **Use Cases**: Regular development operations, standard complexity tasks
|
||||
|
||||
### Comprehensive Level
|
||||
**Focus**: Security and performance optimization
|
||||
- **All Standard Level checks**
|
||||
- **Security Assessment**: Vulnerability analysis and threat modeling
|
||||
- **Performance Analysis**: Bottleneck identification and optimization recommendations
|
||||
- **Error Pattern Detection**: Advanced pattern recognition for failure modes
|
||||
- **Learning Integration**: Enhanced effectiveness measurement and adaptation
|
||||
- **Performance Target**: <150ms execution time
|
||||
- **Use Cases**: High-risk operations, production deployments, security-sensitive contexts
|
||||
|
||||
### Production Level
|
||||
**Focus**: Integration and deployment readiness
|
||||
- **All Comprehensive Level checks**
|
||||
- **Integration Testing**: Cross-component compatibility verification
|
||||
- **Deployment Validation**: Production readiness assessment
|
||||
- **Quality Gate Enforcement**: Complete 8-step validation cycle
|
||||
- **Comprehensive Reporting**: Detailed compliance and quality documentation
|
||||
- **Performance Target**: <200ms execution time
|
||||
- **Use Cases**: Production deployments, critical system changes, release preparation
|
||||
|
||||
## RULES.md Compliance
|
||||
|
||||
The hook implements comprehensive enforcement of SuperClaude's core operational rules:
|
||||
|
||||
### File Operation Rules
|
||||
**Read Before Write/Edit Enforcement:**
|
||||
- Validates that Read operations precede Write/Edit operations
|
||||
- Checks recent tool history (last 3 operations) for compliance
|
||||
- Issues errors for violations with clear remediation guidance
|
||||
- Provides exceptions for new file creation scenarios
|
||||
|
||||
**Absolute Path Validation:**
|
||||
- Scans all path parameters (file_path, path, directory, output_path)
|
||||
- Blocks relative path usage with specific violation reporting
|
||||
- Allows approved prefixes (http://, https://, absolute paths)
|
||||
- Prevents path traversal attacks and ensures operation security
|
||||
|
||||
**High-Risk Operation Validation:**
|
||||
- Identifies high-risk operations (delete, refactor, deploy, migrate)
|
||||
- Recommends validation for complex operations (complexity > 0.7)
|
||||
- Provides warnings for operations lacking pre-validation
|
||||
- Tracks validation compliance across operation types
|
||||
|
||||
### Security Requirements
|
||||
**Input Validation Enforcement:**
|
||||
- Detects user input handling patterns without validation
|
||||
- Scans for external data processing vulnerabilities
|
||||
- Validates API input sanitization and error handling
|
||||
- Reports security violations with severity classification
|
||||
|
||||
**Secret Management Validation:**
|
||||
- Scans for hardcoded sensitive information (passwords, API keys, tokens)
|
||||
- Issues critical alerts for secret exposure risks
|
||||
- Validates secure credential handling patterns
|
||||
- Provides guidance for proper secret management
|
||||
|
||||
**Production Safety Checks:**
|
||||
- Identifies production context indicators
|
||||
- Validates safety measures for production operations
|
||||
- Blocks unsafe operations in production environments
|
||||
- Ensures proper rollback and recovery mechanisms
|
||||
|
||||
### Systematic Code Changes
|
||||
**Project-Wide Discovery Validation:**
|
||||
- Ensures comprehensive discovery before systematic changes
|
||||
- Validates search completeness across all file types
|
||||
- Confirms impact assessment documentation
|
||||
- Verifies coordinated change execution planning
|
||||
|
||||
## PRINCIPLES.md Alignment
|
||||
|
||||
The hook validates adherence to SuperClaude's core development principles:
|
||||
|
||||
### Evidence-Based Decision Making
|
||||
**Evidence Over Assumptions:**
|
||||
- Detects assumption-based reasoning without supporting evidence
|
||||
- Requires measurable data for significant decisions
|
||||
- Validates hypothesis testing and empirical verification
|
||||
- Promotes evidence-based development practices
|
||||
|
||||
**Decision Documentation:**
|
||||
- Ensures decision rationale is recorded and accessible
|
||||
- Validates trade-off analysis and alternative consideration
|
||||
- Requires evidence for architectural and design choices
|
||||
- Supports future decision review and learning
|
||||
|
||||
### Development Priority Validation
|
||||
**Code Over Documentation:**
|
||||
- Validates that documentation follows working code implementation
|
||||
- Prevents documentation-first development anti-patterns
|
||||
- Ensures documentation accuracy reflects actual implementation
|
||||
- Promotes iterative development with validated outcomes
|
||||
|
||||
**Working Software Priority:**
|
||||
- Verifies working implementations before extensive documentation
|
||||
- Validates incremental development with functional milestones
|
||||
- Ensures user value delivery through functional software
|
||||
- Supports rapid prototyping and validation cycles
|
||||
|
||||
### Efficiency and Quality Balance
|
||||
**Efficiency Over Verbosity:**
|
||||
- Analyzes output size and complexity for unnecessary verbosity
|
||||
- Recommends token efficiency techniques for large outputs
|
||||
- Validates communication clarity without redundancy
|
||||
- Promotes concise, actionable guidance and documentation
|
||||
|
||||
**Quality Without Compromise:**
|
||||
- Ensures efficiency improvements don't sacrifice quality
|
||||
- Validates testing and validation coverage during optimization
|
||||
- Maintains code clarity and maintainability standards
|
||||
- Balances development speed with long-term sustainability
|
||||
|
||||
## Learning Integration
|
||||
|
||||
The hook implements sophisticated learning mechanisms to continuously improve framework effectiveness:
|
||||
|
||||
### Effectiveness Measurement
|
||||
**Multi-Dimensional Scoring:**
|
||||
- **Overall Effectiveness**: Weighted combination of quality, performance, and satisfaction
|
||||
- **Quality Score**: Code quality, security compliance, and principle alignment
|
||||
- **Performance Score**: Execution time efficiency and resource utilization
|
||||
- **User Satisfaction Estimate**: Success rate and error impact assessment
|
||||
- **Learning Value**: Complexity, novelty, and insight generation potential
|
||||
|
||||
**Effectiveness Calculation:**
|
||||
```yaml
|
||||
effectiveness_weights:
|
||||
quality_score: 30% # Code quality and compliance
|
||||
performance_score: 25% # Execution efficiency
|
||||
user_satisfaction: 35% # Perceived value and success
|
||||
learning_value: 10% # Knowledge generation potential
|
||||
```
|
||||
|
||||
### Pattern Recognition and Adaptation
|
||||
**Success Pattern Detection:**
|
||||
- Identifies effective tool usage patterns and MCP server coordination
|
||||
- Recognizes high-quality output characteristics and optimal performance
|
||||
- Records successful validation patterns and compliance strategies
|
||||
- Builds pattern library for future operation optimization
|
||||
|
||||
**Failure Pattern Analysis:**
|
||||
- Detects recurring error patterns and failure modes
|
||||
- Analyzes root causes and contributing factors
|
||||
- Identifies improvement opportunities and prevention strategies
|
||||
- Generates targeted recommendations for specific failure types
|
||||
|
||||
**Adaptation Mechanisms:**
|
||||
- **Real-Time Adjustment**: Dynamic threshold modification based on effectiveness
|
||||
- **Rule Refinement**: Continuous improvement of validation rules and criteria
|
||||
- **Principle Enhancement**: Evolution of principle interpretation and application
|
||||
- **Validation Optimization**: Performance tuning based on usage patterns
|
||||
|
||||
### Learning Event Recording
|
||||
**Operation Pattern Learning:**
|
||||
- Records tool usage effectiveness with context and outcomes
|
||||
- Tracks MCP server coordination patterns and success rates
|
||||
- Documents user preference patterns and adaptation opportunities
|
||||
- Builds comprehensive operation effectiveness database
|
||||
|
||||
**Error Recovery Learning:**
|
||||
- Captures error context, recovery actions, and success rates
|
||||
- Identifies effective error handling patterns and prevention strategies
|
||||
- Records recovery time and resource requirements
|
||||
- Builds error pattern knowledge base for future prevention
|
||||
|
||||
## Error Pattern Detection
|
||||
|
||||
The hook implements advanced error pattern detection to identify and prevent recurring issues:
|
||||
|
||||
### Error Classification System
|
||||
**Severity-Based Classification:**
|
||||
- **Critical Errors**: Security vulnerabilities, data corruption risks, system instability
|
||||
- **Standard Errors**: Rule violations, quality failures, incomplete implementations
|
||||
- **Warnings**: Principle deviations, optimization opportunities, best practice suggestions
|
||||
- **Suggestions**: Code improvements, efficiency enhancements, learning recommendations
|
||||
|
||||
**Pattern Recognition Engine:**
|
||||
- **Temporal Pattern Detection**: Identifies error trends over time and contexts
|
||||
- **Contextual Pattern Analysis**: Recognizes error patterns specific to operation types
|
||||
- **Cross-Operation Correlation**: Detects error patterns spanning multiple tool executions
|
||||
- **User-Specific Pattern Learning**: Identifies individual user error tendencies
|
||||
|
||||
### Error Prevention Strategies
|
||||
**Proactive Prevention:**
|
||||
- **Pre-Validation Recommendations**: Suggests validation for similar high-risk operations
|
||||
- **Security Check Integration**: Implements automated security validation checks
|
||||
- **Performance Optimization**: Recommends parallel execution for large operations
|
||||
- **Pattern-Based Warnings**: Provides early warnings for known problematic patterns
|
||||
|
||||
**Reactive Learning:**
|
||||
- **Error Recovery Documentation**: Records successful recovery strategies
|
||||
- **Pattern Knowledge Base**: Builds comprehensive error pattern database
|
||||
- **Adaptation Recommendations**: Generates specific guidance for error prevention
|
||||
- **User Education**: Provides learning opportunities from error analysis
|
||||
|
||||
## Configuration
|
||||
|
||||
The hook's behavior is controlled through multiple configuration layers providing flexibility and customization:
|
||||
|
||||
### Primary Configuration Source
|
||||
**superclaude-config.json - post_tool_use section:**
|
||||
```json
|
||||
{
|
||||
"post_tool_use": {
|
||||
"enabled": true,
|
||||
"performance_target_ms": 100,
|
||||
"features": [
|
||||
"quality_validation",
|
||||
"rules_compliance_checking",
|
||||
"principles_alignment_verification",
|
||||
"effectiveness_measurement",
|
||||
"error_pattern_detection",
|
||||
"learning_opportunity_identification"
|
||||
],
|
||||
"configuration": {
|
||||
"rules_validation": true,
|
||||
"principles_validation": true,
|
||||
"quality_standards_enforcement": true,
|
||||
"effectiveness_tracking": true,
|
||||
"learning_integration": true
|
||||
},
|
||||
"validation_levels": {
|
||||
"basic": ["syntax_validation"],
|
||||
"standard": ["syntax_validation", "type_analysis", "code_quality"],
|
||||
"comprehensive": ["syntax_validation", "type_analysis", "code_quality", "security_assessment", "performance_analysis"],
|
||||
"production": ["syntax_validation", "type_analysis", "code_quality", "security_assessment", "performance_analysis", "integration_testing", "deployment_validation"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Detailed Validation Configuration
|
||||
**config/validation.yaml** provides comprehensive validation rule definitions:
|
||||
|
||||
**Rules Validation Configuration:**
|
||||
- File operation rules (read_before_write, absolute_paths_only, validate_before_execution)
|
||||
- Security requirements (input_validation, no_hardcoded_secrets, production_safety)
|
||||
- Error severity levels and blocking behavior
|
||||
- Context-aware validation adjustments
|
||||
|
||||
**Principles Validation Configuration:**
|
||||
- Evidence-based decision making requirements
|
||||
- Code-over-documentation enforcement
|
||||
- Efficiency-over-verbosity thresholds
|
||||
- Test-driven development validation
|
||||
|
||||
**Quality Standards:**
|
||||
- Minimum quality scores for different assessment areas
|
||||
- Performance thresholds and optimization indicators
|
||||
- Security compliance requirements and checks
|
||||
- Maintainability factors and measurement criteria
|
||||
|
||||
### Performance and Resource Configuration
|
||||
**Performance Targets:**
|
||||
```yaml
|
||||
performance_configuration:
|
||||
validation_targets:
|
||||
processing_time_ms: 100 # Primary performance target
|
||||
memory_usage_mb: 50 # Memory utilization limit
|
||||
cpu_utilization_percent: 30 # CPU usage threshold
|
||||
|
||||
optimization_strategies:
|
||||
parallel_validation: true # Enable parallel processing
|
||||
cached_results: true # Cache validation results
|
||||
incremental_validation: true # Optimize for repeated operations
|
||||
smart_rule_selection: true # Context-aware rule application
|
||||
```
|
||||
|
||||
**Resource Management:**
|
||||
- Maximum validation time limits with fallback mechanisms
|
||||
- Memory and CPU usage constraints with monitoring
|
||||
- Automatic resource optimization and constraint handling
|
||||
- Performance degradation detection and response
|
||||
|
||||
## Quality Gates Integration
|
||||
|
||||
The post_tool_use hook is integral to SuperClaude's 8-step validation cycle, contributing to multiple quality gates:
|
||||
|
||||
### Step 3: Code Quality Assessment
|
||||
**Comprehensive Quality Analysis:**
|
||||
- **Code Structure**: Evaluates organization, modularity, and architectural patterns
|
||||
- **Maintainability**: Assesses readability, documentation, and modification ease
|
||||
- **Best Practices**: Validates adherence to language and framework conventions
|
||||
- **Technical Debt**: Identifies accumulation and provides reduction recommendations
|
||||
|
||||
**Quality Metrics:**
|
||||
- Code quality score calculation (target: >0.7)
|
||||
- Maintainability index with trend analysis
|
||||
- Technical debt assessment and prioritization
|
||||
- Best practice compliance percentage
|
||||
|
||||
### Step 4: Security Assessment
|
||||
**Multi-Layer Security Validation:**
|
||||
- **Vulnerability Analysis**: Scans for common security vulnerabilities (OWASP Top 10)
|
||||
- **Input Validation**: Ensures proper sanitization and validation of external inputs
|
||||
- **Authentication/Authorization**: Validates proper access control implementation
|
||||
- **Data Protection**: Verifies secure data handling and storage practices
|
||||
|
||||
**Security Compliance:**
|
||||
- Security score calculation (target: >0.8)
|
||||
- Vulnerability severity assessment and prioritization
|
||||
- Compliance reporting for security standards
|
||||
- Threat modeling and risk assessment integration
|
||||
|
||||
### Step 5: Testing Validation
|
||||
**Test Coverage and Quality:**
|
||||
- **Test Presence**: Validates existence of appropriate tests for code changes
|
||||
- **Coverage Analysis**: Measures test coverage depth and breadth
|
||||
- **Test Quality**: Assesses test effectiveness and maintainability
|
||||
- **Integration Testing**: Validates cross-component test coverage
|
||||
|
||||
**Testing Metrics:**
|
||||
- Unit test coverage percentage (target: ≥80%)
|
||||
- Integration test coverage (target: ≥70%)
|
||||
- Test quality score and effectiveness measurement
|
||||
- Testing best practice compliance validation
|
||||
|
||||
### Integration with Other Quality Gates
|
||||
**Coordination with Pre-Tool-Use (Steps 1-2):**
|
||||
- Receives syntax and type validation results for enhanced analysis
|
||||
- Builds upon initial validation with deeper quality assessment
|
||||
- Provides feedback for future pre-validation optimization
|
||||
|
||||
**Coordination with Session End (Steps 6-8):**
|
||||
- Contributes validation results to performance analysis
|
||||
- Provides quality metrics for documentation verification
|
||||
- Supports integration testing with operation effectiveness data
|
||||
|
||||
### Quality Gate Reporting
|
||||
**Comprehensive Quality Reports:**
|
||||
- Step-by-step validation results with detailed findings
|
||||
- Quality score breakdowns by category and importance
|
||||
- Trend analysis and improvement recommendations
|
||||
- Compliance status with actionable remediation steps
|
||||
|
||||
**Integration Metrics:**
|
||||
- Overall quality gate passage rate
|
||||
- Step-specific success rates and failure analysis
|
||||
- Quality improvement trends over time
|
||||
- Framework effectiveness measurement and optimization
|
||||
|
||||
## Advanced Features
|
||||
|
||||
### Context-Aware Validation
|
||||
**Project Type Adaptations:**
|
||||
- **Frontend Projects**: Additional accessibility, responsive design, and browser compatibility checks
|
||||
- **Backend Projects**: Enhanced API security, data validation, and performance optimization focus
|
||||
- **Full-Stack Projects**: Integration testing, end-to-end validation, and deployment safety verification
|
||||
|
||||
**User Expertise Adjustments:**
|
||||
- **Beginner Users**: High validation verbosity, educational suggestions, step-by-step guidance
|
||||
- **Intermediate Users**: Medium verbosity, best practice suggestions, optimization recommendations
|
||||
- **Expert Users**: Low verbosity, advanced optimization suggestions, architectural guidance
|
||||
|
||||
### Learning System Integration
|
||||
**Cross-Hook Learning:**
|
||||
- Shares effectiveness data with pre_tool_use hook for optimization
|
||||
- Coordinates with session_start hook for user preference learning
|
||||
- Integrates with stop hook for comprehensive session analysis
|
||||
|
||||
**Adaptive Behavior:**
|
||||
- Adjusts validation thresholds based on user expertise and project context
|
||||
- Learns from validation effectiveness and user feedback
|
||||
- Optimizes rule selection and severity based on operation patterns
|
||||
|
||||
### Error Recovery and Resilience
|
||||
**Graceful Degradation:**
|
||||
- Maintains essential validation even during system constraints
|
||||
- Provides fallback validation reports on processing errors
|
||||
- Preserves user context and operation continuity during failures
|
||||
|
||||
**Learning from Failures:**
|
||||
- Records validation hook errors for system improvement
|
||||
- Analyzes failure patterns to prevent future issues
|
||||
- Generates insights from error recovery experiences
|
||||
|
||||
## Integration Examples
|
||||
|
||||
### MCP Server Coordination
|
||||
**Serena Integration:**
|
||||
- Receives semantic validation support for code structure analysis
|
||||
- Coordinates edit validation for complex refactoring operations
|
||||
- Leverages project context for enhanced validation accuracy
|
||||
|
||||
**Morphllm Integration:**
|
||||
- Validates intelligent editing operations and pattern applications
|
||||
- Coordinates edit effectiveness measurement and optimization
|
||||
- Provides feedback for fast-apply optimization
|
||||
|
||||
**Sequential Integration:**
|
||||
- Leverages complex validation analysis for multi-step operations
|
||||
- Coordinates systematic validation for architectural changes
|
||||
- Integrates reasoning validation with decision documentation
|
||||
|
||||
### Hook Ecosystem Integration
|
||||
**Pre-Tool-Use Coordination:**
|
||||
- Receives validation preparation data for enhanced analysis
|
||||
- Provides effectiveness feedback for future operation optimization
|
||||
- Coordinates rule enforcement across the complete execution cycle
|
||||
|
||||
**Session Management Integration:**
|
||||
- Contributes validation metrics to session analytics
|
||||
- Provides quality insights for session summary generation
|
||||
- Supports cross-session learning and pattern recognition
|
||||
|
||||
## Conclusion
|
||||
|
||||
The post_tool_use hook serves as the cornerstone of SuperClaude's quality assurance and continuous improvement system. By providing comprehensive validation, learning integration, and adaptive behavior, it ensures that every tool execution contributes to the framework's overall effectiveness while maintaining the highest standards of quality, security, and compliance.
|
||||
|
||||
Through its sophisticated validation levels, error pattern detection, and learning mechanisms, the hook enables SuperClaude to continuously evolve and improve, providing users with increasingly effective and reliable development assistance while maintaining strict adherence to the framework's core principles and operational rules.
|
||||
@@ -1,658 +0,0 @@
|
||||
# pre_compact Hook Technical Documentation
|
||||
|
||||
## Purpose
|
||||
|
||||
The `pre_compact` hook implements token optimization before context compaction in Claude Code. It analyzes content for compression opportunities, applies selective compression strategies, and maintains quality preservation targets while reducing token usage.
|
||||
|
||||
**Core Implementation**: Implements MODE_Token_Efficiency.md compression algorithms with selective content classification, symbol systems, and quality-gated compression with a target execution time of <150ms.
|
||||
|
||||
## Execution Context
|
||||
|
||||
The pre_compact hook runs before context compaction in Claude Code. According to `settings.json`, it has a 15-second timeout and executes via: `python3 ~/.claude/hooks/pre_compact.py`
|
||||
|
||||
**Actual Execution Flow:**
|
||||
1. Receives compaction request from Claude Code via stdin (JSON)
|
||||
2. Initializes PreCompactHook class with compression engine and shared modules
|
||||
3. Processes request through `process_pre_compact()` method
|
||||
4. Analyzes content characteristics and determines compression strategy
|
||||
5. Outputs compression configuration via stdout (JSON)
|
||||
6. Falls back gracefully on errors with no compression applied
|
||||
|
||||
## Performance Target
|
||||
|
||||
**Performance Target: <150ms execution time**
|
||||
|
||||
The hook operates within strict performance constraints to ensure real-time compression decisions:
|
||||
|
||||
### Performance Benchmarks
|
||||
- **Target Execution Time**: 150ms maximum
|
||||
- **Typical Performance**: 50-100ms for standard content
|
||||
- **Efficiency Metric**: 100 characters per millisecond processing rate
|
||||
- **Resource Overhead**: <5% additional memory usage during compression
|
||||
|
||||
### Performance Monitoring
|
||||
```python
|
||||
performance_metrics = {
|
||||
'compression_time_ms': execution_time,
|
||||
'target_met': execution_time < 150,
|
||||
'efficiency_score': chars_per_ms / 100,
|
||||
'processing_rate': content_length / execution_time
|
||||
}
|
||||
```
|
||||
|
||||
### Optimization Strategies
|
||||
- **Parallel Content Analysis**: Concurrent processing of content sections
|
||||
- **Intelligent Caching**: Reuse compression results for similar content patterns
|
||||
- **Early Exit Strategies**: Skip compression for framework content immediately
|
||||
- **Selective Processing**: Apply compression only where beneficial
|
||||
|
||||
## Compression Levels
|
||||
|
||||
**5-Level Compression Strategy** providing adaptive optimization based on resource constraints and content characteristics:
|
||||
|
||||
### Level 1: Minimal (0-40% compression)
|
||||
```yaml
|
||||
compression_level: minimal
|
||||
symbol_systems: false
|
||||
abbreviation_systems: false
|
||||
structural_optimization: false
|
||||
quality_threshold: 0.98
|
||||
use_cases:
|
||||
- user_content
|
||||
- low_resource_usage
|
||||
- high_quality_required
|
||||
```
|
||||
|
||||
**Application**: User project files, documentation, source code requiring high fidelity preservation.
|
||||
|
||||
### Level 2: Efficient (40-70% compression)
|
||||
```yaml
|
||||
compression_level: efficient
|
||||
symbol_systems: true
|
||||
abbreviation_systems: false
|
||||
structural_optimization: true
|
||||
quality_threshold: 0.95
|
||||
use_cases:
|
||||
- moderate_resource_usage
|
||||
- balanced_efficiency
|
||||
```
|
||||
|
||||
**Application**: Session metadata, checkpoint data, working artifacts with acceptable optimization trade-offs.
|
||||
|
||||
### Level 3: Compressed (70-85% compression)
|
||||
```yaml
|
||||
compression_level: compressed
|
||||
symbol_systems: true
|
||||
abbreviation_systems: true
|
||||
structural_optimization: true
|
||||
quality_threshold: 0.90
|
||||
use_cases:
|
||||
- high_resource_usage
|
||||
- user_requests_brevity
|
||||
```
|
||||
|
||||
**Application**: Analysis results, cached data, temporary working content with aggressive optimization.
|
||||
|
||||
### Level 4: Critical (85-95% compression)
|
||||
```yaml
|
||||
compression_level: critical
|
||||
symbol_systems: true
|
||||
abbreviation_systems: true
|
||||
structural_optimization: true
|
||||
advanced_techniques: true
|
||||
quality_threshold: 0.85
|
||||
use_cases:
|
||||
- resource_constraints
|
||||
- emergency_compression
|
||||
```
|
||||
|
||||
**Application**: Emergency resource situations, historical session data, highly repetitive content.
|
||||
|
||||
### Level 5: Emergency (95%+ compression)
|
||||
```yaml
|
||||
compression_level: emergency
|
||||
symbol_systems: true
|
||||
abbreviation_systems: true
|
||||
structural_optimization: true
|
||||
advanced_techniques: true
|
||||
aggressive_optimization: true
|
||||
quality_threshold: 0.80
|
||||
use_cases:
|
||||
- critical_resource_constraints
|
||||
- emergency_situations
|
||||
```
|
||||
|
||||
**Application**: Critical resource exhaustion scenarios with maximum token conservation priority.
|
||||
|
||||
## Selective Compression
|
||||
|
||||
**Framework exclusion and content classification** ensuring optimal compression strategies based on content type and preservation requirements:
|
||||
|
||||
### Content Classification System
|
||||
|
||||
#### Framework Content (0% compression)
|
||||
```yaml
|
||||
framework_exclusions:
|
||||
patterns:
|
||||
- "~/.claude/"
|
||||
- ".claude/"
|
||||
- "SuperClaude/*"
|
||||
- "CLAUDE.md"
|
||||
- "FLAGS.md"
|
||||
- "PRINCIPLES.md"
|
||||
- "ORCHESTRATOR.md"
|
||||
- "MCP_*.md"
|
||||
- "MODE_*.md"
|
||||
- "SESSION_LIFECYCLE.md"
|
||||
compression_level: "preserve"
|
||||
reasoning: "Framework content must be preserved for proper operation"
|
||||
```
|
||||
|
||||
**Protection Strategy**: Complete exclusion from all compression algorithms with immediate early exit upon framework content detection.
|
||||
|
||||
#### User Content Preservation (Minimal compression)
|
||||
```yaml
|
||||
user_content_preservation:
|
||||
patterns:
|
||||
- "project_files"
|
||||
- "user_documentation"
|
||||
- "source_code"
|
||||
- "configuration_files"
|
||||
- "custom_content"
|
||||
compression_level: "minimal"
|
||||
reasoning: "User content requires high fidelity preservation"
|
||||
```
|
||||
|
||||
**Protection Strategy**: Light compression with whitespace optimization only, preserving semantic accuracy and technical correctness.
|
||||
|
||||
#### Session Data Optimization (Efficient compression)
|
||||
```yaml
|
||||
session_data_optimization:
|
||||
patterns:
|
||||
- "session_metadata"
|
||||
- "checkpoint_data"
|
||||
- "cache_content"
|
||||
- "working_artifacts"
|
||||
- "analysis_results"
|
||||
compression_level: "efficient"
|
||||
reasoning: "Session data can be compressed while maintaining utility"
|
||||
```
|
||||
|
||||
**Optimization Strategy**: Symbol systems and structural optimization applied with 95% quality preservation target.
|
||||
|
||||
### Content Detection Algorithm
|
||||
```python
|
||||
def _analyze_content_sources(self, content: str, metadata: dict) -> Tuple[float, float]:
|
||||
"""Analyze ratio of framework vs user content."""
|
||||
framework_indicators = [
|
||||
'SuperClaude', 'CLAUDE.md', 'FLAGS.md', 'PRINCIPLES.md',
|
||||
'ORCHESTRATOR.md', 'MCP_', 'MODE_', 'SESSION_LIFECYCLE'
|
||||
]
|
||||
|
||||
user_indicators = [
|
||||
'project_files', 'user_documentation', 'source_code',
|
||||
'configuration_files', 'custom_content'
|
||||
]
|
||||
```
|
||||
|
||||
## Symbol Systems
|
||||
|
||||
**Symbol systems replace verbose text** with standardized symbols for efficient communication while preserving semantic meaning:
|
||||
|
||||
### Core Logic & Flow Symbols
|
||||
| Symbol | Meaning | Example Usage |
|
||||
|--------|---------|---------------|
|
||||
| → | leads to, implies | `auth.js:45 → security risk` |
|
||||
| ⇒ | transforms to | `input ⇒ validated_output` |
|
||||
| ← | rollback, reverse | `migration ← rollback` |
|
||||
| ⇄ | bidirectional | `sync ⇄ remote` |
|
||||
| & | and, combine | `security & performance` |
|
||||
| \| | separator, or | `react\|vue\|angular` |
|
||||
| : | define, specify | `scope: file\|module` |
|
||||
| » | sequence, then | `build » test » deploy` |
|
||||
| ∴ | therefore | `tests fail ∴ code broken` |
|
||||
| ∵ | because | `slow ∵ O(n²) algorithm` |
|
||||
| ≡ | equivalent | `method1 ≡ method2` |
|
||||
| ≈ | approximately | `≈2.5K tokens` |
|
||||
| ≠ | not equal | `actual ≠ expected` |
|
||||
|
||||
### Status & Progress Symbols
|
||||
| Symbol | Meaning | Context |
|
||||
|--------|---------|---------|
|
||||
| ✅ | completed, passed | Task completion, validation success |
|
||||
| ❌ | failed, error | Operation failure, validation error |
|
||||
| ⚠️ | warning | Non-critical issues, attention required |
|
||||
| ℹ️ | information | Informational messages, context |
|
||||
| 🔄 | in progress | Active operations, processing |
|
||||
| ⏳ | waiting, pending | Queued operations, dependencies |
|
||||
| 🚨 | critical, urgent | High-priority issues, immediate action |
|
||||
| 🎯 | target, goal | Objectives, milestones |
|
||||
| 📊 | metrics, data | Performance data, analytics |
|
||||
| 💡 | insight, learning | Discoveries, optimizations |
|
||||
|
||||
### Technical Domain Symbols
|
||||
| Symbol | Domain | Usage Context |
|
||||
|--------|---------|---------------|
|
||||
| ⚡ | Performance | Speed optimization, efficiency |
|
||||
| 🔍 | Analysis | Investigation, examination |
|
||||
| 🔧 | Configuration | Setup, tool configuration |
|
||||
| 🛡️ | Security | Protection, vulnerability analysis |
|
||||
| 📦 | Deployment | Packaging, distribution |
|
||||
| 🎨 | Design | UI/UX, frontend development |
|
||||
| 🌐 | Network | Web services, connectivity |
|
||||
| 📱 | Mobile | Responsive design, mobile apps |
|
||||
| 🏗️ | Architecture | System structure, design patterns |
|
||||
| 🧩 | Components | Modular design, composability |
|
||||
|
||||
### Symbol System Implementation
|
||||
```python
|
||||
symbol_systems = {
|
||||
'core_logic_flow': {
|
||||
'enabled': True,
|
||||
'mappings': {
|
||||
'leads to': '→',
|
||||
'transforms to': '⇒',
|
||||
'therefore': '∴',
|
||||
'because': '∵'
|
||||
}
|
||||
},
|
||||
'status_progress': {
|
||||
'enabled': True,
|
||||
'mappings': {
|
||||
'completed': '✅',
|
||||
'failed': '❌',
|
||||
'warning': '⚠️',
|
||||
'in progress': '🔄'
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Abbreviation Systems
|
||||
|
||||
**Technical abbreviations for efficiency** providing domain-specific shorthand while maintaining clarity and context:
|
||||
|
||||
### System & Architecture Abbreviations
|
||||
| Full Term | Abbreviation | Context |
|
||||
|-----------|--------------|---------|
|
||||
| configuration | cfg | System settings, setup files |
|
||||
| settings | cfg | Configuration parameters |
|
||||
| implementation | impl | Code structure, algorithms |
|
||||
| code structure | impl | Software architecture |
|
||||
| architecture | arch | System design, patterns |
|
||||
| system design | arch | Architectural decisions |
|
||||
| performance | perf | Optimization, benchmarks |
|
||||
| optimization | perf | Efficiency improvements |
|
||||
| operations | ops | Deployment, DevOps |
|
||||
| deployment | ops | Release processes |
|
||||
| environment | env | Runtime context, settings |
|
||||
| runtime context | env | Execution environment |
|
||||
|
||||
### Development Process Abbreviations
|
||||
| Full Term | Abbreviation | Context |
|
||||
|-----------|--------------|---------|
|
||||
| requirements | req | Project specifications |
|
||||
| dependencies | deps | Package management |
|
||||
| packages | deps | Library dependencies |
|
||||
| validation | val | Testing, verification |
|
||||
| verification | val | Quality assurance |
|
||||
| testing | test | Quality validation |
|
||||
| quality assurance | test | Testing processes |
|
||||
| documentation | docs | Technical writing |
|
||||
| guides | docs | User documentation |
|
||||
| standards | std | Coding conventions |
|
||||
| conventions | std | Style guidelines |
|
||||
|
||||
### Quality & Analysis Abbreviations
|
||||
| Full Term | Abbreviation | Context |
|
||||
|-----------|--------------|---------|
|
||||
| quality | qual | Code quality, maintainability |
|
||||
| maintainability | qual | Long-term code health |
|
||||
| security | sec | Safety measures, vulnerabilities |
|
||||
| safety measures | sec | Security protocols |
|
||||
| error | err | Exception handling |
|
||||
| exception handling | err | Error management |
|
||||
| recovery | rec | Resilience, fault tolerance |
|
||||
| resilience | rec | System robustness |
|
||||
| severity | sev | Priority levels, criticality |
|
||||
| priority level | sev | Issue classification |
|
||||
| optimization | opt | Performance improvements |
|
||||
| improvement | opt | Enhancement strategies |
|
||||
|
||||
### Abbreviation System Implementation
|
||||
```python
|
||||
abbreviation_systems = {
|
||||
'system_architecture': {
|
||||
'enabled': True,
|
||||
'mappings': {
|
||||
'configuration': 'cfg',
|
||||
'implementation': 'impl',
|
||||
'architecture': 'arch',
|
||||
'performance': 'perf'
|
||||
}
|
||||
},
|
||||
'development_process': {
|
||||
'enabled': True,
|
||||
'mappings': {
|
||||
'requirements': 'req',
|
||||
'dependencies': 'deps',
|
||||
'validation': 'val',
|
||||
'testing': 'test'
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Quality Preservation
|
||||
|
||||
**95% information retention target** through comprehensive quality validation and evidence-based compression effectiveness monitoring:
|
||||
|
||||
### Quality Preservation Standards
|
||||
```yaml
|
||||
quality_preservation:
|
||||
minimum_thresholds:
|
||||
information_preservation: 0.95
|
||||
semantic_accuracy: 0.95
|
||||
technical_correctness: 0.98
|
||||
user_content_fidelity: 0.99
|
||||
|
||||
validation_criteria:
|
||||
key_concept_retention: true
|
||||
technical_term_preservation: true
|
||||
code_example_accuracy: true
|
||||
reference_link_preservation: true
|
||||
```
|
||||
|
||||
### Quality Validation Framework
|
||||
```python
|
||||
def _validate_compression_quality(self, compression_results, strategy) -> dict:
|
||||
"""Validate compression quality against standards."""
|
||||
validation = {
|
||||
'overall_quality_met': True,
|
||||
'preservation_score': 0.0,
|
||||
'compression_efficiency': 0.0,
|
||||
'quality_issues': [],
|
||||
'quality_warnings': []
|
||||
}
|
||||
|
||||
# Calculate preservation score
|
||||
total_preservation = sum(result.preservation_score for result in compression_results.values())
|
||||
validation['preservation_score'] = total_preservation / len(compression_results)
|
||||
|
||||
# Quality threshold validation
|
||||
if validation['preservation_score'] < strategy.quality_threshold:
|
||||
validation['overall_quality_met'] = False
|
||||
validation['quality_issues'].append(
|
||||
f"Preservation score {validation['preservation_score']:.2f} below threshold {strategy.quality_threshold}"
|
||||
)
|
||||
```
|
||||
|
||||
### Quality Monitoring Metrics
|
||||
- **Information Preservation**: Semantic content retention measurement
|
||||
- **Technical Correctness**: Code accuracy and reference preservation
|
||||
- **Compression Efficiency**: Token reduction vs. quality trade-off analysis
|
||||
- **User Content Fidelity**: Project-specific content preservation verification
|
||||
|
||||
### Quality Gate Integration
|
||||
```python
|
||||
quality_validation = self._validate_compression_quality(
|
||||
compression_results, compression_strategy
|
||||
)
|
||||
|
||||
if not quality_validation['overall_quality_met']:
|
||||
log_decision(
|
||||
"pre_compact",
|
||||
"quality_validation",
|
||||
"failed",
|
||||
f"Preservation score: {quality_validation['preservation_score']:.2f}"
|
||||
)
|
||||
```
|
||||
|
||||
## Configuration
|
||||
|
||||
**Settings from compression.yaml** providing comprehensive configuration management for adaptive compression strategies:
|
||||
|
||||
### Core Configuration Structure
|
||||
```yaml
|
||||
# Performance Targets
|
||||
performance_targets:
|
||||
processing_time_ms: 150
|
||||
compression_ratio_target: 0.50
|
||||
quality_preservation_target: 0.95
|
||||
token_efficiency_gain: 0.40
|
||||
|
||||
# Adaptive Compression Strategy
|
||||
adaptive_compression:
|
||||
context_awareness:
|
||||
user_expertise_factor: true
|
||||
project_complexity_factor: true
|
||||
domain_specific_optimization: true
|
||||
|
||||
learning_integration:
|
||||
effectiveness_feedback: true
|
||||
user_preference_learning: true
|
||||
pattern_optimization: true
|
||||
```
|
||||
|
||||
### Compression Level Configuration
|
||||
```python
|
||||
def __init__(self):
|
||||
# Load compression configuration
|
||||
try:
|
||||
self.compression_config = config_loader.load_config('compression')
|
||||
except FileNotFoundError:
|
||||
self.compression_config = self.hook_config.get('configuration', {})
|
||||
|
||||
# Performance tracking
|
||||
self.performance_target_ms = config_loader.get_hook_config(
|
||||
'pre_compact', 'performance_target_ms', 150
|
||||
)
|
||||
```
|
||||
|
||||
### Dynamic Configuration Management
|
||||
- **Context-Aware Settings**: Automatic adjustment based on content type and resource state
|
||||
- **Learning Integration**: User preference adaptation and pattern optimization
|
||||
- **Performance Monitoring**: Real-time configuration tuning based on effectiveness metrics
|
||||
- **Fallback Strategies**: Graceful degradation when configuration loading fails
|
||||
|
||||
### Integration with SuperClaude Framework
|
||||
```yaml
|
||||
integration:
|
||||
mcp_servers:
|
||||
morphllm: "coordinate_compression_with_editing"
|
||||
serena: "memory_compression_strategies"
|
||||
|
||||
modes:
|
||||
token_efficiency: "primary_compression_mode"
|
||||
task_management: "session_data_compression"
|
||||
|
||||
learning_engine:
|
||||
effectiveness_tracking: true
|
||||
pattern_learning: true
|
||||
adaptation_feedback: true
|
||||
```
|
||||
|
||||
## MODE_Token_Efficiency Integration
|
||||
|
||||
**Implementation of MODE_Token_Efficiency compression algorithms** providing seamless integration with SuperClaude's token optimization behavioral mode:
|
||||
|
||||
### Mode Integration Architecture
|
||||
```python
|
||||
# MODE_Token_Efficiency.md → pre_compact.py implementation
|
||||
class PreCompactHook:
|
||||
"""
|
||||
Pre-compact hook implementing SuperClaude token efficiency intelligence.
|
||||
|
||||
Implements MODE_Token_Efficiency.md algorithms:
|
||||
- 5-level compression strategy
|
||||
- Selective content classification
|
||||
- Symbol systems optimization
|
||||
- Quality preservation validation
|
||||
"""
|
||||
```
|
||||
|
||||
### Behavioral Mode Coordination
|
||||
- **Auto-Activation**: Resource usage >75%, large-scale operations, user brevity requests
|
||||
- **Compression Strategy Selection**: Adaptive algorithm based on MODE configuration
|
||||
- **Quality Gate Integration**: Validation against MODE preservation targets
|
||||
- **Performance Compliance**: Sub-150ms execution aligned with MODE efficiency requirements
|
||||
|
||||
### MODE Configuration Inheritance
|
||||
```yaml
|
||||
# MODE_Token_Efficiency.md settings → compression.yaml
|
||||
compression_levels:
|
||||
minimal: # MODE: 0-40% compression
|
||||
quality_threshold: 0.98
|
||||
symbol_systems: false
|
||||
|
||||
efficient: # MODE: 40-70% compression
|
||||
quality_threshold: 0.95
|
||||
symbol_systems: true
|
||||
|
||||
compressed: # MODE: 70-85% compression
|
||||
quality_threshold: 0.90
|
||||
abbreviation_systems: true
|
||||
```
|
||||
|
||||
### Real-Time Mode Synchronization
|
||||
```python
|
||||
def _determine_compression_strategy(self, context: dict, content_analysis: dict) -> CompressionStrategy:
|
||||
"""Determine optimal compression strategy aligned with MODE_Token_Efficiency."""
|
||||
# MODE-compliant compression level determination
|
||||
compression_level = self.compression_engine.determine_compression_level({
|
||||
'resource_usage_percent': context.get('token_usage_percent', 0),
|
||||
'conversation_length': context.get('conversation_length', 0),
|
||||
'user_requests_brevity': context.get('user_requests_compression', False),
|
||||
'complexity_score': context.get('content_complexity', 0.0)
|
||||
})
|
||||
```
|
||||
|
||||
### Learning Integration with MODE
|
||||
```python
|
||||
def _record_compression_learning(self, context, compression_results, quality_validation):
|
||||
"""Record compression learning aligned with MODE adaptation."""
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.PERFORMANCE_OPTIMIZATION,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'compression_level': compression_level.value,
|
||||
'preservation_score': quality_validation['preservation_score'],
|
||||
'compression_efficiency': quality_validation['compression_efficiency']
|
||||
},
|
||||
overall_effectiveness,
|
||||
0.9 # High confidence in MODE-aligned compression metrics
|
||||
)
|
||||
```
|
||||
|
||||
### Framework Compliance Validation
|
||||
- **Symbol Systems**: Direct implementation of MODE symbol mappings
|
||||
- **Abbreviation Systems**: MODE-compliant technical abbreviation patterns
|
||||
- **Quality Preservation**: MODE 95% information retention standards
|
||||
- **Selective Compression**: MODE content classification and protection strategies
|
||||
|
||||
## Key Features
|
||||
|
||||
### Intelligent Compression Strategy Selection
|
||||
```python
|
||||
def _determine_compression_strategy(self, context: dict, content_analysis: dict) -> CompressionStrategy:
|
||||
"""
|
||||
Adaptive compression strategy based on:
|
||||
- Resource constraints and token usage
|
||||
- Content type classification
|
||||
- User preferences and expertise level
|
||||
- Quality preservation requirements
|
||||
"""
|
||||
```
|
||||
|
||||
### Selective Content Preservation
|
||||
- **Framework Exclusion**: Zero compression for SuperClaude components
|
||||
- **User Content Protection**: High-fidelity preservation for project files
|
||||
- **Session Data Optimization**: Efficient compression for operational data
|
||||
- **Quality-Gated Processing**: Real-time validation against preservation targets
|
||||
|
||||
### Symbol Systems Optimization
|
||||
- **Logic Flow Enhancement**: Mathematical and directional symbols
|
||||
- **Status Communication**: Visual progress and state indicators
|
||||
- **Domain-Specific Symbols**: Technical context-aware representations
|
||||
- **Persona-Aware Selection**: Symbol choice based on active domain expertise
|
||||
|
||||
### Abbreviation Systems
|
||||
- **Technical Efficiency**: Domain-specific shorthand for common terms
|
||||
- **Context-Sensitive Application**: Intelligent abbreviation based on user familiarity
|
||||
- **Quality Preservation**: Abbreviations that maintain semantic clarity
|
||||
- **Learning Integration**: Pattern optimization based on effectiveness feedback
|
||||
|
||||
### Quality-Gated Compression
|
||||
- **Real-Time Validation**: Continuous quality monitoring during compression
|
||||
- **Preservation Score Tracking**: Quantitative information retention measurement
|
||||
- **Adaptive Threshold Management**: Dynamic quality targets based on content type
|
||||
- **Fallback Strategies**: Graceful degradation when quality targets not met
|
||||
|
||||
## Implementation Details
|
||||
|
||||
### Compression Engine Architecture
|
||||
```python
|
||||
from compression_engine import (
|
||||
CompressionEngine, CompressionLevel, ContentType,
|
||||
CompressionResult, CompressionStrategy
|
||||
)
|
||||
|
||||
class PreCompactHook:
|
||||
def __init__(self):
|
||||
self.compression_engine = CompressionEngine()
|
||||
self.performance_target_ms = 150
|
||||
```
|
||||
|
||||
### Content Analysis Pipeline
|
||||
1. **Content Characteristics Analysis**: Complexity, repetition, technical density
|
||||
2. **Source Classification**: Framework vs. user vs. session content identification
|
||||
3. **Compressibility Assessment**: Potential optimization opportunity evaluation
|
||||
4. **Strategy Selection**: Optimal compression level and technique determination
|
||||
5. **Quality Validation**: Real-time preservation score monitoring
|
||||
|
||||
### Performance Optimization Techniques
|
||||
- **Early Exit Strategy**: Framework content bypass for immediate exclusion
|
||||
- **Parallel Processing**: Concurrent analysis of content sections
|
||||
- **Intelligent Caching**: Compression result reuse for similar patterns
|
||||
- **Selective Application**: Compression only where beneficial and safe
|
||||
|
||||
### Error Handling and Fallback
|
||||
```python
|
||||
def _create_fallback_compression_config(self, compact_request: dict, error: str) -> dict:
|
||||
"""Create fallback compression configuration on error."""
|
||||
return {
|
||||
'compression_enabled': False,
|
||||
'fallback_mode': True,
|
||||
'error': error,
|
||||
'quality': {
|
||||
'preservation_score': 1.0, # No compression = perfect preservation
|
||||
'quality_met': False, # But failed to optimize
|
||||
'issues': [f"Compression hook error: {error}"]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Results and Benefits
|
||||
|
||||
### Typical Performance Metrics
|
||||
- **Token Reduction**: 30-50% typical savings with quality preservation
|
||||
- **Processing Speed**: 50-100ms typical execution time (well under 150ms target)
|
||||
- **Quality Preservation**: ≥95% information retention consistently achieved
|
||||
- **Framework Protection**: 100% exclusion success rate for SuperClaude components
|
||||
|
||||
### Integration Benefits
|
||||
- **Seamless MODE Integration**: Direct implementation of MODE_Token_Efficiency algorithms
|
||||
- **Real-Time Optimization**: Sub-150ms compression decisions during active sessions
|
||||
- **Quality-First Approach**: Preservation targets never compromised for efficiency gains
|
||||
- **Adaptive Intelligence**: Learning-based optimization for improved effectiveness over time
|
||||
|
||||
### User Experience Improvements
|
||||
- **Transparent Operation**: Compression applied without user intervention or awareness
|
||||
- **Quality Assurance**: Technical correctness and semantic accuracy maintained
|
||||
- **Performance Enhancement**: Faster response times through optimized token usage
|
||||
- **Contextual Adaptation**: Compression strategies tailored to specific use cases and domains
|
||||
|
||||
---
|
||||
|
||||
*This hook serves as the core implementation of SuperClaude's intelligent token optimization system, providing evidence-based compression with adaptive strategies and quality-first preservation standards.*
|
||||
@@ -1,773 +0,0 @@
|
||||
# Pre-Tool-Use Hook Technical Documentation
|
||||
|
||||
## Purpose
|
||||
|
||||
The `pre_tool_use` hook analyzes tool requests and provides intelligent routing decisions before tool execution in Claude Code. It determines optimal MCP server coordination, performance optimizations, and execution strategies based on tool characteristics and context.
|
||||
|
||||
**Core Implementation**: A 648-line Python implementation that processes tool requests, analyzes operation characteristics, routes to appropriate MCP servers, and provides enhanced tool configurations with a target execution time of <200ms.
|
||||
|
||||
---
|
||||
|
||||
## Execution Context
|
||||
|
||||
The pre_tool_use hook runs before every tool execution in Claude Code. According to `settings.json`, it has a 15-second timeout and executes via: `python3 ~/.claude/hooks/pre_tool_use.py`
|
||||
|
||||
**Actual Execution Flow:**
|
||||
1. Receives tool request from Claude Code via stdin (JSON)
|
||||
2. Initializes PreToolUseHook class with shared module components
|
||||
3. Processes tool request through `process_tool_use()` method
|
||||
4. Analyzes operation characteristics and routing patterns
|
||||
5. Outputs enhanced tool configuration via stdout (JSON)
|
||||
6. Falls back gracefully on errors with basic tool configuration
|
||||
|
||||
**Input Processing:**
|
||||
- Extracts tool context including tool name, parameters, user intent
|
||||
- Analyzes operation characteristics (file count, complexity, parallelizability)
|
||||
- Identifies tool chain patterns (read-edit, multi-file, analysis chains)
|
||||
|
||||
**Output Configuration:**
|
||||
- Enhanced mode flag and MCP server coordination
|
||||
- Performance optimization settings (parallel execution, caching)
|
||||
- Quality enhancement settings (validation, error recovery)
|
||||
- Execution metadata (estimated time, complexity score, intelligence level)
|
||||
|
||||
---
|
||||
|
||||
## Performance Target
|
||||
|
||||
### Primary Target: <200ms Execution Time
|
||||
- **Requirement**: Complete routing analysis and configuration within 200ms
|
||||
- **Measurement**: End-to-end hook execution time from input to enhanced configuration
|
||||
- **Validation**: Real-time performance tracking with target compliance reporting
|
||||
- **Optimization**: Cached pattern recognition, pre-computed routing tables, intelligent fallbacks
|
||||
|
||||
### Performance Architecture
|
||||
```yaml
|
||||
Performance Zones:
|
||||
green_zone: 0-150ms # Optimal performance with full intelligence
|
||||
yellow_zone: 150-200ms # Target compliance with efficiency mode
|
||||
red_zone: 200ms+ # Performance fallback with reduced intelligence
|
||||
```
|
||||
|
||||
### Efficiency Calculation
|
||||
```python
|
||||
efficiency_score = (
|
||||
time_efficiency * 0.4 + # Execution speed relative to target
|
||||
complexity_efficiency * 0.3 + # Handling complexity appropriately
|
||||
resource_efficiency * 0.3 # Resource utilization optimization
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Core Features
|
||||
|
||||
### 1. Intelligent Tool Routing
|
||||
**Pattern-Based Tool Analysis**:
|
||||
- Analyzes tool name, parameters, and context to determine optimal execution strategy
|
||||
- Detects operation complexity (0.0-1.0 scale) based on file count, operation type, and requirements
|
||||
- Identifies parallelization opportunities for multi-file operations
|
||||
- Determines intelligence requirements for analysis and generation tasks
|
||||
|
||||
**Operation Categorization**:
|
||||
```python
|
||||
Operation Types:
|
||||
- READ: File reading, search, navigation
|
||||
- WRITE: File creation, editing, updates
|
||||
- BUILD: Implementation, generation, creation
|
||||
- TEST: Validation, testing, verification
|
||||
- ANALYZE: Analysis, debugging, investigation
|
||||
```
|
||||
|
||||
**Complexity Scoring Algorithm**:
|
||||
```python
|
||||
base_complexity = {
|
||||
'READ': 0.0,
|
||||
'WRITE': 0.2,
|
||||
'BUILD': 0.4,
|
||||
'TEST': 0.1,
|
||||
'ANALYZE': 0.3
|
||||
}
|
||||
|
||||
file_multiplier = (file_count - 1) * 0.1
|
||||
directory_multiplier = (directory_count - 1) * 0.05
|
||||
intelligence_bonus = 0.2 if requires_intelligence else 0.0
|
||||
|
||||
complexity_score = base_complexity + file_multiplier + directory_multiplier + intelligence_bonus
|
||||
```
|
||||
|
||||
### 2. Context-Aware Configuration
|
||||
**Session Context Integration**:
|
||||
- Tracks tool usage patterns across session for optimization opportunities
|
||||
- Analyzes tool chain patterns (Read→Edit, Multi-file operations, Analysis chains)
|
||||
- Applies session-specific optimizations based on detected patterns
|
||||
- Maintains resource state awareness for performance tuning
|
||||
|
||||
**Operation Chain Analysis**:
|
||||
```python
|
||||
Pattern Detection:
|
||||
- read_edit_pattern: Read followed by Edit operations
|
||||
- multi_file_pattern: Multiple file operations in sequence
|
||||
- analysis_chain: Sequential analysis operations with caching opportunities
|
||||
```
|
||||
|
||||
### 3. Real-Time Adaptation
|
||||
**Learning Engine Integration**:
|
||||
- Records tool usage effectiveness for routing optimization
|
||||
- Adapts routing decisions based on historical performance
|
||||
- Applies user-specific and project-specific routing preferences
|
||||
- Continuous improvement through effectiveness measurement
|
||||
|
||||
**Adaptation Scopes**:
|
||||
- **User Level**: Personal routing preferences and patterns
|
||||
- **Project Level**: Project-specific tool effectiveness patterns
|
||||
- **Session Level**: Real-time adaptation within current session
|
||||
|
||||
---
|
||||
|
||||
## MCP Server Routing Logic
|
||||
|
||||
### Server Capability Matching
|
||||
The hook implements sophisticated capability matching to select optimal MCP servers:
|
||||
|
||||
```python
|
||||
Server Capabilities Map:
|
||||
context7: [documentation_access, framework_patterns, best_practices]
|
||||
sequential: [complex_reasoning, systematic_analysis, hypothesis_testing]
|
||||
magic: [ui_generation, design_systems, component_patterns]
|
||||
playwright: [browser_automation, testing_frameworks, performance_testing]
|
||||
morphllm: [pattern_application, fast_apply, intelligent_editing]
|
||||
serena: [semantic_understanding, project_context, memory_management]
|
||||
```
|
||||
|
||||
### Routing Decision Matrix
|
||||
|
||||
#### Single Server Selection
|
||||
```yaml
|
||||
Context7 Triggers:
|
||||
- Library/framework keywords in user intent
|
||||
- Documentation-related operations
|
||||
- API reference needs
|
||||
- Best practices queries
|
||||
|
||||
Sequential Triggers:
|
||||
- Complexity score > 0.6
|
||||
- Multi-step analysis required
|
||||
- Debugging complex issues
|
||||
- System architecture analysis
|
||||
|
||||
Magic Triggers:
|
||||
- UI/component keywords
|
||||
- Frontend development operations
|
||||
- Design system integration
|
||||
- Component generation requests
|
||||
|
||||
Playwright Triggers:
|
||||
- Testing operations
|
||||
- Browser automation needs
|
||||
- Performance testing requirements
|
||||
- E2E validation requests
|
||||
|
||||
Morphllm Triggers:
|
||||
- Pattern-based editing
|
||||
- Fast apply suitable operations
|
||||
- Token optimization critical
|
||||
- Simple to moderate complexity
|
||||
|
||||
Serena Triggers:
|
||||
- File count > 5
|
||||
- Symbol-level operations
|
||||
- Project-wide analysis
|
||||
- Memory operations
|
||||
```
|
||||
|
||||
#### Multi-Server Coordination
|
||||
```python
|
||||
Coordination Strategies:
|
||||
- single_server: One MCP server handles the operation
|
||||
- collaborative: Multiple servers work together
|
||||
- sequential_handoff: Primary server → Secondary server
|
||||
- parallel_coordination: Servers work on different aspects simultaneously
|
||||
```
|
||||
|
||||
### Server Selection Algorithm
|
||||
```python
|
||||
def select_mcp_servers(context, requirements):
|
||||
servers = []
|
||||
|
||||
# Primary capability matching
|
||||
for server, capabilities in server_capabilities.items():
|
||||
if any(cap in requirements['capabilities_needed'] for cap in capabilities):
|
||||
servers.append(server)
|
||||
|
||||
# Context-specific routing
|
||||
if context['complexity_score'] > 0.6:
|
||||
servers.append('sequential')
|
||||
|
||||
if context['file_count'] > 5:
|
||||
servers.append('serena')
|
||||
|
||||
# User intent analysis
|
||||
intent_lower = context.get('user_intent', '').lower()
|
||||
if any(word in intent_lower for word in ['component', 'ui', 'frontend']):
|
||||
servers.append('magic')
|
||||
|
||||
# Deduplication and prioritization
|
||||
return list(dict.fromkeys(servers)) # Preserve order, remove duplicates
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Fallback Strategies
|
||||
|
||||
### Hierarchy of Fallback Options
|
||||
|
||||
#### Level 1: Preferred MCP Server Unavailable
|
||||
```python
|
||||
Strategy: Alternative Server Selection
|
||||
- Sequential unavailable → Use Morphllm for analysis
|
||||
- Serena unavailable → Use native tools with manual coordination
|
||||
- Magic unavailable → Generate basic components with Context7 patterns
|
||||
```
|
||||
|
||||
#### Level 2: Multiple MCP Servers Unavailable
|
||||
```python
|
||||
Strategy: Capability Degradation
|
||||
- Disable enhanced intelligence features
|
||||
- Fall back to native Claude Code tools
|
||||
- Maintain basic functionality with warnings
|
||||
- Preserve user context and intent
|
||||
```
|
||||
|
||||
#### Level 3: All MCP Servers Unavailable
|
||||
```python
|
||||
Strategy: Native Tool Execution
|
||||
- Execute original tool request without enhancement
|
||||
- Log degradation for performance analysis
|
||||
- Provide clear feedback about reduced capabilities
|
||||
- Maintain operational continuity
|
||||
```
|
||||
|
||||
### Fallback Configuration Generation
|
||||
```python
|
||||
def create_fallback_tool_config(tool_request, error):
|
||||
return {
|
||||
'tool_name': tool_request.get('tool_name'),
|
||||
'enhanced_mode': False,
|
||||
'fallback_mode': True,
|
||||
'error': error,
|
||||
'mcp_integration': {
|
||||
'enabled': False,
|
||||
'servers': [],
|
||||
'coordination_strategy': 'none'
|
||||
},
|
||||
'performance_optimization': {
|
||||
'parallel_execution': False,
|
||||
'caching_enabled': False,
|
||||
'optimizations': []
|
||||
},
|
||||
'performance_metrics': {
|
||||
'routing_time_ms': 0,
|
||||
'target_met': False,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Error Recovery Mechanisms
|
||||
- **Graceful Degradation**: Reduce capability rather than failing completely
|
||||
- **Context Preservation**: Maintain user intent and session context during fallback
|
||||
- **Performance Continuity**: Ensure operations continue with acceptable performance
|
||||
- **Learning Integration**: Record fallback events for routing improvement
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
### Hook-Specific Configuration (superclaude-config.json)
|
||||
```json
|
||||
{
|
||||
"pre_tool_use": {
|
||||
"enabled": true,
|
||||
"description": "ORCHESTRATOR + MCP routing intelligence for optimal tool selection",
|
||||
"performance_target_ms": 200,
|
||||
"features": [
|
||||
"intelligent_tool_routing",
|
||||
"mcp_server_selection",
|
||||
"performance_optimization",
|
||||
"context_aware_configuration",
|
||||
"fallback_strategy_implementation",
|
||||
"real_time_adaptation"
|
||||
],
|
||||
"configuration": {
|
||||
"mcp_intelligence": true,
|
||||
"pattern_detection": true,
|
||||
"learning_adaptations": true,
|
||||
"performance_optimization": true,
|
||||
"fallback_strategies": true
|
||||
},
|
||||
"integration": {
|
||||
"mcp_servers": ["context7", "sequential", "magic", "playwright", "morphllm", "serena"],
|
||||
"quality_gates": true,
|
||||
"learning_engine": true
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### MCP Server Integration Configuration
|
||||
```json
|
||||
{
|
||||
"mcp_server_integration": {
|
||||
"enabled": true,
|
||||
"servers": {
|
||||
"context7": {
|
||||
"description": "Library documentation and framework patterns",
|
||||
"capabilities": ["documentation_access", "framework_patterns", "best_practices"],
|
||||
"performance_profile": "standard"
|
||||
},
|
||||
"sequential": {
|
||||
"description": "Multi-step reasoning and complex analysis",
|
||||
"capabilities": ["complex_reasoning", "systematic_analysis", "hypothesis_testing"],
|
||||
"performance_profile": "intensive"
|
||||
},
|
||||
"serena": {
|
||||
"description": "Semantic analysis and memory management",
|
||||
"capabilities": ["semantic_understanding", "project_context", "memory_management"],
|
||||
"performance_profile": "standard"
|
||||
}
|
||||
},
|
||||
"coordination": {
|
||||
"intelligent_routing": true,
|
||||
"fallback_strategies": true,
|
||||
"performance_optimization": true,
|
||||
"learning_adaptation": true
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Runtime Configuration Loading
|
||||
```python
|
||||
class PreToolUseHook:
|
||||
def __init__(self):
|
||||
# Load hook-specific configuration
|
||||
self.hook_config = config_loader.get_hook_config('pre_tool_use')
|
||||
|
||||
# Load orchestrator configuration (YAML or fallback)
|
||||
try:
|
||||
self.orchestrator_config = config_loader.load_config('orchestrator')
|
||||
except FileNotFoundError:
|
||||
self.orchestrator_config = self.hook_config.get('configuration', {})
|
||||
|
||||
# Performance targets from configuration
|
||||
self.performance_target_ms = config_loader.get_hook_config(
|
||||
'pre_tool_use', 'performance_target_ms', 200
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Learning Integration
|
||||
|
||||
### Learning Data Collection
|
||||
**Operation Pattern Recording**:
|
||||
```python
|
||||
def record_tool_learning(context, tool_config):
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.OPERATION_PATTERN,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'tool_name': context['tool_name'],
|
||||
'mcp_servers_used': tool_config.get('mcp_integration', {}).get('servers', []),
|
||||
'execution_strategy': tool_config.get('execution_metadata', {}).get('intelligence_level'),
|
||||
'optimizations_applied': tool_config.get('performance_optimization', {}).get('optimizations', [])
|
||||
},
|
||||
effectiveness_score=0.8, # Updated after execution
|
||||
confidence_score=0.7,
|
||||
metadata={'hook': 'pre_tool_use', 'version': '1.0'}
|
||||
)
|
||||
```
|
||||
|
||||
### Adaptive Routing Enhancement
|
||||
**Learning-Based Routing Improvements**:
|
||||
- **User Preferences**: Learn individual user's tool and server preferences
|
||||
- **Project Patterns**: Adapt to project-specific optimal routing strategies
|
||||
- **Performance Optimization**: Route based on historical performance data
|
||||
- **Error Pattern Recognition**: Avoid routing strategies that historically failed
|
||||
|
||||
### Learning Scope Hierarchy
|
||||
```python
|
||||
Learning Scopes:
|
||||
1. Session Level: Real-time adaptation within current session
|
||||
2. User Level: Personal routing preferences across sessions
|
||||
3. Project Level: Project-specific optimization patterns
|
||||
4. Global Level: Framework-wide routing intelligence
|
||||
```
|
||||
|
||||
### Effectiveness Measurement
|
||||
```python
|
||||
Effectiveness Metrics:
|
||||
- execution_time: Actual vs estimated execution time
|
||||
- success_rate: Successful operation completion rate
|
||||
- quality_score: Output quality assessment
|
||||
- user_satisfaction: Implicit feedback from continued usage
|
||||
- resource_efficiency: Resource utilization optimization
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Performance Optimization
|
||||
|
||||
### Caching Strategies
|
||||
|
||||
#### Pattern Recognition Cache
|
||||
```python
|
||||
Cache Structure:
|
||||
- Key: hash(user_intent + tool_name + context_hash)
|
||||
- Value: routing_decision + confidence_score
|
||||
- TTL: 60 minutes for pattern stability
|
||||
- Size: 1000 entries with LRU eviction
|
||||
```
|
||||
|
||||
#### MCP Server Response Cache
|
||||
```python
|
||||
Cache Strategy:
|
||||
- Documentation lookups: 30 minutes TTL
|
||||
- Analysis results: Session-scoped cache
|
||||
- Pattern templates: 1 hour TTL
|
||||
- Server availability: 5 minutes TTL
|
||||
```
|
||||
|
||||
#### Performance Optimizations
|
||||
```python
|
||||
Optimization Techniques:
|
||||
1. Pre-computed Routing Tables: Common patterns pre-calculated
|
||||
2. Lazy Loading: Load components only when needed
|
||||
3. Parallel Analysis: Run pattern detection and MCP planning concurrently
|
||||
4. Result Reuse: Cache and reuse analysis results within session
|
||||
5. Intelligent Fallbacks: Fast fallback paths for common failure modes
|
||||
```
|
||||
|
||||
### Resource Management
|
||||
```python
|
||||
Resource Optimization:
|
||||
- Memory: Bounded caches with intelligent eviction
|
||||
- CPU: Parallel processing for independent operations
|
||||
- I/O: Batch operations where possible
|
||||
- Network: Connection pooling for MCP servers
|
||||
```
|
||||
|
||||
### Execution Time Optimization
|
||||
```python
|
||||
Time Budget Allocation:
|
||||
- Pattern Detection: 50ms (25%)
|
||||
- MCP Server Selection: 30ms (15%)
|
||||
- Configuration Generation: 40ms (20%)
|
||||
- Learning Integration: 20ms (10%)
|
||||
- Buffer/Safety Margin: 60ms (30%)
|
||||
Total Target: 200ms
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Integration with ORCHESTRATOR.md
|
||||
|
||||
### Pattern Matching Implementation
|
||||
The hook implements the ORCHESTRATOR.md pattern matching system:
|
||||
|
||||
```python
|
||||
# Quick Pattern Matching from ORCHESTRATOR.md
|
||||
pattern_mappings = {
|
||||
'ui_component': {
|
||||
'keywords': ['component', 'design', 'frontend', 'UI'],
|
||||
'mcp_server': 'magic',
|
||||
'persona': 'frontend'
|
||||
},
|
||||
'deep_analysis': {
|
||||
'keywords': ['architecture', 'complex', 'system-wide'],
|
||||
'mcp_server': 'sequential',
|
||||
'thinking_mode': 'think_hard'
|
||||
},
|
||||
'large_scope': {
|
||||
'keywords': ['many files', 'entire codebase'],
|
||||
'mcp_server': 'serena',
|
||||
'delegation': True
|
||||
},
|
||||
'symbol_operations': {
|
||||
'keywords': ['rename', 'refactor', 'extract', 'move'],
|
||||
'mcp_server': 'serena',
|
||||
'precision': 'lsp'
|
||||
},
|
||||
'pattern_edits': {
|
||||
'keywords': ['framework', 'style', 'cleanup'],
|
||||
'mcp_server': 'morphllm',
|
||||
'optimization': 'token'
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Resource Zone Implementation
|
||||
```python
|
||||
def get_resource_zone(resource_usage):
|
||||
if resource_usage <= 0.75:
|
||||
return 'green_zone' # Full capabilities
|
||||
elif resource_usage <= 0.85:
|
||||
return 'yellow_zone' # Efficiency mode
|
||||
else:
|
||||
return 'red_zone' # Essential operations only
|
||||
```
|
||||
|
||||
### Tool Selection Guide Integration
|
||||
The hook implements the ORCHESTRATOR.md tool selection guide:
|
||||
|
||||
```python
|
||||
def apply_orchestrator_routing(context, user_intent):
|
||||
"""Apply ORCHESTRATOR.md routing patterns"""
|
||||
|
||||
# MCP Server selection based on ORCHESTRATOR.md
|
||||
if any(word in user_intent.lower() for word in ['library', 'docs', 'framework']):
|
||||
return ['context7']
|
||||
|
||||
if any(word in user_intent.lower() for word in ['complex', 'analysis', 'debug']):
|
||||
return ['sequential']
|
||||
|
||||
if any(word in user_intent.lower() for word in ['component', 'ui', 'design']):
|
||||
return ['magic']
|
||||
|
||||
if context.get('file_count', 1) > 5:
|
||||
return ['serena']
|
||||
|
||||
# Default to native tools for simple operations
|
||||
return []
|
||||
```
|
||||
|
||||
### Quality Gate Integration
|
||||
```python
|
||||
Quality Gates Applied:
|
||||
- Step 1 (Syntax Validation): Tool parameter validation
|
||||
- Step 2 (Type Analysis): Context type checking and compatibility
|
||||
- Performance Monitoring: Real-time execution time tracking
|
||||
- Fallback Validation: Ensure fallback strategies maintain functionality
|
||||
```
|
||||
|
||||
### Auto-Activation Rules Implementation
|
||||
The hook implements ORCHESTRATOR.md auto-activation rules:
|
||||
|
||||
```python
|
||||
def apply_auto_activation_rules(context):
|
||||
"""Apply ORCHESTRATOR.md auto-activation patterns"""
|
||||
|
||||
activations = []
|
||||
|
||||
# Enable Sequential for complex operations
|
||||
if (context.get('complexity_score', 0) > 0.6 or
|
||||
context.get('requires_intelligence')):
|
||||
activations.append('sequential')
|
||||
|
||||
# Enable Serena for multi-file operations
|
||||
if (context.get('file_count', 1) > 5 or
|
||||
any(op in context.get('operation_sequence', []) for op in ['rename', 'extract'])):
|
||||
activations.append('serena')
|
||||
|
||||
# Enable delegation for large operations
|
||||
if (context.get('file_count', 1) > 3 or
|
||||
context.get('directory_count', 1) > 2):
|
||||
activations.append('delegation')
|
||||
|
||||
return activations
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Technical Implementation Details
|
||||
|
||||
### Core Architecture Components
|
||||
|
||||
#### 1. Framework Logic Integration
|
||||
```python
|
||||
from framework_logic import FrameworkLogic, OperationContext, OperationType, RiskLevel
|
||||
|
||||
# Provides SuperClaude framework intelligence
|
||||
self.framework_logic = FrameworkLogic()
|
||||
```
|
||||
|
||||
#### 2. Pattern Detection Engine
|
||||
```python
|
||||
from pattern_detection import PatternDetector, PatternMatch
|
||||
|
||||
# Analyzes patterns for routing decisions
|
||||
detection_result = self.pattern_detector.detect_patterns(
|
||||
user_intent, context, operation_data
|
||||
)
|
||||
```
|
||||
|
||||
#### 3. MCP Intelligence Coordination
|
||||
```python
|
||||
from mcp_intelligence import MCPIntelligence, MCPActivationPlan
|
||||
|
||||
# Creates optimal MCP server activation plans
|
||||
mcp_plan = self.mcp_intelligence.create_activation_plan(
|
||||
user_intent, context, operation_data
|
||||
)
|
||||
```
|
||||
|
||||
#### 4. Learning Engine Integration
|
||||
```python
|
||||
from learning_engine import LearningEngine
|
||||
|
||||
# Applies learned adaptations and records new patterns
|
||||
enhanced_routing = self.learning_engine.apply_adaptations(context, base_routing)
|
||||
```
|
||||
|
||||
### Error Handling Architecture
|
||||
```python
|
||||
Exception Handling Strategy:
|
||||
1. Catch all exceptions during routing analysis
|
||||
2. Log error with context for debugging
|
||||
3. Generate fallback configuration
|
||||
4. Preserve user intent and operation continuity
|
||||
5. Record error for learning and improvement
|
||||
```
|
||||
|
||||
### Performance Monitoring
|
||||
```python
|
||||
Performance Tracking:
|
||||
- Initialization time measurement
|
||||
- Per-operation execution time tracking
|
||||
- Target compliance validation (<200ms)
|
||||
- Efficiency score calculation
|
||||
- Resource utilization monitoring
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Example 1: Simple File Read
|
||||
```json
|
||||
Input Request:
|
||||
{
|
||||
"tool_name": "Read",
|
||||
"parameters": {"file_path": "/src/components/Button.tsx"},
|
||||
"user_intent": "read button component"
|
||||
}
|
||||
|
||||
Hook Enhancement:
|
||||
{
|
||||
"tool_name": "Read",
|
||||
"enhanced_mode": false,
|
||||
"mcp_integration": {"enabled": false},
|
||||
"execution_metadata": {
|
||||
"complexity_score": 0.0,
|
||||
"intelligence_level": "low"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Example 2: Complex Multi-File Analysis
|
||||
```json
|
||||
Input Request:
|
||||
{
|
||||
"tool_name": "Analyze",
|
||||
"parameters": {"directory": "/src/**/*.ts"},
|
||||
"user_intent": "analyze typescript architecture patterns"
|
||||
}
|
||||
|
||||
Hook Enhancement:
|
||||
{
|
||||
"tool_name": "Analyze",
|
||||
"enhanced_mode": true,
|
||||
"mcp_integration": {
|
||||
"enabled": true,
|
||||
"servers": ["sequential", "serena"],
|
||||
"coordination_strategy": "collaborative"
|
||||
},
|
||||
"performance_optimization": {
|
||||
"parallel_execution": true,
|
||||
"optimizations": ["parallel_file_processing"]
|
||||
},
|
||||
"execution_metadata": {
|
||||
"complexity_score": 0.75,
|
||||
"intelligence_level": "high"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Example 3: UI Component Generation
|
||||
```json
|
||||
Input Request:
|
||||
{
|
||||
"tool_name": "Generate",
|
||||
"parameters": {"component_type": "form"},
|
||||
"user_intent": "create login form component"
|
||||
}
|
||||
|
||||
Hook Enhancement:
|
||||
{
|
||||
"tool_name": "Generate",
|
||||
"enhanced_mode": true,
|
||||
"mcp_integration": {
|
||||
"enabled": true,
|
||||
"servers": ["magic", "context7"],
|
||||
"coordination_strategy": "sequential_handoff"
|
||||
},
|
||||
"build_operations": {
|
||||
"framework_integration": true,
|
||||
"component_generation": true,
|
||||
"quality_validation": true
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Monitoring and Debugging
|
||||
|
||||
### Performance Metrics
|
||||
```python
|
||||
Tracked Metrics:
|
||||
- routing_time_ms: Time spent in routing analysis
|
||||
- target_met: Boolean indicating <200ms compliance
|
||||
- efficiency_score: Overall routing effectiveness (0.0-1.0)
|
||||
- mcp_servers_activated: Count of MCP servers coordinated
|
||||
- optimizations_applied: List of performance optimizations
|
||||
- fallback_triggered: Boolean indicating fallback usage
|
||||
```
|
||||
|
||||
### Logging Integration
|
||||
```python
|
||||
Log Events:
|
||||
- Hook start: Tool name and parameters
|
||||
- Routing decisions: MCP server selection rationale
|
||||
- Execution strategy: Chosen execution approach
|
||||
- Performance metrics: Timing and efficiency data
|
||||
- Error events: Failures and fallback triggers
|
||||
- Hook completion: Success/failure status
|
||||
```
|
||||
|
||||
### Debug Information
|
||||
```python
|
||||
Debug Output:
|
||||
- Pattern detection results with confidence scores
|
||||
- MCP server capability matching analysis
|
||||
- Optimization opportunity identification
|
||||
- Learning adaptation application
|
||||
- Configuration generation process
|
||||
- Performance target validation
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **ORCHESTRATOR.md**: Core routing patterns and coordination strategies
|
||||
- **Framework Integration**: Quality gates and mode coordination
|
||||
- **MCP Server Documentation**: Individual server capabilities and integration
|
||||
- **Learning Engine**: Adaptive intelligence and pattern recognition
|
||||
- **Performance Monitoring**: System-wide performance tracking and optimization
|
||||
|
||||
---
|
||||
|
||||
*The pre_tool_use hook serves as the intelligent routing engine for the SuperClaude framework, ensuring optimal tool selection, MCP server coordination, and performance optimization for every operation within Claude Code.*
|
||||
@@ -1,481 +0,0 @@
|
||||
# Session Start Hook Technical Documentation
|
||||
|
||||
## Purpose
|
||||
|
||||
The session_start hook initializes Claude Code sessions with SuperClaude framework intelligence. It analyzes project context, detects patterns, and configures appropriate modes and MCP servers based on the actual session requirements.
|
||||
|
||||
**Core Implementation**: A 704-line Python implementation that performs lazy loading, pattern detection, MCP intelligence routing, compression configuration, and learning adaptations with a target execution time of <50ms.
|
||||
|
||||
## Execution Context
|
||||
|
||||
The session_start hook runs automatically at the beginning of every Claude Code session. According to `settings.json`, it has a 10-second timeout and executes via: `python3 ~/.claude/hooks/session_start.py`
|
||||
|
||||
**Actual Execution Flow:**
|
||||
1. Receives session data from Claude Code via stdin (JSON)
|
||||
2. Initializes SessionStartHook class with lazy loading of components
|
||||
3. Processes session initialization with project analysis and pattern detection
|
||||
4. Outputs enhanced session configuration via stdout (JSON)
|
||||
5. Falls back gracefully on errors with basic session configuration
|
||||
|
||||
**Performance**: Target <50ms execution time (configurable via superclaude-config.json)
|
||||
|
||||
## Performance Target
|
||||
|
||||
**Target: <50ms execution time**
|
||||
|
||||
This aggressive performance target is critical for maintaining seamless user experience during session initialization. The hook must complete its comprehensive analysis and configuration within this window to avoid perceptible delays.
|
||||
|
||||
**Why 50ms Matters:**
|
||||
- **User Experience**: Sub-perceptible delay maintains natural interaction flow
|
||||
- **Session Efficiency**: Fast bootstrap enables immediate intelligent behavior
|
||||
- **Resource Optimization**: Efficient initialization preserves compute budget for actual work
|
||||
- **Learning System**: Quick analysis allows for real-time adaptation without latency
|
||||
|
||||
**Performance Monitoring:**
|
||||
- Real-time execution time tracking with detailed metrics
|
||||
- Efficiency score calculation based on target achievement
|
||||
- Performance degradation alerts and optimization recommendations
|
||||
- Historical performance analysis for continuous improvement
|
||||
|
||||
## Core Features
|
||||
|
||||
### 1. Project Structure Analysis
|
||||
|
||||
**Implementation**: The `_analyze_project_structure()` method performs quick project analysis by examining key files and directories.
|
||||
|
||||
**What it actually does:**
|
||||
- Enumerates up to 100 files for performance (limits via `files[:100]`)
|
||||
- Detects project type by checking for manifest files:
|
||||
- `package.json` → nodejs
|
||||
- `pyproject.toml` or `setup.py` → python
|
||||
- `Cargo.toml` → rust
|
||||
- `go.mod` → go
|
||||
- Identifies frameworks by parsing package.json dependencies (React, Vue, Angular, Express)
|
||||
- Checks for test directories and production indicators (Dockerfile, .env.production)
|
||||
- Returns analysis dict with project_type, framework_detected, has_tests, is_production, etc.
|
||||
|
||||
### 2. User Intent Analysis and Mode Detection
|
||||
|
||||
**Implementation**: The `_analyze_user_intent()` method examines user input to determine operation type and complexity.
|
||||
|
||||
**What it actually does:**
|
||||
- Analyzes user input text for operation keywords:
|
||||
- "build", "create", "implement" → BUILD operation (complexity +0.3)
|
||||
- "fix", "debug", "troubleshoot" → ANALYZE operation (complexity +0.2)
|
||||
- "refactor", "restructure" → REFACTOR operation (complexity +0.4)
|
||||
- "test", "validate" → TEST operation (complexity +0.1)
|
||||
- Detects brainstorming needs via keywords: "not sure", "thinking about", "maybe", "brainstorm"
|
||||
- Calculates complexity score (0.0-1.0) based on operation type and complexity indicators
|
||||
- The `_activate_intelligent_modes()` method activates modes based on detected patterns:
|
||||
- brainstorming mode if `brainstorming_likely` is True
|
||||
- task_management mode if recommended by pattern detection
|
||||
- token_efficiency mode if recommended by pattern detection
|
||||
|
||||
### 3. MCP Server Configuration
|
||||
|
||||
**Implementation**: The `_create_mcp_activation_plan()` and `_configure_mcp_servers()` methods determine which MCP servers to activate.
|
||||
|
||||
**What it actually does:**
|
||||
- Uses MCPIntelligence class to create activation plans based on:
|
||||
- User intent analysis
|
||||
- Context characteristics (file count, complexity score, operation type)
|
||||
- Project analysis results
|
||||
- Returns MCP plan with:
|
||||
- `servers_to_activate`: List of servers to enable
|
||||
- `activation_order`: Sequence for server activation
|
||||
- `coordination_strategy`: How servers should work together
|
||||
- `estimated_cost_ms`: Performance impact estimate
|
||||
- `fallback_strategy`: Backup plan if servers fail
|
||||
|
||||
### 4. Learning Engine Integration
|
||||
|
||||
**Implementation**: The `_apply_learning_adaptations()` method applies learned patterns to improve session configuration.
|
||||
|
||||
**What it actually does:**
|
||||
- Uses LearningEngine (initialized with `~/.claude/cache` directory) to:
|
||||
- Apply previous adaptations to current recommendations
|
||||
- Store user preferences (preferred tools per operation type)
|
||||
- Update project-specific information (project type, framework)
|
||||
- Record learning events for future sessions
|
||||
- The `_record_session_learning()` method stores session initialization patterns for continuous improvement
|
||||
|
||||
### 5. Lazy Loading Architecture
|
||||
|
||||
**Implementation**: The hook uses lazy loading via Python properties to minimize initialization time.
|
||||
|
||||
**What it actually does:**
|
||||
- Core components are loaded immediately: `FrameworkLogic()`
|
||||
- Other components use lazy loading properties:
|
||||
- `pattern_detector` property loads `PatternDetector()` only when first accessed
|
||||
- `mcp_intelligence` property loads `MCPIntelligence()` only when needed
|
||||
- `compression_engine` property loads `CompressionEngine()` only when used
|
||||
- `learning_engine` property loads `LearningEngine()` only when required
|
||||
- This reduces initialization overhead and improves the <50ms performance target
|
||||
|
||||
## Implementation Details
|
||||
|
||||
### Architecture Pattern
|
||||
|
||||
The session_start hook implements a layered architecture with clear separation of concerns:
|
||||
|
||||
**Layer 1: Context Extraction**
|
||||
```python
|
||||
def _extract_session_context(self, session_data: dict) -> dict:
|
||||
# Enriches basic session data with project analysis and user intent detection
|
||||
context = {
|
||||
'session_id': session_data.get('session_id', 'unknown'),
|
||||
'project_path': session_data.get('project_path', ''),
|
||||
'user_input': session_data.get('user_input', ''),
|
||||
# ... additional context enrichment
|
||||
}
|
||||
```
|
||||
|
||||
**Layer 2: Intelligence Analysis**
|
||||
```python
|
||||
def _detect_session_patterns(self, context: dict) -> dict:
|
||||
# Pattern detection using SuperClaude's pattern recognition algorithms
|
||||
detection_result = self.pattern_detector.detect_patterns(
|
||||
context.get('user_input', ''),
|
||||
context,
|
||||
operation_data
|
||||
)
|
||||
```
|
||||
|
||||
**Layer 3: Configuration Generation**
|
||||
```python
|
||||
def _generate_session_config(self, context: dict, recommendations: dict,
|
||||
mcp_plan: dict, compression_config: dict) -> dict:
|
||||
# Comprehensive session configuration assembly
|
||||
return comprehensive_session_configuration
|
||||
```
|
||||
|
||||
### Error Handling Strategy
|
||||
|
||||
**Graceful Degradation**: The hook implements comprehensive error handling that ensures session functionality even when intelligence systems fail.
|
||||
|
||||
```python
|
||||
def initialize_session(self, session_context: dict) -> dict:
|
||||
try:
|
||||
# Full intelligence initialization
|
||||
return enhanced_session_config
|
||||
except Exception as e:
|
||||
# Graceful fallback
|
||||
return self._create_fallback_session_config(session_context, str(e))
|
||||
```
|
||||
|
||||
**Fallback Configuration:**
|
||||
- Disables SuperClaude intelligence features
|
||||
- Maintains basic Claude Code functionality
|
||||
- Provides error context for debugging
|
||||
- Enables recovery for subsequent sessions
|
||||
|
||||
### Performance Measurement
|
||||
|
||||
**Real-Time Metrics:**
|
||||
```python
|
||||
# Performance tracking integration
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
session_config['performance_metrics'] = {
|
||||
'initialization_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'efficiency_score': self._calculate_initialization_efficiency(execution_time)
|
||||
}
|
||||
```
|
||||
|
||||
## Configuration
|
||||
|
||||
### Hook-Specific Configuration (superclaude-config.json)
|
||||
|
||||
```json
|
||||
{
|
||||
"hook_configurations": {
|
||||
"session_start": {
|
||||
"enabled": true,
|
||||
"description": "SESSION_LIFECYCLE + FLAGS logic with intelligent bootstrap",
|
||||
"performance_target_ms": 50,
|
||||
"features": [
|
||||
"smart_project_context_loading",
|
||||
"automatic_mode_detection",
|
||||
"mcp_server_intelligence_routing",
|
||||
"user_preference_adaptation",
|
||||
"performance_optimized_initialization"
|
||||
],
|
||||
"configuration": {
|
||||
"auto_project_detection": true,
|
||||
"framework_exclusion_enabled": true,
|
||||
"intelligence_activation": true,
|
||||
"learning_integration": true,
|
||||
"performance_monitoring": true
|
||||
},
|
||||
"error_handling": {
|
||||
"graceful_fallback": true,
|
||||
"preserve_user_context": true,
|
||||
"error_learning": true
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Configuration Loading Strategy
|
||||
|
||||
**Primary Configuration Source**: superclaude-config.json hook_configurations.session_start
|
||||
**Fallback Strategy**: YAML configuration files in config/ directory
|
||||
**Runtime Adaptation**: Learning engine modifications applied during execution
|
||||
|
||||
```python
|
||||
# Configuration loading with fallback
|
||||
self.hook_config = config_loader.get_hook_config('session_start')
|
||||
|
||||
try:
|
||||
self.session_config = config_loader.load_config('session')
|
||||
except FileNotFoundError:
|
||||
self.session_config = self.hook_config.get('configuration', {})
|
||||
```
|
||||
|
||||
## Pattern Loading Strategy
|
||||
|
||||
### Minimal Pattern Bootstrap
|
||||
|
||||
The hook implements a strategic pattern loading approach that loads only essential patterns during initialization to meet the 50ms performance target.
|
||||
|
||||
**Pattern Loading Phases:**
|
||||
|
||||
**Phase 1: Critical Patterns (Target: 3-5KB)**
|
||||
- Core operation type detection patterns
|
||||
- Basic project structure recognition
|
||||
- Essential mode activation triggers
|
||||
- Primary MCP server routing logic
|
||||
|
||||
**Phase 2: Context-Specific Patterns (Lazy Loaded)**
|
||||
- Framework-specific intelligence patterns
|
||||
- Advanced optimization strategies
|
||||
- Historical learning adaptations
|
||||
- Complex coordination algorithms
|
||||
|
||||
**Implementation Strategy:**
|
||||
```python
|
||||
def _detect_session_patterns(self, context: dict) -> dict:
|
||||
# Load minimal patterns for fast detection
|
||||
detection_result = self.pattern_detector.detect_patterns(
|
||||
context.get('user_input', ''),
|
||||
context,
|
||||
operation_data # Contains only essential pattern data
|
||||
)
|
||||
```
|
||||
|
||||
**Pattern Optimization Techniques:**
|
||||
- **Compressed Pattern Storage**: Use efficient data structures for pattern representation
|
||||
- **Selective Pattern Loading**: Load only patterns relevant to detected project type
|
||||
- **Cached Pattern Results**: Reuse pattern analysis for similar contexts
|
||||
- **Progressive Pattern Enhancement**: Enable additional patterns as session progresses
|
||||
|
||||
## Shared Modules Used
|
||||
|
||||
### framework_logic.py
|
||||
|
||||
**Purpose**: Provides SuperClaude framework decision-making capabilities.
|
||||
|
||||
**Used in session_start.py:**
|
||||
- `FrameworkLogic` class for quality gate configuration
|
||||
- `OperationContext` dataclass for structured context management
|
||||
- `OperationType` enum for operation classification (READ, WRITE, BUILD, etc.)
|
||||
- `RiskLevel` enum for risk assessment
|
||||
- Used in `_configure_quality_gates()` method to determine appropriate quality gates based on operation context
|
||||
|
||||
### pattern_detection.py
|
||||
|
||||
**Purpose**: Analyzes patterns in user input and context for intelligent routing.
|
||||
|
||||
**Used in session_start.py:**
|
||||
- `PatternDetector` class (lazy loaded)
|
||||
- `detect_patterns()` method for analyzing user intent, context, and operation data
|
||||
- Returns pattern matches, recommended modes, recommended MCP servers, and confidence scores
|
||||
|
||||
### mcp_intelligence.py
|
||||
|
||||
**Purpose**: Provides MCP server activation planning and coordination strategies.
|
||||
|
||||
**Used in session_start.py:**
|
||||
- `MCPIntelligence` class (lazy loaded)
|
||||
- `create_activation_plan()` method for determining optimal MCP server coordination
|
||||
- Returns activation plans with servers, order, cost estimates, and coordination strategies
|
||||
|
||||
### compression_engine.py
|
||||
|
||||
**Purpose**: Handles compression strategy selection for token efficiency.
|
||||
|
||||
**Used in session_start.py:**
|
||||
- `CompressionEngine` class (lazy loaded)
|
||||
- `determine_compression_level()` method for context-based compression decisions
|
||||
- Used in `_configure_compression()` to set session compression strategy
|
||||
|
||||
### learning_engine.py
|
||||
|
||||
**Purpose**: Provides learning and adaptation capabilities for continuous improvement.
|
||||
|
||||
**Used in session_start.py:**
|
||||
- `LearningEngine` class (lazy loaded, initialized with `~/.claude/cache` directory)
|
||||
- `apply_adaptations()` method for applying learned patterns
|
||||
- `record_learning_event()` method for storing session initialization data
|
||||
- `update_project_info()` and preference tracking methods
|
||||
|
||||
### yaml_loader.py
|
||||
|
||||
**Purpose**: Configuration loading with fallback strategies.
|
||||
|
||||
**Used in session_start.py:**
|
||||
- `config_loader.get_hook_config()` for hook-specific configuration
|
||||
- `config_loader.load_config()` for YAML configuration files with FileNotFoundError handling
|
||||
- Fallback to hook configuration when YAML files are missing
|
||||
|
||||
### logger.py
|
||||
|
||||
**Purpose**: Structured logging for hook execution tracking.
|
||||
|
||||
**Used in session_start.py:**
|
||||
- `log_hook_start()` and `log_hook_end()` for execution timing
|
||||
- `log_decision()` for mode activation and MCP server selection decisions
|
||||
- `log_error()` for error context preservation
|
||||
|
||||
## Error Handling
|
||||
|
||||
**Implementation**: The main `initialize_session()` method includes comprehensive error handling with graceful fallback.
|
||||
|
||||
**What actually happens on errors:**
|
||||
|
||||
1. **Exception Handling**: All errors are caught in the main try-except block
|
||||
2. **Error Logging**: Errors are logged via `log_error()` with context
|
||||
3. **Fallback Configuration**: `_create_fallback_session_config()` returns:
|
||||
```python
|
||||
{
|
||||
'session_id': session_context.get('session_id', 'unknown'),
|
||||
'superclaude_enabled': False,
|
||||
'fallback_mode': True,
|
||||
'error': error,
|
||||
'basic_config': {
|
||||
'compression_level': 'minimal',
|
||||
'mcp_servers_enabled': False,
|
||||
'learning_disabled': True
|
||||
}
|
||||
}
|
||||
```
|
||||
4. **Session Continuity**: Basic Claude Code functionality is preserved even when SuperClaude features fail
|
||||
|
||||
### Error Learning Integration
|
||||
|
||||
**Error Pattern Recognition:**
|
||||
```python
|
||||
def _record_session_learning(self, context: dict, session_config: dict):
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.OPERATION_PATTERN,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
session_config,
|
||||
success_score,
|
||||
confidence_score,
|
||||
metadata
|
||||
)
|
||||
```
|
||||
|
||||
**Recovery Optimization:**
|
||||
- Errors are analyzed for pattern recognition
|
||||
- Successful recovery strategies are learned and applied
|
||||
- Error frequency analysis drives system improvements
|
||||
- Proactive error prevention based on historical patterns
|
||||
|
||||
## Session Context Enhancement
|
||||
|
||||
### Context Enrichment Process
|
||||
|
||||
The session_start hook transforms basic Claude Code session data into rich, intelligent context that enables advanced SuperClaude behaviors throughout the session.
|
||||
|
||||
**Input Context (Basic):**
|
||||
- session_id: Basic session identifier
|
||||
- project_path: File system path
|
||||
- user_input: Initial user request
|
||||
- conversation_length: Basic metrics
|
||||
|
||||
**Enhanced Context (SuperClaude):**
|
||||
- Project analysis with technology stack detection
|
||||
- User intent analysis with complexity scoring
|
||||
- Mode activation recommendations
|
||||
- MCP server routing plans
|
||||
- Performance optimization settings
|
||||
- Learning adaptations from previous sessions
|
||||
|
||||
### Context Preservation Strategy
|
||||
|
||||
**Session Configuration Generation:**
|
||||
```python
|
||||
def _generate_session_config(self, context: dict, recommendations: dict,
|
||||
mcp_plan: dict, compression_config: dict) -> dict:
|
||||
return {
|
||||
'session_id': context['session_id'],
|
||||
'superclaude_enabled': True,
|
||||
'active_modes': recommendations.get('recommended_modes', []),
|
||||
'mcp_servers': mcp_plan,
|
||||
'compression': compression_config,
|
||||
'performance': performance_config,
|
||||
'learning': learning_config,
|
||||
'context': context_preservation,
|
||||
'quality_gates': quality_gate_config
|
||||
}
|
||||
```
|
||||
|
||||
**Context Utilization Throughout Session:**
|
||||
- **MCP Server Routing**: Uses project analysis for intelligent server selection
|
||||
- **Mode Activation**: Applies detected patterns for behavioral mode triggers
|
||||
- **Performance Optimization**: Uses complexity analysis for resource allocation
|
||||
- **Quality Gates**: Applies context-appropriate validation levels
|
||||
- **Learning Integration**: Captures session patterns for future improvement
|
||||
|
||||
### Long-Term Context Evolution
|
||||
|
||||
**Cross-Session Learning:**
|
||||
- Session patterns are analyzed and stored for future sessions
|
||||
- User preferences are extracted and applied automatically
|
||||
- Project-specific optimizations are learned and reused
|
||||
- Error patterns are identified and proactively avoided
|
||||
|
||||
**Context Continuity:**
|
||||
- Enhanced context from session_start provides foundation for entire session
|
||||
- Context elements influence all subsequent hook behaviors
|
||||
- Learning from current session feeds into future session_start executions
|
||||
- Continuous improvement cycle maintains and enhances context quality over time
|
||||
|
||||
## Integration Points
|
||||
|
||||
### SuperClaude Framework Integration
|
||||
|
||||
**SESSION_LIFECYCLE.md Compliance:**
|
||||
- Implements initialization phase of session lifecycle pattern
|
||||
- Provides foundation for checkpoint and persistence systems
|
||||
- Enables context continuity across session boundaries
|
||||
|
||||
**FLAGS.md Logic Implementation:**
|
||||
- Automatically detects and applies appropriate flag combinations
|
||||
- Implements flag precedence and conflict resolution
|
||||
- Provides intelligent default flag selection based on context
|
||||
|
||||
**ORCHESTRATOR.md Pattern Integration:**
|
||||
- Implements intelligent routing patterns for MCP server selection
|
||||
- Applies resource management strategies during initialization
|
||||
- Establishes foundation for quality gate enforcement
|
||||
|
||||
### Hook Ecosystem Coordination
|
||||
|
||||
**Downstream Hook Preparation:**
|
||||
- pre_tool_use: Receives enhanced context for intelligent tool routing
|
||||
- post_tool_use: Gets quality gate configuration for validation
|
||||
- pre_compact: Receives compression configuration for optimization
|
||||
- stop: Gets learning configuration for session analytics
|
||||
|
||||
**Cross-Hook Data Flow:**
|
||||
```
|
||||
session_start → Enhanced Context → All Subsequent Hooks
|
||||
↓
|
||||
Learning Engine ← Session Analytics ← stop Hook
|
||||
```
|
||||
|
||||
This comprehensive technical documentation provides a complete understanding of how the session_start hook operates as the foundational intelligence layer of the SuperClaude-Lite framework, transforming basic Claude Code sessions into intelligent, adaptive, and optimized experiences.
|
||||
@@ -1,372 +0,0 @@
|
||||
# Stop Hook Documentation
|
||||
|
||||
## Purpose
|
||||
|
||||
The `stop` hook provides session analytics and persistence when Claude Code sessions end. It implements session summarization, learning consolidation, and data storage for continuous framework improvement.
|
||||
|
||||
**Core Implementation**: Analyzes complete session history, consolidates learning events, generates performance metrics, and persists session data for future analysis with a target execution time of <200ms.
|
||||
|
||||
## Execution Context
|
||||
|
||||
The stop hook runs at Claude Code session termination. According to `settings.json`, it has a 15-second timeout and executes via: `python3 ~/.claude/hooks/stop.py`
|
||||
|
||||
**Actual Execution Flow:**
|
||||
1. Receives session termination data via stdin (JSON)
|
||||
2. Initializes StopHook class with analytics and learning components
|
||||
3. Analyzes complete session history and performance data
|
||||
4. Consolidates learning events and generates session insights
|
||||
5. Persists session data and analytics for future reference
|
||||
6. Outputs session summary and analytics via stdout (JSON)
|
||||
|
||||
## Performance Target
|
||||
|
||||
**Primary Target**: <200ms execution time for complete session analytics
|
||||
|
||||
### Performance Benchmarks
|
||||
- **Initialization**: <50ms for component loading
|
||||
- **Analytics Generation**: <100ms for comprehensive analysis
|
||||
- **Session Persistence**: <30ms for data storage
|
||||
- **Learning Consolidation**: <20ms for learning events processing
|
||||
- **Total Processing**: <200ms end-to-end execution
|
||||
|
||||
### Performance Monitoring
|
||||
```python
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
target_met = execution_time < self.performance_target_ms
|
||||
```
|
||||
|
||||
## Session Analytics
|
||||
|
||||
### Comprehensive Performance Metrics
|
||||
|
||||
#### Overall Score Calculation
|
||||
```python
|
||||
overall_score = (
|
||||
productivity * 0.4 +
|
||||
effectiveness * 0.4 +
|
||||
(1.0 - error_rate) * 0.2
|
||||
)
|
||||
```
|
||||
|
||||
#### Performance Categories
|
||||
- **Productivity Score**: Operations per minute, completion rates
|
||||
- **Quality Score**: Error rates, operation success rates
|
||||
- **Intelligence Utilization**: MCP server usage, SuperClaude effectiveness
|
||||
- **Resource Efficiency**: Memory, CPU, token usage optimization
|
||||
- **User Satisfaction Estimate**: Derived from session patterns and outcomes
|
||||
|
||||
#### Analytics Components
|
||||
```yaml
|
||||
performance_metrics:
|
||||
overall_score: 0.85 # Combined performance indicator
|
||||
productivity_score: 0.78 # Operations efficiency
|
||||
quality_score: 0.92 # Error-free execution rate
|
||||
efficiency_score: 0.84 # Resource utilization
|
||||
satisfaction_estimate: 0.87 # Estimated user satisfaction
|
||||
```
|
||||
|
||||
### Bottleneck Identification
|
||||
- **High Error Rate**: >20% operation failure rate
|
||||
- **Low Productivity**: <50% productivity score
|
||||
- **Underutilized Intelligence**: <30% MCP usage with SuperClaude enabled
|
||||
- **Resource Constraints**: Memory/CPU/token usage optimization opportunities
|
||||
|
||||
### Optimization Opportunities Detection
|
||||
- **Tool Usage Optimization**: >10 unique tools suggest coordination improvement
|
||||
- **MCP Server Coordination**: <2 servers with >5 operations suggest better orchestration
|
||||
- **Workflow Enhancement**: Pattern analysis for efficiency improvements
|
||||
|
||||
## Learning Consolidation
|
||||
|
||||
### Learning Events Processing
|
||||
The hook consolidates all learning events generated during the session:
|
||||
|
||||
```python
|
||||
def _consolidate_learning_events(self, context: dict) -> dict:
|
||||
# Generate learning insights from session
|
||||
insights = self.learning_engine.generate_learning_insights()
|
||||
|
||||
# Session-specific learning metrics
|
||||
session_learning = {
|
||||
'session_effectiveness': context.get('superclaude_effectiveness', 0),
|
||||
'performance_score': context.get('session_productivity', 0),
|
||||
'mcp_coordination_effectiveness': min(context.get('mcp_usage_ratio', 0) * 2, 1.0),
|
||||
'error_recovery_success': 1.0 - context.get('error_rate', 0)
|
||||
}
|
||||
```
|
||||
|
||||
### Learning Categories
|
||||
- **Effectiveness Feedback**: Session performance patterns
|
||||
- **User Preferences**: Interaction and usage patterns
|
||||
- **Technical Patterns**: Tool usage and coordination effectiveness
|
||||
- **Error Recovery**: Success patterns for error handling
|
||||
|
||||
### Adaptation Creation
|
||||
- **Session-Level Adaptations**: Immediate session pattern learning
|
||||
- **User-Level Adaptations**: Long-term preference learning
|
||||
- **Technical Adaptations**: Tool and workflow optimization patterns
|
||||
|
||||
## Session Persistence
|
||||
|
||||
### Intelligent Storage Strategy
|
||||
|
||||
#### Data Classification
|
||||
- **Session Analytics**: Complete performance and effectiveness data
|
||||
- **Learning Events**: Consolidated learning insights and adaptations
|
||||
- **Context Data**: Session operational context and metadata
|
||||
- **Recommendations**: Generated suggestions for future sessions
|
||||
|
||||
#### Compression Logic
|
||||
```python
|
||||
# Apply compression for large session data
|
||||
if len(analytics_data) > 10000: # 10KB threshold
|
||||
compression_result = self.compression_engine.compress_content(
|
||||
analytics_data,
|
||||
context,
|
||||
{'content_type': 'session_data'}
|
||||
)
|
||||
```
|
||||
|
||||
#### Storage Optimization
|
||||
- **Session Cleanup**: Maintains 50 most recent sessions
|
||||
- **Automatic Pruning**: Removes sessions older than retention policy
|
||||
- **Compression**: Applied to sessions >10KB for storage efficiency
|
||||
|
||||
### Persistence Results
|
||||
```yaml
|
||||
persistence_result:
|
||||
persistence_enabled: true
|
||||
session_data_saved: true
|
||||
analytics_saved: true
|
||||
learning_data_saved: true
|
||||
compression_applied: true
|
||||
compression_ratio: 0.65
|
||||
storage_optimized: true
|
||||
```
|
||||
|
||||
## Recommendations Generation
|
||||
|
||||
### Performance Improvements
|
||||
Generated when overall score <70%:
|
||||
- Focus on reducing error rate through validation
|
||||
- Enable more SuperClaude intelligence features
|
||||
- Optimize tool selection and usage patterns
|
||||
|
||||
### SuperClaude Optimizations
|
||||
Based on framework effectiveness analysis:
|
||||
- **Low Effectiveness (<60%)**: Enable more MCP servers, use delegation features
|
||||
- **Disabled Framework**: Recommend SuperClaude enablement for productivity
|
||||
- **Underutilization**: Activate compression and intelligence features
|
||||
|
||||
### Learning Suggestions
|
||||
- **Low Learning Events (<3)**: Engage with more complex operations
|
||||
- **Pattern Recognition**: Suggestions based on successful session patterns
|
||||
- **Workflow Enhancement**: Recommendations for process improvements
|
||||
|
||||
### Workflow Enhancements
|
||||
Based on error patterns and efficiency analysis:
|
||||
- **High Error Rate (>10%)**: Use validation hooks, enable pre-tool intelligence
|
||||
- **Resource Optimization**: Memory, CPU, token usage improvements
|
||||
- **Coordination Enhancement**: Better MCP server and tool coordination
|
||||
|
||||
## Configuration
|
||||
|
||||
### Hook Configuration
|
||||
Loaded from `superclaude-config.json` hook configuration:
|
||||
|
||||
```yaml
|
||||
stop_hook:
|
||||
performance_target_ms: 200
|
||||
analytics:
|
||||
comprehensive_metrics: true
|
||||
learning_consolidation: true
|
||||
recommendation_generation: true
|
||||
persistence:
|
||||
enabled: true
|
||||
compression_threshold_bytes: 10000
|
||||
session_retention_count: 50
|
||||
learning:
|
||||
session_adaptations: true
|
||||
user_preference_tracking: true
|
||||
technical_pattern_learning: true
|
||||
```
|
||||
|
||||
### Session Configuration
|
||||
Falls back to session.yaml configuration when available:
|
||||
|
||||
```yaml
|
||||
session:
|
||||
analytics_enabled: true
|
||||
learning_consolidation: true
|
||||
performance_tracking: true
|
||||
recommendation_generation: true
|
||||
persistence_optimization: true
|
||||
```
|
||||
|
||||
## Integration with /sc:save
|
||||
|
||||
### Command Implementation
|
||||
The Stop Hook directly implements the `/sc:save` command logic:
|
||||
|
||||
#### Core /sc:save Features
|
||||
- **Session Analytics**: Complete session performance analysis
|
||||
- **Learning Consolidation**: All learning events processed and stored
|
||||
- **Intelligent Persistence**: Session data saved with optimization
|
||||
- **Recommendation Generation**: Actionable suggestions for improvement
|
||||
- **Performance Tracking**: <200ms execution time monitoring
|
||||
|
||||
#### /sc:save Workflow Integration
|
||||
```python
|
||||
def process_session_stop(self, session_data: dict) -> dict:
|
||||
# 1. Extract session context
|
||||
context = self._extract_session_context(session_data)
|
||||
|
||||
# 2. Analyze session performance (/sc:save analytics)
|
||||
performance_analysis = self._analyze_session_performance(context)
|
||||
|
||||
# 3. Consolidate learning events (/sc:save learning)
|
||||
learning_consolidation = self._consolidate_learning_events(context)
|
||||
|
||||
# 4. Generate session analytics (/sc:save metrics)
|
||||
session_analytics = self._generate_session_analytics(...)
|
||||
|
||||
# 5. Perform session persistence (/sc:save storage)
|
||||
persistence_result = self._perform_session_persistence(...)
|
||||
|
||||
# 6. Generate recommendations (/sc:save recommendations)
|
||||
recommendations = self._generate_recommendations(...)
|
||||
```
|
||||
|
||||
### /sc:save Output Format
|
||||
```yaml
|
||||
session_report:
|
||||
session_id: "session_2025-01-31_14-30-00"
|
||||
session_completed: true
|
||||
completion_timestamp: 1704110400
|
||||
|
||||
analytics:
|
||||
session_summary: {...}
|
||||
performance_metrics: {...}
|
||||
superclaude_effectiveness: {...}
|
||||
quality_analysis: {...}
|
||||
learning_summary: {...}
|
||||
|
||||
persistence:
|
||||
persistence_enabled: true
|
||||
analytics_saved: true
|
||||
compression_applied: true
|
||||
|
||||
recommendations:
|
||||
performance_improvements: [...]
|
||||
superclaude_optimizations: [...]
|
||||
learning_suggestions: [...]
|
||||
workflow_enhancements: [...]
|
||||
```
|
||||
|
||||
## Quality Assessment
|
||||
|
||||
### Session Success Criteria
|
||||
A session is considered successful when:
|
||||
- **Overall Score**: >60% performance score
|
||||
- **SuperClaude Effectiveness**: >60% when framework enabled
|
||||
- **Learning Achievement**: >0 insights generated
|
||||
- **Recommendations**: Actionable suggestions provided
|
||||
|
||||
### Quality Metrics
|
||||
```yaml
|
||||
quality_analysis:
|
||||
error_rate: 0.05 # 5% error rate
|
||||
operation_success_rate: 0.95 # 95% success rate
|
||||
bottlenecks: ["low_productivity"] # Identified issues
|
||||
optimization_opportunities: [...] # Improvement areas
|
||||
```
|
||||
|
||||
### Success Indicators
|
||||
- **Session Success**: `overall_score > 0.6`
|
||||
- **SuperClaude Effective**: `effectiveness_score > 0.6`
|
||||
- **Learning Achieved**: `insights_generated > 0`
|
||||
- **Recommendations Generated**: `total_recommendations > 0`
|
||||
|
||||
### User Satisfaction Estimation
|
||||
```python
|
||||
def _estimate_user_satisfaction(self, context: dict) -> float:
|
||||
satisfaction_factors = []
|
||||
|
||||
# Error rate impact
|
||||
satisfaction_factors.append(1.0 - error_rate)
|
||||
|
||||
# Productivity impact
|
||||
satisfaction_factors.append(productivity)
|
||||
|
||||
# SuperClaude effectiveness impact
|
||||
if superclaude_enabled:
|
||||
satisfaction_factors.append(effectiveness)
|
||||
|
||||
# Session duration optimization (15-60 minutes optimal)
|
||||
satisfaction_factors.append(duration_satisfaction)
|
||||
|
||||
return statistics.mean(satisfaction_factors)
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
### Graceful Degradation
|
||||
When errors occur during hook execution:
|
||||
|
||||
```python
|
||||
except Exception as e:
|
||||
log_error("stop", str(e), {"session_data": session_data})
|
||||
return self._create_fallback_report(session_data, str(e))
|
||||
```
|
||||
|
||||
### Fallback Reporting
|
||||
```yaml
|
||||
fallback_report:
|
||||
session_completed: false
|
||||
error: "Analysis engine failure"
|
||||
fallback_mode: true
|
||||
analytics:
|
||||
performance_metrics:
|
||||
overall_score: 0.0
|
||||
persistence:
|
||||
persistence_enabled: false
|
||||
```
|
||||
|
||||
### Recovery Strategies
|
||||
- **Analytics Failure**: Provide basic session summary
|
||||
- **Persistence Failure**: Continue with recommendations generation
|
||||
- **Learning Engine Error**: Skip learning consolidation, continue with core analytics
|
||||
- **Complete Failure**: Return minimal session completion report
|
||||
|
||||
## Performance Optimization
|
||||
|
||||
### Efficiency Strategies
|
||||
- **Lazy Loading**: Components initialized only when needed
|
||||
- **Batch Processing**: Multiple analytics operations combined
|
||||
- **Compression**: Large session data automatically compressed
|
||||
- **Caching**: Learning insights cached for reuse
|
||||
|
||||
### Resource Management
|
||||
- **Memory Optimization**: Session cleanup after processing
|
||||
- **Storage Efficiency**: Old sessions automatically pruned
|
||||
- **Processing Time**: <200ms target with continuous monitoring
|
||||
- **Token Efficiency**: Compressed analytics data when appropriate
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
### Planned Features
|
||||
- **Cross-Session Analytics**: Performance trends across multiple sessions
|
||||
- **Predictive Recommendations**: ML-based optimization suggestions
|
||||
- **Real-Time Monitoring**: Live session analytics during execution
|
||||
- **Collaborative Learning**: Shared learning patterns across users
|
||||
- **Advanced Compression**: Context-aware compression algorithms
|
||||
|
||||
### Integration Opportunities
|
||||
- **Dashboard Integration**: Real-time analytics visualization
|
||||
- **Notification System**: Alerts for performance degradation
|
||||
- **API Endpoints**: Session analytics via REST API
|
||||
- **Export Capabilities**: Analytics data export for external analysis
|
||||
|
||||
---
|
||||
|
||||
*The Stop Hook represents the culmination of session management in SuperClaude Framework, providing comprehensive analytics, learning consolidation, and intelligent persistence to enable continuous improvement and optimization of user productivity.*
|
||||
@@ -1,457 +0,0 @@
|
||||
# Subagent Stop Hook Documentation
|
||||
|
||||
## Purpose
|
||||
|
||||
The `subagent_stop` hook analyzes subagent task completion and provides delegation effectiveness measurement after subagent operations. It implements MODE_Task_Management delegation coordination analytics for multi-agent collaboration optimization.
|
||||
|
||||
**Core Implementation**: Measures delegation effectiveness, analyzes cross-agent coordination patterns, and optimizes wave orchestration strategies with a target execution time of <150ms.
|
||||
|
||||
## Execution Context
|
||||
|
||||
The subagent_stop hook runs after subagent operations complete in Claude Code. According to `settings.json`, it has a 15-second timeout and executes via: `python3 ~/.claude/hooks/subagent_stop.py`
|
||||
|
||||
**Execution Triggers:**
|
||||
- Individual subagent task completion
|
||||
- Multi-agent coordination end
|
||||
- Wave orchestration completion
|
||||
- Delegation strategy assessment
|
||||
|
||||
**Actual Processing:**
|
||||
1. Receives subagent completion data via stdin (JSON)
|
||||
2. Analyzes delegation effectiveness and coordination patterns
|
||||
3. Measures multi-agent collaboration success
|
||||
4. Records learning events for delegation optimization
|
||||
5. Outputs coordination analytics via stdout (JSON)
|
||||
|
||||
## Performance Target
|
||||
|
||||
**Target Execution Time: <150ms**
|
||||
|
||||
The hook maintains strict performance requirements to ensure minimal overhead during delegation analytics:
|
||||
|
||||
```python
|
||||
# Performance configuration
|
||||
self.performance_target_ms = config_loader.get_hook_config('subagent_stop', 'performance_target_ms', 150)
|
||||
|
||||
# Performance tracking
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
coordination_report['performance_metrics'] = {
|
||||
'coordination_analysis_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'coordination_efficiency': self._calculate_coordination_efficiency(context, execution_time)
|
||||
}
|
||||
```
|
||||
|
||||
**Performance Optimization Features:**
|
||||
- **Fast Context Extraction**: Efficient subagent data parsing and context enrichment
|
||||
- **Streamlined Analytics**: Optimized delegation effectiveness calculations
|
||||
- **Batched Operations**: Grouped analysis operations for efficiency
|
||||
- **Cached Learning**: Reuse of previous coordination patterns for faster analysis
|
||||
|
||||
## Delegation Analytics
|
||||
|
||||
The hook provides comprehensive **delegation effectiveness measurement** through multiple analytical dimensions:
|
||||
|
||||
### Task Completion Analysis
|
||||
|
||||
```python
|
||||
def _analyze_task_completion(self, context: dict) -> dict:
|
||||
"""Analyze task completion performance."""
|
||||
task_analysis = {
|
||||
'completion_success': context.get('task_success', False),
|
||||
'completion_quality': context.get('output_quality', 0.0),
|
||||
'completion_efficiency': context.get('resource_efficiency', 0.0),
|
||||
'completion_time_performance': 0.0,
|
||||
'success_factors': [],
|
||||
'improvement_areas': []
|
||||
}
|
||||
```
|
||||
|
||||
**Key Metrics:**
|
||||
- **Completion Success Rate**: Binary success/failure tracking for delegated tasks
|
||||
- **Output Quality Assessment**: Quality scoring (0.0-1.0) based on validation results and error indicators
|
||||
- **Resource Efficiency**: Memory, CPU, and time utilization effectiveness measurement
|
||||
- **Time Performance**: Actual vs. expected execution time analysis
|
||||
- **Success Factor Identification**: Patterns that lead to successful delegation outcomes
|
||||
- **Improvement Area Detection**: Areas requiring optimization in future delegations
|
||||
|
||||
### Delegation Effectiveness Measurement
|
||||
|
||||
```python
|
||||
def _analyze_delegation_effectiveness(self, context: dict, task_analysis: dict) -> dict:
|
||||
"""Analyze effectiveness of task delegation."""
|
||||
delegation_analysis = {
|
||||
'delegation_strategy': context.get('delegation_strategy', 'unknown'),
|
||||
'delegation_success': context.get('task_success', False),
|
||||
'delegation_efficiency': 0.0,
|
||||
'coordination_overhead': 0.0,
|
||||
'parallel_benefit': 0.0,
|
||||
'delegation_value': 0.0
|
||||
}
|
||||
```
|
||||
|
||||
**Delegation Strategies Analyzed:**
|
||||
- **Files Strategy**: Individual file-based delegation effectiveness
|
||||
- **Folders Strategy**: Directory-level delegation performance
|
||||
- **Auto Strategy**: Intelligent delegation strategy effectiveness
|
||||
- **Custom Strategies**: User-defined delegation pattern analysis
|
||||
|
||||
**Effectiveness Dimensions:**
|
||||
- **Delegation Efficiency**: Ratio of productive work to coordination overhead
|
||||
- **Coordination Overhead**: Time and resource cost of agent coordination
|
||||
- **Parallel Benefit**: Actual speedup achieved through parallel execution
|
||||
- **Overall Delegation Value**: Composite score weighing quality, efficiency, and parallel benefits
|
||||
|
||||
## Wave Orchestration
|
||||
|
||||
The hook provides advanced **multi-agent coordination analysis** for wave-based task orchestration:
|
||||
|
||||
### Wave Coordination Success
|
||||
|
||||
```python
|
||||
def _analyze_coordination_patterns(self, context: dict, delegation_analysis: dict) -> dict:
|
||||
"""Analyze coordination patterns and effectiveness."""
|
||||
coordination_analysis = {
|
||||
'coordination_strategy': 'unknown',
|
||||
'synchronization_effectiveness': 0.0,
|
||||
'data_flow_efficiency': 0.0,
|
||||
'wave_coordination_success': 0.0,
|
||||
'cross_agent_learning': 0.0,
|
||||
'coordination_patterns_detected': []
|
||||
}
|
||||
```
|
||||
|
||||
**Wave Orchestration Features:**
|
||||
- **Progressive Enhancement**: Iterative improvement through multiple coordination waves
|
||||
- **Systematic Analysis**: Comprehensive methodical analysis across wave cycles
|
||||
- **Adaptive Coordination**: Dynamic strategy adjustment based on wave performance
|
||||
- **Enterprise Orchestration**: Large-scale coordination for complex multi-agent operations
|
||||
|
||||
### Wave Performance Metrics
|
||||
|
||||
```python
|
||||
def _update_wave_orchestration_metrics(self, context: dict, coordination_analysis: dict) -> dict:
|
||||
"""Update wave orchestration performance metrics."""
|
||||
wave_metrics = {
|
||||
'wave_performance': 0.0,
|
||||
'orchestration_efficiency': 0.0,
|
||||
'wave_learning_value': 0.0,
|
||||
'next_wave_recommendations': []
|
||||
}
|
||||
```
|
||||
|
||||
**Wave Strategy Analysis:**
|
||||
- **Wave Position Tracking**: Current position within multi-wave coordination
|
||||
- **Inter-Wave Communication**: Data flow and synchronization between waves
|
||||
- **Wave Success Metrics**: Performance measurement across wave cycles
|
||||
- **Orchestration Efficiency**: Resource utilization effectiveness in wave coordination
|
||||
|
||||
## Cross-Agent Learning
|
||||
|
||||
The hook implements **sophisticated learning mechanisms** for continuous delegation improvement:
|
||||
|
||||
### Learning Event Recording
|
||||
|
||||
```python
|
||||
def _record_coordination_learning(self, context: dict, delegation_analysis: dict,
|
||||
optimization_insights: dict):
|
||||
"""Record coordination learning for future optimization."""
|
||||
# Record delegation effectiveness
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.PERFORMANCE_OPTIMIZATION,
|
||||
AdaptationScope.PROJECT,
|
||||
context,
|
||||
{
|
||||
'delegation_strategy': context.get('delegation_strategy'),
|
||||
'task_type': context.get('task_type'),
|
||||
'delegation_value': delegation_analysis['delegation_value'],
|
||||
'coordination_overhead': delegation_analysis['coordination_overhead'],
|
||||
'parallel_benefit': delegation_analysis['parallel_benefit']
|
||||
},
|
||||
delegation_analysis['delegation_value'],
|
||||
0.8,
|
||||
{'hook': 'subagent_stop', 'coordination_learning': True}
|
||||
)
|
||||
```
|
||||
|
||||
**Learning Categories:**
|
||||
- **Performance Optimization**: Delegation strategy effectiveness patterns
|
||||
- **Operation Patterns**: Successful task completion patterns
|
||||
- **Coordination Patterns**: Effective multi-agent coordination strategies
|
||||
- **Error Recovery**: Learning from delegation failures and recovery strategies
|
||||
|
||||
**Learning Scopes:**
|
||||
- **Project-Level Learning**: Delegation patterns specific to current project
|
||||
- **User-Level Learning**: Cross-project delegation preferences and patterns
|
||||
- **System-Level Learning**: Framework-wide coordination optimization patterns
|
||||
|
||||
## Parallel Execution Tracking
|
||||
|
||||
The hook provides comprehensive **parallel operation performance analysis**:
|
||||
|
||||
### Parallel Benefit Calculation
|
||||
|
||||
```python
|
||||
# Calculate parallel benefit
|
||||
parallel_tasks = context.get('parallel_tasks', [])
|
||||
if len(parallel_tasks) > 1:
|
||||
# Estimate parallel benefit based on task coordination
|
||||
parallel_efficiency = context.get('parallel_efficiency', 1.0)
|
||||
theoretical_speedup = len(parallel_tasks)
|
||||
actual_speedup = theoretical_speedup * parallel_efficiency
|
||||
delegation_analysis['parallel_benefit'] = actual_speedup / theoretical_speedup
|
||||
```
|
||||
|
||||
**Parallel Performance Metrics:**
|
||||
- **Theoretical vs. Actual Speedup**: Comparison of expected and achieved parallel performance
|
||||
- **Parallel Efficiency**: Effectiveness of parallel task coordination
|
||||
- **Synchronization Overhead**: Cost of coordinating parallel operations
|
||||
- **Resource Contention Analysis**: Impact of resource sharing on parallel performance
|
||||
|
||||
### Coordination Pattern Detection
|
||||
|
||||
```python
|
||||
# Detect coordination patterns
|
||||
if delegation_analysis['delegation_value'] > 0.8:
|
||||
coordination_analysis['coordination_patterns_detected'].append('effective_delegation')
|
||||
|
||||
if coordination_analysis['synchronization_effectiveness'] > 0.8:
|
||||
coordination_analysis['coordination_patterns_detected'].append('efficient_synchronization')
|
||||
|
||||
if coordination_analysis['wave_coordination_success'] > 0.8:
|
||||
coordination_analysis['coordination_patterns_detected'].append('successful_wave_orchestration')
|
||||
```
|
||||
|
||||
**Pattern Categories:**
|
||||
- **Effective Delegation**: High-value delegation strategies
|
||||
- **Efficient Synchronization**: Optimal coordination mechanisms
|
||||
- **Successful Wave Orchestration**: High-performing wave coordination patterns
|
||||
- **Resource Optimization**: Efficient resource utilization patterns
|
||||
|
||||
## Configuration
|
||||
|
||||
The hook is configured through `superclaude-config.json` with comprehensive settings for delegation analytics:
|
||||
|
||||
### Core Configuration
|
||||
|
||||
```json
|
||||
{
|
||||
"hooks": {
|
||||
"subagent_stop": {
|
||||
"enabled": true,
|
||||
"priority": 7,
|
||||
"performance_target_ms": 150,
|
||||
"delegation_analytics": {
|
||||
"enabled": true,
|
||||
"strategy_analysis": ["files", "folders", "auto"],
|
||||
"effectiveness_threshold": 0.6,
|
||||
"coordination_overhead_threshold": 0.3
|
||||
},
|
||||
"wave_orchestration": {
|
||||
"enabled": true,
|
||||
"wave_strategies": ["progressive", "systematic", "adaptive", "enterprise"],
|
||||
"success_threshold": 0.7,
|
||||
"learning_enabled": true
|
||||
},
|
||||
"parallel_tracking": {
|
||||
"efficiency_threshold": 0.7,
|
||||
"synchronization_tracking": true,
|
||||
"resource_contention_analysis": true
|
||||
},
|
||||
"learning_configuration": {
|
||||
"coordination_learning": true,
|
||||
"pattern_detection": true,
|
||||
"cross_agent_learning": true,
|
||||
"performance_learning": true
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Task Management Configuration
|
||||
|
||||
```json
|
||||
{
|
||||
"session": {
|
||||
"task_management": {
|
||||
"delegation_strategies": ["files", "folders", "auto"],
|
||||
"wave_orchestration": {
|
||||
"enabled": true,
|
||||
"strategies": ["progressive", "systematic", "adaptive", "enterprise"],
|
||||
"complexity_threshold": 0.4,
|
||||
"min_wave_tasks": 3
|
||||
},
|
||||
"parallel_coordination": {
|
||||
"max_parallel_agents": 7,
|
||||
"synchronization_timeout_ms": 5000,
|
||||
"resource_sharing_enabled": true
|
||||
},
|
||||
"learning_integration": {
|
||||
"delegation_learning": true,
|
||||
"wave_learning": true,
|
||||
"cross_session_learning": true
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## MODE_Task_Management Integration
|
||||
|
||||
The hook implements **MODE_Task_Management** through comprehensive integration with the task management framework:
|
||||
|
||||
### Task Management Layer Integration
|
||||
|
||||
```python
|
||||
# Load task management configuration
|
||||
self.task_config = config_loader.get_section('session', 'task_management', {})
|
||||
|
||||
# Integration with task management layers
|
||||
# Layer 1: TodoRead/TodoWrite (Session Tasks) - Real-time state management
|
||||
# Layer 2: /task Command (Project Management) - Cross-session persistence
|
||||
# Layer 3: /spawn Command (Meta-Orchestration) - Complex multi-domain operations
|
||||
# Layer 4: /loop Command (Iterative Enhancement) - Progressive refinement workflows
|
||||
```
|
||||
|
||||
**Framework Integration Points:**
|
||||
- **Session Task Tracking**: Integration with TodoWrite for task completion analytics
|
||||
- **Project Task Coordination**: Cross-session task management integration
|
||||
- **Meta-Orchestration**: Complex multi-domain operation coordination
|
||||
- **Iterative Enhancement**: Progressive refinement and quality improvement cycles
|
||||
|
||||
### Auto-Activation Patterns
|
||||
|
||||
The hook supports MODE_Task_Management auto-activation patterns:
|
||||
|
||||
```python
|
||||
# Auto-activation triggers from MODE_Task_Management:
|
||||
# - Sub-Agent Delegation: >2 directories OR >3 files OR complexity >0.4
|
||||
# - Wave Mode: complexity ≥0.4 AND files >3 AND operation_types >2
|
||||
# - Loop Mode: polish, refine, enhance, improve keywords detected
|
||||
```
|
||||
|
||||
**Detection Patterns:**
|
||||
- **Multi-Step Operations**: 3+ step sequences with dependency analysis
|
||||
- **Complexity Thresholds**: Operations exceeding 0.4 complexity score
|
||||
- **File Count Triggers**: 3+ files for delegation, 2+ directories for coordination
|
||||
- **Performance Opportunities**: Auto-detect parallelizable operations with time estimates
|
||||
|
||||
## Coordination Effectiveness
|
||||
|
||||
The hook provides comprehensive **success metrics for delegation** through multiple measurement dimensions:
|
||||
|
||||
### Overall Effectiveness Calculation
|
||||
|
||||
```python
|
||||
'performance_summary': {
|
||||
'overall_effectiveness': (
|
||||
task_analysis['completion_quality'] * 0.4 +
|
||||
delegation_analysis['delegation_value'] * 0.3 +
|
||||
coordination_analysis['synchronization_effectiveness'] * 0.3
|
||||
),
|
||||
'delegation_success': delegation_analysis['delegation_value'] > 0.6,
|
||||
'coordination_success': coordination_analysis['synchronization_effectiveness'] > 0.7,
|
||||
'learning_value': wave_metrics.get('wave_learning_value', 0.5)
|
||||
}
|
||||
```
|
||||
|
||||
**Effectiveness Dimensions:**
|
||||
- **Task Quality (40%)**: Output quality and completion success
|
||||
- **Delegation Value (30%)**: Effectiveness of delegation strategy and execution
|
||||
- **Coordination Success (30%)**: Synchronization and coordination effectiveness
|
||||
|
||||
### Success Thresholds
|
||||
|
||||
```python
|
||||
# Success criteria
|
||||
delegation_success = delegation_analysis['delegation_value'] > 0.6
|
||||
coordination_success = coordination_analysis['synchronization_effectiveness'] > 0.7
|
||||
wave_success = wave_metrics['wave_performance'] > 0.8
|
||||
```
|
||||
|
||||
**Performance Benchmarks:**
|
||||
- **Delegation Success**: >60% delegation value threshold
|
||||
- **Coordination Success**: >70% synchronization effectiveness threshold
|
||||
- **Wave Success**: >80% wave performance threshold
|
||||
- **Overall Effectiveness**: Composite score incorporating all dimensions
|
||||
|
||||
### Optimization Recommendations
|
||||
|
||||
```python
|
||||
def _generate_optimization_insights(self, context: dict, task_analysis: dict,
|
||||
delegation_analysis: dict, coordination_analysis: dict) -> dict:
|
||||
"""Generate optimization insights for future delegations."""
|
||||
insights = {
|
||||
'delegation_optimizations': [],
|
||||
'coordination_improvements': [],
|
||||
'wave_strategy_recommendations': [],
|
||||
'performance_enhancements': [],
|
||||
'learning_opportunities': []
|
||||
}
|
||||
```
|
||||
|
||||
**Recommendation Categories:**
|
||||
- **Delegation Optimizations**: Alternative strategies, overhead reduction, task partitioning improvements
|
||||
- **Coordination Improvements**: Synchronization mechanism optimization, data exchange pattern improvements
|
||||
- **Wave Strategy Recommendations**: Orchestration strategy adjustments, task distribution optimization
|
||||
- **Performance Enhancements**: Execution speed optimization, resource utilization improvements
|
||||
- **Learning Opportunities**: Pattern recognition, cross-agent learning, continuous improvement areas
|
||||
|
||||
## Error Handling and Resilience
|
||||
|
||||
The hook implements robust error handling with graceful degradation:
|
||||
|
||||
```python
|
||||
def _create_fallback_report(self, subagent_data: dict, error: str) -> dict:
|
||||
"""Create fallback coordination report on error."""
|
||||
return {
|
||||
'subagent_id': subagent_data.get('subagent_id', 'unknown'),
|
||||
'task_id': subagent_data.get('task_id', 'unknown'),
|
||||
'completion_timestamp': time.time(),
|
||||
'error': error,
|
||||
'fallback_mode': True,
|
||||
|
||||
'task_completion': {
|
||||
'success': False,
|
||||
'quality_score': 0.0,
|
||||
'efficiency_score': 0.0,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Error Recovery Strategies:**
|
||||
- **Graceful Degradation**: Fallback coordination reports when analysis fails
|
||||
- **Context Preservation**: Maintain essential coordination data even during errors
|
||||
- **Error Logging**: Comprehensive error tracking for debugging and improvement
|
||||
- **Performance Monitoring**: Continue performance tracking even in error conditions
|
||||
|
||||
## Integration with SuperClaude Framework
|
||||
|
||||
The hook integrates seamlessly with the broader SuperClaude framework:
|
||||
|
||||
### Framework Components
|
||||
|
||||
- **Learning Engine Integration**: Records coordination patterns for continuous improvement
|
||||
- **Pattern Detection**: Identifies successful delegation and coordination patterns
|
||||
- **MCP Intelligence**: Coordinates with MCP servers for enhanced analysis
|
||||
- **Compression Engine**: Optimizes data storage and transfer for coordination analytics
|
||||
- **Framework Logic**: Implements SuperClaude operational principles and patterns
|
||||
|
||||
### Quality Gates Integration
|
||||
|
||||
The hook contributes to SuperClaude's 8-step quality validation cycle:
|
||||
- **Step 2.5**: Task management validation during orchestration operations
|
||||
- **Step 7.5**: Session completion verification and summary documentation
|
||||
- **Continuous**: Real-time metrics collection and performance monitoring
|
||||
- **Post-Session**: Comprehensive session analytics and completion reporting
|
||||
|
||||
### Future Enhancements
|
||||
|
||||
Planned improvements for enhanced delegation coordination:
|
||||
- **Predictive Delegation**: ML-based delegation strategy recommendation
|
||||
- **Cross-Project Learning**: Delegation pattern sharing across projects
|
||||
- **Real-Time Optimization**: Dynamic delegation adjustment during execution
|
||||
- **Advanced Wave Strategies**: More sophisticated wave orchestration patterns
|
||||
- **Resource Prediction**: Predictive resource allocation for delegated tasks
|
||||
@@ -1,184 +0,0 @@
|
||||
# Installation Guide
|
||||
|
||||
Framework-Hooks provides intelligent session management for Claude Code through Python hooks that run at specific lifecycle events.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Python 3.8+ (Python 3.x required by hook scripts)
|
||||
- Claude Code application
|
||||
- Write access to your system's hook installation directory
|
||||
|
||||
## Installation Steps
|
||||
|
||||
### 1. Verify Python Installation
|
||||
|
||||
```bash
|
||||
python3 --version
|
||||
# Should show Python 3.8 or higher
|
||||
```
|
||||
|
||||
### 2. Clone or Extract Framework-Hooks
|
||||
|
||||
Place the Framework-Hooks directory in your SuperClaude installation:
|
||||
|
||||
```
|
||||
YourProject/
|
||||
├── SuperClaude/
|
||||
│ └── Framework-Hooks/ # This repository
|
||||
└── other-files...
|
||||
```
|
||||
|
||||
### 3. Install Hook Scripts
|
||||
|
||||
Framework-Hooks includes pre-configured hook registration files:
|
||||
|
||||
- `settings.json` - Claude Code hook configuration
|
||||
- `superclaude-config.json` - SuperClaude framework settings
|
||||
|
||||
These files configure 7 hooks to run at specific lifecycle events:
|
||||
- `session_start.py` - Session initialization (<50ms target)
|
||||
- `pre_tool_use.py` - Tool preparation (<200ms target)
|
||||
- `post_tool_use.py` - Tool usage recording (<100ms target)
|
||||
- `pre_compact.py` - Context compression (<150ms target)
|
||||
- `notification.py` - Notification handling (<50ms target)
|
||||
- `stop.py` - Session cleanup (<100ms target)
|
||||
- `subagent_stop.py` - Subagent coordination (<100ms target)
|
||||
|
||||
### 4. Directory Structure Verification
|
||||
|
||||
After installation, verify this structure exists:
|
||||
|
||||
```
|
||||
Framework-Hooks/
|
||||
├── hooks/
|
||||
│ ├── session_start.py
|
||||
│ ├── pre_tool_use.py
|
||||
│ ├── post_tool_use.py
|
||||
│ ├── pre_compact.py
|
||||
│ ├── notification.py
|
||||
│ ├── stop.py
|
||||
│ ├── subagent_stop.py
|
||||
│ └── shared/ # 9 shared modules
|
||||
│ ├── framework_logic.py
|
||||
│ ├── compression_engine.py
|
||||
│ ├── learning_engine.py
|
||||
│ ├── mcp_intelligence.py
|
||||
│ ├── pattern_detection.py
|
||||
│ ├── intelligence_engine.py
|
||||
│ ├── logger.py
|
||||
│ ├── yaml_loader.py
|
||||
│ └── validate_system.py
|
||||
├── config/ # 12+ YAML configuration files
|
||||
│ ├── session.yaml
|
||||
│ ├── performance.yaml
|
||||
│ ├── compression.yaml
|
||||
│ ├── modes.yaml
|
||||
│ ├── mcp_orchestration.yaml
|
||||
│ ├── orchestrator.yaml
|
||||
│ ├── logging.yaml
|
||||
│ └── validation.yaml
|
||||
├── patterns/ # 3-tier pattern system
|
||||
│ ├── minimal/ # Basic patterns (3-5KB each)
|
||||
│ ├── dynamic/ # Feature-specific (8-12KB each)
|
||||
│ └── learned/ # User adaptations (10-20KB each)
|
||||
├── cache/ # Runtime cache directory
|
||||
└── docs/ # Documentation
|
||||
```
|
||||
|
||||
### 5. Configuration Check
|
||||
|
||||
The system ships with conservative defaults:
|
||||
|
||||
- **Logging**: Disabled by default (`logging.yaml` has `enabled: false`)
|
||||
- **Performance targets**: session_start <50ms, pre_tool_use <200ms
|
||||
- **Timeouts**: 10-15 seconds per hook execution
|
||||
- **All hooks enabled**: via settings.json configuration
|
||||
|
||||
### 6. Test Installation
|
||||
|
||||
Run the validation system to verify installation:
|
||||
|
||||
```bash
|
||||
cd Framework-Hooks/hooks/shared
|
||||
python3 validate_system.py --check-installation
|
||||
```
|
||||
|
||||
This will verify:
|
||||
- Python dependencies available
|
||||
- All hook files are executable
|
||||
- Configuration files are valid YAML
|
||||
- Required directories exist
|
||||
- Shared modules can be imported
|
||||
|
||||
## Verification
|
||||
|
||||
### Quick Test
|
||||
|
||||
Start a Claude Code session. You should see hook execution if logging is enabled (edit `config/logging.yaml` to enable logging).
|
||||
|
||||
### Check Hook Registration
|
||||
|
||||
The hooks register automatically through:
|
||||
- `settings.json` - Defines 7 hooks with 10-15 second timeouts
|
||||
- Commands like `python3 ~/.claude/hooks/session_start.py`
|
||||
- Universal matcher `"*"` applies to all sessions
|
||||
|
||||
### Performance Verification
|
||||
|
||||
Hooks should execute within performance targets:
|
||||
- Session start: <50ms
|
||||
- Pre tool use: <200ms
|
||||
- Post tool use: <100ms
|
||||
- Other hooks: <100-150ms each
|
||||
|
||||
## Configuration
|
||||
|
||||
### Default Settings
|
||||
|
||||
All hooks start **enabled** with conservative defaults:
|
||||
- Logging **disabled** (`config/logging.yaml`)
|
||||
- Error-only log level
|
||||
- 30-day log retention
|
||||
- Privacy-safe logging (sanitizes user content)
|
||||
|
||||
### Enable Logging (Optional)
|
||||
|
||||
To see hook activity, edit `config/logging.yaml`:
|
||||
|
||||
```yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: "INFO" # or DEBUG for verbose output
|
||||
```
|
||||
|
||||
### Pattern System
|
||||
|
||||
The 3-tier pattern system loads automatically:
|
||||
- **minimal/**: Basic project detection (always loaded)
|
||||
- **dynamic/**: Feature-specific patterns (loaded on demand)
|
||||
- **learned/**: User-specific adaptations (evolve with usage)
|
||||
|
||||
## Next Steps
|
||||
|
||||
After installation:
|
||||
|
||||
1. **Normal Usage**: Start Claude Code sessions normally - hooks run automatically
|
||||
2. **Monitor Performance**: Check that hooks execute within target times
|
||||
3. **Review Logs**: Enable logging to see hook decisions and learning
|
||||
4. **Customize Patterns**: Add project-specific patterns to `patterns/` directories
|
||||
|
||||
## What Framework-Hooks Does
|
||||
|
||||
Once installed, the system automatically:
|
||||
|
||||
1. **Detects project type** and loads appropriate patterns
|
||||
2. **Activates relevant modes** and MCP servers based on context
|
||||
3. **Applies learned preferences** from previous sessions
|
||||
4. **Optimizes performance** based on resource constraints
|
||||
5. **Learns from usage patterns** to improve future sessions
|
||||
|
||||
The system operates transparently - no manual invocation required.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
If you encounter issues, see [TROUBLESHOOTING.md](TROUBLESHOOTING.md) for common problems and solutions.
|
||||
@@ -1,297 +0,0 @@
|
||||
# Framework-Hooks Integration with SuperClaude
|
||||
|
||||
## Overview
|
||||
|
||||
The Framework-Hooks system implements SuperClaude framework patterns through Claude Code lifecycle hooks. The system executes 7 Python hooks during session lifecycle events to provide mode detection, MCP server routing, and configuration management.
|
||||
|
||||
## 1. Hook Implementation Architecture
|
||||
|
||||
### Lifecycle Hook Integration
|
||||
|
||||
The Framework-Hooks system implements SuperClaude patterns through 7 Python hooks:
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ Claude Code Runtime │
|
||||
├─────────────────────────────────────────────────────────────┤
|
||||
│ SessionStart → PreTool → PostTool → PreCompact → Notify │
|
||||
│ ↓ ↓ ↓ ↓ ↓ │
|
||||
│ Mode/MCP Server Learning Token Pattern │
|
||||
│ Detection Selection Tracking Compression Updates │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### SuperClaude Framework Implementation
|
||||
|
||||
Each hook implements specific SuperClaude framework aspects:
|
||||
|
||||
- **session_start.py**: MODE detection patterns from MODE_*.md files
|
||||
- **pre_tool_use.py**: MCP server routing from ORCHESTRATOR.md patterns
|
||||
- **post_tool_use.py**: Learning and effectiveness tracking
|
||||
- **pre_compact.py**: Token efficiency patterns from MODE_Token_Efficiency.md
|
||||
- **stop.py/subagent_stop.py**: Session analytics and coordination tracking
|
||||
|
||||
### Configuration Integration
|
||||
|
||||
Hook behavior is configured through:
|
||||
|
||||
- **settings.json**: Hook timeouts and execution commands
|
||||
- **performance.yaml**: Performance targets (50ms session_start, 200ms pre_tool_use, etc.)
|
||||
- **modes.yaml**: Mode detection patterns and triggers
|
||||
- **pattern files**: Project-specific behavior in minimal/, dynamic/, learned/ directories
|
||||
|
||||
## 2. Hook Lifecycle Integration
|
||||
|
||||
### Hook Execution Flow
|
||||
|
||||
The hooks execute during specific Claude Code lifecycle events:
|
||||
|
||||
```yaml
|
||||
Hook Execution Sequence:
|
||||
1. SessionStart (10s timeout)
|
||||
- Detects project type (Python, React, etc.)
|
||||
- Loads appropriate pattern files
|
||||
- Activates SuperClaude modes based on user input
|
||||
- Routes to MCP servers
|
||||
|
||||
2. PreToolUse (15s timeout)
|
||||
- Analyzes operation type and complexity
|
||||
- Selects optimal MCP servers
|
||||
- Applies performance optimizations
|
||||
|
||||
3. PostToolUse (10s timeout)
|
||||
- Validates operation results
|
||||
- Records learning data and effectiveness metrics
|
||||
- Updates user preferences
|
||||
|
||||
4. PreCompact (15s timeout)
|
||||
- Applies token compression strategies
|
||||
- Preserves framework content (0% compression)
|
||||
- Uses symbols and abbreviations for efficiency
|
||||
|
||||
5. Notification (10s timeout)
|
||||
- Updates pattern caches
|
||||
- Refreshes configurations
|
||||
- Handles runtime notifications
|
||||
|
||||
6. Stop (15s timeout)
|
||||
- Generates session analytics
|
||||
- Saves learning data to files
|
||||
- Creates performance metrics
|
||||
|
||||
7. SubagentStop (15s timeout)
|
||||
- Tracks delegation performance
|
||||
- Records coordination effectiveness
|
||||
```
|
||||
|
||||
### Integration Points
|
||||
|
||||
- **Pattern Loading**: Minimal patterns loaded during session_start for project-specific behavior
|
||||
- **Learning Persistence**: User preferences and effectiveness data saved to learned/ directory
|
||||
- **Performance Monitoring**: Hook execution times tracked against targets in performance.yaml
|
||||
- **Configuration Updates**: YAML configuration changes applied during runtime
|
||||
|
||||
## 3. MCP Server Coordination
|
||||
|
||||
### Server Routing Logic
|
||||
|
||||
The pre_tool_use hook routes operations to MCP servers based on detected patterns:
|
||||
|
||||
```yaml
|
||||
MCP Server Selection:
|
||||
Magic:
|
||||
- Triggers: UI keywords (component, button, form, modal)
|
||||
- Use case: UI component generation and design
|
||||
|
||||
Sequential:
|
||||
- Triggers: Analysis keywords (analyze, debug, complex)
|
||||
- Use case: Multi-step reasoning and systematic analysis
|
||||
|
||||
Context7:
|
||||
- Triggers: Documentation keywords (library, framework, api)
|
||||
- Use case: Library documentation and best practices
|
||||
|
||||
Playwright:
|
||||
- Triggers: Testing keywords (test, e2e, browser)
|
||||
- Use case: Browser automation and testing
|
||||
|
||||
Morphllm vs Serena:
|
||||
- Morphllm: Simple edits (<10 files, token optimization)
|
||||
- Serena: Complex operations (>5 files, semantic analysis)
|
||||
|
||||
Auto-activation:
|
||||
- Project patterns trigger appropriate server combinations
|
||||
- User preferences influence server selection
|
||||
- Fallback strategies for unavailable servers
|
||||
```
|
||||
|
||||
### Server Configuration
|
||||
|
||||
Server routing is configured through:
|
||||
|
||||
- **mcp_intelligence.py** (31KB) - Core routing logic and server capability matching
|
||||
- **mcp_activation.yaml** - Dynamic patterns for server activation
|
||||
- **Project patterns** - Server preferences by project type (e.g., python_project.yaml specifies Serena + Context7)
|
||||
- **Learning data** - User preferences for server selection stored in learned/ directory
|
||||
|
||||
## 4. SuperClaude Mode Integration
|
||||
|
||||
### Mode Detection
|
||||
|
||||
The session_start hook detects user intent and activates SuperClaude modes:
|
||||
|
||||
```yaml
|
||||
Mode Detection Patterns:
|
||||
Brainstorming Mode:
|
||||
- Triggers: "not sure", "thinking about", "explore", ambiguous requests
|
||||
- Implementation: Activates interactive requirements discovery
|
||||
|
||||
Task Management Mode:
|
||||
- Triggers: Multi-file operations, "build", "implement", complexity >0.4
|
||||
- Implementation: Enables delegation and wave orchestration
|
||||
|
||||
Token Efficiency Mode:
|
||||
- Triggers: Resource constraints >75%, "--uc", "brief"
|
||||
- Implementation: Activates compression in pre_compact hook
|
||||
|
||||
Introspection Mode:
|
||||
- Triggers: "analyze reasoning", meta-cognitive requests
|
||||
- Implementation: Enables framework compliance analysis
|
||||
```
|
||||
|
||||
### Mode Implementation
|
||||
|
||||
Modes are implemented across multiple hooks:
|
||||
|
||||
- **session_start.py**: Detects mode triggers and sets activation flags
|
||||
- **pre_compact.py**: Implements token efficiency compression strategies
|
||||
- **post_tool_use.py**: Validates mode-specific behaviors and tracks effectiveness
|
||||
- **stop.py**: Records mode usage analytics and learning data
|
||||
|
||||
## 5. Configuration and Validation
|
||||
|
||||
### Configuration Management
|
||||
|
||||
The system uses 19 YAML configuration files to define behavior:
|
||||
|
||||
- **performance.yaml** (345 lines): Performance targets and monitoring thresholds
|
||||
- **modes.yaml**: Mode detection patterns and activation triggers
|
||||
- **validation.yaml**: Quality gate definitions and validation rules
|
||||
- **compression.yaml**: Token efficiency settings and compression levels
|
||||
- **session.yaml**: Session lifecycle and analytics configuration
|
||||
|
||||
### Validation Implementation
|
||||
|
||||
Validation is distributed across hooks:
|
||||
|
||||
- **pre_tool_use.py**: Basic validation before tool execution
|
||||
- **post_tool_use.py**: Results validation and quality assessment
|
||||
- **validate_system.py** (32KB): System health checks and validation utilities
|
||||
- **stop.py**: Final session validation and analytics generation
|
||||
|
||||
### Learning and Analytics
|
||||
|
||||
The system tracks effectiveness and adapts behavior:
|
||||
|
||||
- **learning_engine.py** (40KB): Records user preferences and operation effectiveness
|
||||
- **Learned patterns**: Stored in patterns/learned/ directory
|
||||
- **Performance tracking**: Hook execution times and success rates
|
||||
- **User preferences**: Saved across sessions for personalized behavior
|
||||
|
||||
## 6. Session Management
|
||||
|
||||
### Session Integration
|
||||
|
||||
Framework-Hooks integrates with Claude Code session lifecycle:
|
||||
|
||||
- **Session Start**: session_start hook runs when Claude Code sessions begin
|
||||
- **Tool Execution**: pre/post_tool_use hooks run for each tool operation
|
||||
- **Token Optimization**: pre_compact hook runs during token compression
|
||||
- **Session End**: stop hook runs when sessions complete
|
||||
|
||||
### Data Persistence
|
||||
|
||||
Session data is persisted through:
|
||||
|
||||
- **Learning Records**: User preferences saved to patterns/learned/ directory
|
||||
- **Performance Metrics**: Hook execution times and success rates logged
|
||||
- **Session Analytics**: Summary data generated by stop hook
|
||||
- **Pattern Updates**: Dynamic patterns updated based on usage
|
||||
|
||||
### Performance Monitoring
|
||||
|
||||
The system tracks performance against configuration targets:
|
||||
|
||||
- **Hook Timing**: Each hook execution timed and compared to performance.yaml targets
|
||||
- **Resource Usage**: Memory and CPU monitoring during hook execution
|
||||
- **Success Rates**: Operation effectiveness tracked by learning_engine.py
|
||||
- **User Satisfaction**: Implicit feedback through continued usage patterns
|
||||
|
||||
## 7. Pattern System
|
||||
|
||||
### Pattern Directory Structure
|
||||
|
||||
The system uses a three-tier pattern organization:
|
||||
|
||||
```yaml
|
||||
patterns/
|
||||
minimal/ # Essential patterns loaded during session start
|
||||
- python_project.yaml: Python project detection and configuration
|
||||
- react_project.yaml: React project patterns and MCP routing
|
||||
|
||||
dynamic/ # Runtime patterns for adaptive behavior
|
||||
- mode_detection.yaml: SuperClaude mode triggers and activation
|
||||
- mcp_activation.yaml: MCP server routing patterns
|
||||
|
||||
learned/ # User preference and effectiveness data
|
||||
- user_preferences.yaml: Personal configuration adaptations
|
||||
- project_optimizations.yaml: Project-specific learned patterns
|
||||
```
|
||||
|
||||
### Pattern Processing
|
||||
|
||||
Pattern loading and application:
|
||||
|
||||
- **pattern_detection.py** (45KB): Core pattern recognition and matching logic
|
||||
- **Session startup**: Minimal patterns loaded based on detected project type
|
||||
- **Runtime updates**: Dynamic patterns applied during hook execution
|
||||
- **Learning updates**: Successful patterns saved to learned/ directory for future use
|
||||
|
||||
### Pattern Configuration
|
||||
|
||||
Patterns define:
|
||||
|
||||
- **Project detection**: File patterns and dependency analysis for project type identification
|
||||
- **MCP server routing**: Which servers to activate for different operation types
|
||||
- **Mode triggers**: Keywords and contexts that activate SuperClaude modes
|
||||
- **Performance targets**: Project-specific timing and resource goals
|
||||
|
||||
## 8. Implementation Summary
|
||||
|
||||
### System Implementation
|
||||
|
||||
The Framework-Hooks system implements SuperClaude framework patterns through:
|
||||
|
||||
**Core Components:**
|
||||
- 7 Python lifecycle hooks (17 Python files total)
|
||||
- 19 YAML configuration files
|
||||
- 3-tier pattern system (minimal/dynamic/learned)
|
||||
- 9 shared modules providing common functionality
|
||||
|
||||
**Key Features:**
|
||||
- Project type detection and pattern-based configuration
|
||||
- SuperClaude mode activation based on user input patterns
|
||||
- MCP server routing with fallback strategies
|
||||
- Token compression with selective framework protection
|
||||
- Learning system that adapts to user preferences
|
||||
- Performance monitoring against configured targets
|
||||
|
||||
**Integration Points:**
|
||||
- Claude Code lifecycle hooks via settings.json
|
||||
- SuperClaude framework mode implementations
|
||||
- MCP server coordination and routing
|
||||
- Pattern-based project and operation detection
|
||||
- Cross-session learning and preference persistence
|
||||
|
||||
The system provides a Python-based implementation of SuperClaude framework concepts, enabling intelligent behavior through configuration-driven lifecycle hooks that execute during Claude Code sessions.
|
||||
|
||||
@@ -1,214 +0,0 @@
|
||||
# SuperClaude Framework Hooks - Shared Modules Overview
|
||||
|
||||
## Architecture Summary
|
||||
|
||||
The SuperClaude Framework Hooks shared modules provide the intelligent foundation for all 7 Claude Code hooks. These 10 shared modules implement the core SuperClaude framework patterns from RULES.md, PRINCIPLES.md, and ORCHESTRATOR.md, delivering executable intelligence that transforms static configuration into dynamic, adaptive behavior.
|
||||
|
||||
## Module Architecture
|
||||
|
||||
```
|
||||
hooks/shared/
|
||||
├── __init__.py # Module exports and initialization
|
||||
├── framework_logic.py # Core SuperClaude decision algorithms
|
||||
├── pattern_detection.py # Pattern matching and mode activation
|
||||
├── mcp_intelligence.py # MCP server routing and coordination
|
||||
├── compression_engine.py # Token efficiency and optimization
|
||||
├── learning_engine.py # Adaptive learning and feedback
|
||||
├── intelligence_engine.py # Generic YAML pattern interpreter
|
||||
├── validate_system.py # YAML-driven system validation
|
||||
├── yaml_loader.py # Configuration loading and management
|
||||
├── logger.py # Structured logging utilities
|
||||
└── tests/ # Test suite for shared modules
|
||||
```
|
||||
|
||||
## Core Design Principles
|
||||
|
||||
### 1. **Evidence-Based Intelligence**
|
||||
All modules implement measurable decision-making with metrics, performance targets, and validation cycles. No assumptions without evidence.
|
||||
|
||||
### 2. **Adaptive Learning System**
|
||||
Cross-hook learning engine that continuously improves effectiveness through pattern recognition, user preference adaptation, and performance optimization.
|
||||
|
||||
### 3. **Configuration-Driven Behavior**
|
||||
YAML-based configuration system supporting hot-reload, environment interpolation, and modular includes for flexible deployment.
|
||||
|
||||
### 4. **Performance-First Design**
|
||||
Sub-200ms operation targets with intelligent caching, optimized algorithms, and resource-aware processing.
|
||||
|
||||
### 5. **Quality-Gated Operations**
|
||||
Every operation includes validation, error handling, fallback strategies, and comprehensive logging for reliability.
|
||||
|
||||
## Module Responsibilities
|
||||
|
||||
### Intelligence Layer
|
||||
- **framework_logic.py**: Core SuperClaude decision algorithms and validation
|
||||
- **pattern_detection.py**: Intelligent pattern matching for automatic activation
|
||||
- **mcp_intelligence.py**: Smart MCP server selection and coordination
|
||||
- **intelligence_engine.py**: Generic YAML pattern interpreter for hot-reloadable intelligence
|
||||
|
||||
### Optimization Layer
|
||||
- **compression_engine.py**: Token efficiency with quality preservation
|
||||
- **learning_engine.py**: Continuous adaptation and improvement
|
||||
|
||||
### Infrastructure Layer
|
||||
- **yaml_loader.py**: High-performance configuration management
|
||||
- **logger.py**: Structured event logging and analysis
|
||||
- **validate_system.py**: YAML-driven system health validation and diagnostics
|
||||
|
||||
## Key Features
|
||||
|
||||
### Intelligent Decision Making
|
||||
- **Complexity Scoring**: 0.0-1.0 complexity assessment for operation routing
|
||||
- **Risk Assessment**: Low/Medium/High/Critical risk evaluation
|
||||
- **Performance Estimation**: Time and resource impact prediction
|
||||
- **Quality Validation**: Multi-step validation with quality scores
|
||||
|
||||
### Pattern Recognition
|
||||
- **Mode Triggers**: Automatic detection of brainstorming, task management, efficiency needs
|
||||
- **MCP Server Selection**: Context-aware server activation based on operation patterns
|
||||
- **Persona Detection**: Domain expertise hints for specialized routing
|
||||
- **Complexity Indicators**: Multi-file, architectural, and system-wide operation detection
|
||||
|
||||
### Adaptive Learning
|
||||
- **User Preference Learning**: Personalization based on effectiveness feedback
|
||||
- **Operation Pattern Recognition**: Optimization of common workflows
|
||||
- **Performance Feedback Integration**: Continuous improvement through metrics
|
||||
- **Cross-Hook Knowledge Sharing**: Shared learning across all hook implementations
|
||||
|
||||
### Configuration Management
|
||||
- **Dual-Format Support**: JSON (Claude Code settings) + YAML (SuperClaude configs)
|
||||
- **Hot-Reload Capability**: File modification detection with <1s response time
|
||||
- **Environment Interpolation**: ${VAR} and ${VAR:default} syntax support
|
||||
- **Modular Configuration**: Include/merge support for complex deployments
|
||||
|
||||
### Performance Optimization
|
||||
- **Token Compression**: 30-50% reduction with ≥95% quality preservation
|
||||
- **Intelligent Caching**: Sub-10ms configuration access with change detection
|
||||
- **Resource Management**: Adaptive behavior based on usage thresholds
|
||||
- **Parallel Processing**: Coordination strategies for multi-server operations
|
||||
|
||||
## Integration Points
|
||||
|
||||
### Hook Integration
|
||||
Each hook imports and uses shared modules for:
|
||||
```python
|
||||
from shared import (
|
||||
FrameworkLogic, # Decision making
|
||||
PatternDetector, # Pattern recognition
|
||||
MCPIntelligence, # Server coordination
|
||||
CompressionEngine, # Token optimization
|
||||
LearningEngine, # Adaptive learning
|
||||
UnifiedConfigLoader, # Configuration
|
||||
)
|
||||
|
||||
# Additional modules available for direct import:
|
||||
from shared.intelligence_engine import IntelligenceEngine # YAML pattern interpreter
|
||||
from shared.validate_system import YAMLValidationEngine # System health validation
|
||||
from shared.logger import get_logger # Logging utilities
|
||||
```
|
||||
|
||||
### SuperClaude Framework Compliance
|
||||
- **RULES.md**: Operational security, validation requirements, systematic approaches
|
||||
- **PRINCIPLES.md**: Evidence-based decisions, quality standards, error handling
|
||||
- **ORCHESTRATOR.md**: Intelligent routing, resource management, quality gates
|
||||
|
||||
### MCP Server Coordination
|
||||
- **Context7**: Library documentation and framework patterns
|
||||
- **Sequential**: Complex analysis and multi-step reasoning
|
||||
- **Magic**: UI component generation and design systems
|
||||
- **Playwright**: Testing automation and validation
|
||||
- **Morphllm**: Intelligent editing with pattern application
|
||||
- **Serena**: Semantic analysis and project-wide context
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Operation Timings
|
||||
- **Configuration Loading**: <10ms (cached), <50ms (reload)
|
||||
- **Pattern Detection**: <25ms for complex analysis
|
||||
- **Decision Making**: <15ms for framework logic operations
|
||||
- **Compression Processing**: <100ms with quality validation
|
||||
- **Learning Adaptation**: <30ms for preference application
|
||||
|
||||
### Memory Efficiency
|
||||
- **Configuration Cache**: ~2-5KB per config file
|
||||
- **Pattern Cache**: ~1-3KB per compiled pattern set
|
||||
- **Learning Records**: ~500B per learning event
|
||||
- **Compression Cache**: Dynamic based on content size
|
||||
|
||||
### Quality Metrics
|
||||
- **Decision Accuracy**: >90% correct routing decisions
|
||||
- **Pattern Recognition**: >85% confidence for auto-activation
|
||||
- **Compression Quality**: ≥95% information preservation
|
||||
- **Configuration Reliability**: <0.1% cache invalidation errors
|
||||
|
||||
## Error Handling Strategy
|
||||
|
||||
### Graceful Degradation
|
||||
- **Module Failures**: Fallback to simpler algorithms
|
||||
- **Configuration Errors**: Default values with warnings
|
||||
- **Pattern Recognition Failures**: Manual routing options
|
||||
- **Learning System Errors**: Continue without adaptation
|
||||
|
||||
### Recovery Mechanisms
|
||||
- **Configuration Reload**: Automatic retry on file corruption
|
||||
- **Cache Regeneration**: Intelligent cache rebuilding
|
||||
- **Performance Fallbacks**: Resource constraint adaptation
|
||||
- **Error Logging**: Comprehensive error context capture
|
||||
|
||||
## Usage Patterns
|
||||
|
||||
### Basic Hook Integration
|
||||
```python
|
||||
# Initialize shared modules
|
||||
framework_logic = FrameworkLogic()
|
||||
pattern_detector = PatternDetector()
|
||||
mcp_intelligence = MCPIntelligence()
|
||||
|
||||
# Use in hook implementation
|
||||
context = {...}
|
||||
complexity_score = framework_logic.calculate_complexity_score(context)
|
||||
detection_result = pattern_detector.detect_patterns(user_input, context, operation_data)
|
||||
activation_plan = mcp_intelligence.create_activation_plan(user_input, context, operation_data)
|
||||
```
|
||||
|
||||
### Advanced Learning Integration
|
||||
```python
|
||||
# Record learning events
|
||||
learning_engine.record_learning_event(
|
||||
LearningType.USER_PREFERENCE,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
pattern,
|
||||
effectiveness_score=0.85
|
||||
)
|
||||
|
||||
# Apply learned adaptations
|
||||
enhanced_recommendations = learning_engine.apply_adaptations(
|
||||
context, base_recommendations
|
||||
)
|
||||
```
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
### Planned Features
|
||||
- **Multi-Language Support**: Expanded pattern recognition for polyglot projects
|
||||
- **Cloud Configuration**: Remote configuration management with caching
|
||||
- **Advanced Analytics**: Deeper learning insights and recommendation engines
|
||||
- **Real-Time Monitoring**: Live performance dashboards and alerting
|
||||
|
||||
### Architecture Evolution
|
||||
- **Plugin System**: Extensible module architecture for custom intelligence
|
||||
- **Distributed Learning**: Cross-instance learning coordination
|
||||
- **Enhanced Caching**: Redis/memcached integration for enterprise deployments
|
||||
- **API Integration**: REST/GraphQL endpoints for external system integration
|
||||
|
||||
## Related Documentation
|
||||
|
||||
- **Individual Module Documentation**: See module-specific .md files in this directory
|
||||
- **Hook Implementation Guides**: /docs/Hooks/ directory
|
||||
- **Configuration Reference**: /docs/Configuration/ directory
|
||||
- **Performance Tuning**: /docs/Performance/ directory
|
||||
|
||||
---
|
||||
|
||||
*This overview provides the architectural foundation for understanding how SuperClaude's intelligent hooks system transforms static configuration into adaptive, evidence-based automation.*
|
||||
@@ -1,704 +0,0 @@
|
||||
# compression_engine.py - Intelligent Token Optimization Engine
|
||||
|
||||
## Overview
|
||||
|
||||
The `compression_engine.py` module implements intelligent token optimization through MODE_Token_Efficiency.md algorithms, providing adaptive compression, symbol systems, and quality-gated validation. This module enables 30-50% token reduction while maintaining ≥95% information preservation through selective compression strategies and evidence-based validation.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **Adaptive Compression**: 5-level compression strategy from minimal to emergency
|
||||
- **Selective Content Processing**: Framework/user content protection with intelligent classification
|
||||
- **Symbol Systems**: Mathematical and logical relationship compression using Unicode symbols
|
||||
- **Abbreviation Systems**: Technical domain abbreviation with context awareness
|
||||
- **Quality Validation**: Real-time compression effectiveness monitoring with preservation targets
|
||||
|
||||
### Intelligence Capabilities
|
||||
- **Content Type Classification**: Automatic detection of framework vs user vs session content
|
||||
- **Compression Level Determination**: Context-aware selection of optimal compression level
|
||||
- **Quality-Gated Processing**: ≥95% information preservation validation
|
||||
- **Performance Monitoring**: Sub-100ms processing with effectiveness tracking
|
||||
|
||||
## Core Classes and Data Structures
|
||||
|
||||
### Enumerations
|
||||
|
||||
#### CompressionLevel
|
||||
```python
|
||||
class CompressionLevel(Enum):
|
||||
MINIMAL = "minimal" # 0-40% compression - Full detail preservation
|
||||
EFFICIENT = "efficient" # 40-70% compression - Balanced optimization
|
||||
COMPRESSED = "compressed" # 70-85% compression - Aggressive optimization
|
||||
CRITICAL = "critical" # 85-95% compression - Maximum compression
|
||||
EMERGENCY = "emergency" # 95%+ compression - Ultra-compression
|
||||
```
|
||||
|
||||
#### ContentType
|
||||
```python
|
||||
class ContentType(Enum):
|
||||
FRAMEWORK_CONTENT = "framework" # SuperClaude framework - EXCLUDE
|
||||
SESSION_DATA = "session" # Session metadata - COMPRESS
|
||||
USER_CONTENT = "user" # User project files - PRESERVE
|
||||
WORKING_ARTIFACTS = "artifacts" # Analysis results - COMPRESS
|
||||
```
|
||||
|
||||
### Data Classes
|
||||
|
||||
#### CompressionResult
|
||||
```python
|
||||
@dataclass
|
||||
class CompressionResult:
|
||||
original_length: int # Original content length
|
||||
compressed_length: int # Compressed content length
|
||||
compression_ratio: float # Compression ratio achieved
|
||||
quality_score: float # 0.0 to 1.0 quality preservation
|
||||
techniques_used: List[str] # Compression techniques applied
|
||||
preservation_score: float # Information preservation score
|
||||
processing_time_ms: float # Processing time in milliseconds
|
||||
```
|
||||
|
||||
#### CompressionStrategy
|
||||
```python
|
||||
@dataclass
|
||||
class CompressionStrategy:
|
||||
level: CompressionLevel # Target compression level
|
||||
symbol_systems_enabled: bool # Enable symbol replacements
|
||||
abbreviation_systems_enabled: bool # Enable abbreviation systems
|
||||
structural_optimization: bool # Enable structural optimizations
|
||||
selective_preservation: Dict[str, bool] # Content type preservation rules
|
||||
quality_threshold: float # Minimum quality threshold
|
||||
```
|
||||
|
||||
## Content Classification System
|
||||
|
||||
### classify_content()
|
||||
```python
|
||||
def classify_content(self, content: str, metadata: Dict[str, Any]) -> ContentType:
|
||||
file_path = metadata.get('file_path', '')
|
||||
context_type = metadata.get('context_type', '')
|
||||
|
||||
# Framework content - complete exclusion
|
||||
framework_patterns = [
|
||||
'~/.claude/',
|
||||
'.claude/',
|
||||
'SuperClaude/',
|
||||
'CLAUDE.md',
|
||||
'FLAGS.md',
|
||||
'PRINCIPLES.md',
|
||||
'ORCHESTRATOR.md',
|
||||
'MCP_',
|
||||
'MODE_',
|
||||
'SESSION_LIFECYCLE.md'
|
||||
]
|
||||
|
||||
for pattern in framework_patterns:
|
||||
if pattern in file_path or pattern in content:
|
||||
return ContentType.FRAMEWORK_CONTENT
|
||||
|
||||
# Session data - apply compression
|
||||
if context_type in ['session_metadata', 'checkpoint_data', 'cache_content']:
|
||||
return ContentType.SESSION_DATA
|
||||
|
||||
# Working artifacts - apply compression
|
||||
if context_type in ['analysis_results', 'processing_data', 'working_artifacts']:
|
||||
return ContentType.WORKING_ARTIFACTS
|
||||
|
||||
# Default to user content preservation
|
||||
return ContentType.USER_CONTENT
|
||||
```
|
||||
|
||||
**Classification Logic**:
|
||||
1. **Framework Content**: Complete exclusion from compression (0% compression)
|
||||
2. **Session Data**: Session metadata and operational data (apply compression)
|
||||
3. **Working Artifacts**: Analysis results and processing data (apply compression)
|
||||
4. **User Content**: Project code, documentation, configurations (minimal compression only)
|
||||
|
||||
## Compression Level Determination
|
||||
|
||||
### determine_compression_level()
|
||||
```python
|
||||
def determine_compression_level(self, context: Dict[str, Any]) -> CompressionLevel:
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
conversation_length = context.get('conversation_length', 0)
|
||||
user_requests_brevity = context.get('user_requests_brevity', False)
|
||||
complexity_score = context.get('complexity_score', 0.0)
|
||||
|
||||
# Emergency compression for critical resource constraints
|
||||
if resource_usage >= 95:
|
||||
return CompressionLevel.EMERGENCY
|
||||
|
||||
# Critical compression for high resource usage
|
||||
if resource_usage >= 85 or conversation_length > 200:
|
||||
return CompressionLevel.CRITICAL
|
||||
|
||||
# Compressed level for moderate constraints
|
||||
if resource_usage >= 70 or conversation_length > 100 or user_requests_brevity:
|
||||
return CompressionLevel.COMPRESSED
|
||||
|
||||
# Efficient level for mild constraints or complex operations
|
||||
if resource_usage >= 40 or complexity_score > 0.6:
|
||||
return CompressionLevel.EFFICIENT
|
||||
|
||||
# Minimal compression for normal operations
|
||||
return CompressionLevel.MINIMAL
|
||||
```
|
||||
|
||||
**Level Selection Criteria**:
|
||||
- **Emergency (95%+)**: Resource usage ≥95%
|
||||
- **Critical (85-95%)**: Resource usage ≥85% OR conversation >200 messages
|
||||
- **Compressed (70-85%)**: Resource usage ≥70% OR conversation >100 OR user requests brevity
|
||||
- **Efficient (40-70%)**: Resource usage ≥40% OR complexity >0.6
|
||||
- **Minimal (0-40%)**: Normal operations
|
||||
|
||||
## Symbol Systems Framework
|
||||
|
||||
### Symbol Mappings
|
||||
```python
|
||||
def _load_symbol_mappings(self) -> Dict[str, str]:
|
||||
return {
|
||||
# Core Logic & Flow
|
||||
'leads to': '→', 'implies': '→',
|
||||
'transforms to': '⇒', 'converts to': '⇒',
|
||||
'rollback': '←', 'reverse': '←',
|
||||
'bidirectional': '⇄', 'sync': '⇄',
|
||||
'and': '&', 'combine': '&',
|
||||
'separator': '|', 'or': '|',
|
||||
'define': ':', 'specify': ':',
|
||||
'sequence': '»', 'then': '»',
|
||||
'therefore': '∴', 'because': '∵',
|
||||
'equivalent': '≡', 'approximately': '≈',
|
||||
'not equal': '≠',
|
||||
|
||||
# Status & Progress
|
||||
'completed': '✅', 'passed': '✅',
|
||||
'failed': '❌', 'error': '❌',
|
||||
'warning': '⚠️', 'information': 'ℹ️',
|
||||
'in progress': '🔄', 'processing': '🔄',
|
||||
'waiting': '⏳', 'pending': '⏳',
|
||||
'critical': '🚨', 'urgent': '🚨',
|
||||
'target': '🎯', 'goal': '🎯',
|
||||
'metrics': '📊', 'data': '📊',
|
||||
'insight': '💡', 'learning': '💡',
|
||||
|
||||
# Technical Domains
|
||||
'performance': '⚡', 'optimization': '⚡',
|
||||
'analysis': '🔍', 'investigation': '🔍',
|
||||
'configuration': '🔧', 'setup': '🔧',
|
||||
'security': '🛡️', 'protection': '🛡️',
|
||||
'deployment': '📦', 'package': '📦',
|
||||
'design': '🎨', 'frontend': '🎨',
|
||||
'network': '🌐', 'connectivity': '🌐',
|
||||
'mobile': '📱', 'responsive': '📱',
|
||||
'architecture': '🏗️', 'system structure': '🏗️',
|
||||
'components': '🧩', 'modular': '🧩'
|
||||
}
|
||||
```
|
||||
|
||||
### Symbol Application
|
||||
```python
|
||||
def _apply_symbol_systems(self, content: str) -> Tuple[str, List[str]]:
|
||||
compressed = content
|
||||
techniques = []
|
||||
|
||||
# Apply symbol mappings with word boundary protection
|
||||
for phrase, symbol in self.symbol_mappings.items():
|
||||
pattern = r'\b' + re.escape(phrase) + r'\b'
|
||||
if re.search(pattern, compressed, re.IGNORECASE):
|
||||
compressed = re.sub(pattern, symbol, compressed, flags=re.IGNORECASE)
|
||||
techniques.append(f"symbol_{phrase.replace(' ', '_')}")
|
||||
|
||||
return compressed, techniques
|
||||
```
|
||||
|
||||
## Abbreviation Systems Framework
|
||||
|
||||
### Abbreviation Mappings
|
||||
```python
|
||||
def _load_abbreviation_mappings(self) -> Dict[str, str]:
|
||||
return {
|
||||
# System & Architecture
|
||||
'configuration': 'cfg', 'settings': 'cfg',
|
||||
'implementation': 'impl', 'code structure': 'impl',
|
||||
'architecture': 'arch', 'system design': 'arch',
|
||||
'performance': 'perf', 'optimization': 'perf',
|
||||
'operations': 'ops', 'deployment': 'ops',
|
||||
'environment': 'env', 'runtime context': 'env',
|
||||
|
||||
# Development Process
|
||||
'requirements': 'req', 'dependencies': 'deps',
|
||||
'packages': 'deps', 'validation': 'val',
|
||||
'verification': 'val', 'testing': 'test',
|
||||
'quality assurance': 'test', 'documentation': 'docs',
|
||||
'guides': 'docs', 'standards': 'std',
|
||||
'conventions': 'std',
|
||||
|
||||
# Quality & Analysis
|
||||
'quality': 'qual', 'maintainability': 'qual',
|
||||
'security': 'sec', 'safety measures': 'sec',
|
||||
'error': 'err', 'exception handling': 'err',
|
||||
'recovery': 'rec', 'resilience': 'rec',
|
||||
'severity': 'sev', 'priority level': 'sev',
|
||||
'optimization': 'opt', 'improvement': 'opt'
|
||||
}
|
||||
```
|
||||
|
||||
### Abbreviation Application
|
||||
```python
|
||||
def _apply_abbreviation_systems(self, content: str) -> Tuple[str, List[str]]:
|
||||
compressed = content
|
||||
techniques = []
|
||||
|
||||
# Apply abbreviation mappings with context awareness
|
||||
for phrase, abbrev in self.abbreviation_mappings.items():
|
||||
pattern = r'\b' + re.escape(phrase) + r'\b'
|
||||
if re.search(pattern, compressed, re.IGNORECASE):
|
||||
compressed = re.sub(pattern, abbrev, compressed, flags=re.IGNORECASE)
|
||||
techniques.append(f"abbrev_{phrase.replace(' ', '_')}")
|
||||
|
||||
return compressed, techniques
|
||||
```
|
||||
|
||||
## Structural Optimization
|
||||
|
||||
### _apply_structural_optimization()
|
||||
```python
|
||||
def _apply_structural_optimization(self, content: str, level: CompressionLevel) -> Tuple[str, List[str]]:
|
||||
compressed = content
|
||||
techniques = []
|
||||
|
||||
# Remove redundant whitespace
|
||||
compressed = re.sub(r'\s+', ' ', compressed)
|
||||
compressed = re.sub(r'\n\s*\n', '\n', compressed)
|
||||
techniques.append('whitespace_optimization')
|
||||
|
||||
# Aggressive optimizations for higher compression levels
|
||||
if level in [CompressionLevel.COMPRESSED, CompressionLevel.CRITICAL, CompressionLevel.EMERGENCY]:
|
||||
# Remove redundant words
|
||||
compressed = re.sub(r'\b(the|a|an)\s+', '', compressed, flags=re.IGNORECASE)
|
||||
techniques.append('article_removal')
|
||||
|
||||
# Simplify common phrases
|
||||
phrase_simplifications = {
|
||||
r'in order to': 'to',
|
||||
r'it is important to note that': 'note:',
|
||||
r'please be aware that': 'note:',
|
||||
r'it should be noted that': 'note:',
|
||||
r'for the purpose of': 'for',
|
||||
r'with regard to': 'regarding',
|
||||
r'in relation to': 'regarding'
|
||||
}
|
||||
|
||||
for pattern, replacement in phrase_simplifications.items():
|
||||
if re.search(pattern, compressed, re.IGNORECASE):
|
||||
compressed = re.sub(pattern, replacement, compressed, flags=re.IGNORECASE)
|
||||
techniques.append(f'phrase_simplification_{replacement}')
|
||||
|
||||
return compressed, techniques
|
||||
```
|
||||
|
||||
## Compression Strategy Creation
|
||||
|
||||
### _create_compression_strategy()
|
||||
```python
|
||||
def _create_compression_strategy(self, level: CompressionLevel, content_type: ContentType) -> CompressionStrategy:
|
||||
level_configs = {
|
||||
CompressionLevel.MINIMAL: {
|
||||
'symbol_systems': True, # Changed: Enable basic optimizations even for minimal
|
||||
'abbreviations': False,
|
||||
'structural': True, # Changed: Enable basic structural optimization
|
||||
'quality_threshold': 0.98
|
||||
},
|
||||
CompressionLevel.EFFICIENT: {
|
||||
'symbol_systems': True,
|
||||
'abbreviations': False,
|
||||
'structural': True,
|
||||
'quality_threshold': 0.95
|
||||
},
|
||||
CompressionLevel.COMPRESSED: {
|
||||
'symbol_systems': True,
|
||||
'abbreviations': True,
|
||||
'structural': True,
|
||||
'quality_threshold': 0.90
|
||||
},
|
||||
CompressionLevel.CRITICAL: {
|
||||
'symbol_systems': True,
|
||||
'abbreviations': True,
|
||||
'structural': True,
|
||||
'quality_threshold': 0.85
|
||||
},
|
||||
CompressionLevel.EMERGENCY: {
|
||||
'symbol_systems': True,
|
||||
'abbreviations': True,
|
||||
'structural': True,
|
||||
'quality_threshold': 0.80
|
||||
}
|
||||
}
|
||||
|
||||
config = level_configs[level]
|
||||
|
||||
# Adjust for content type
|
||||
if content_type == ContentType.USER_CONTENT:
|
||||
# More conservative for user content
|
||||
config['quality_threshold'] = min(config['quality_threshold'] + 0.1, 1.0)
|
||||
|
||||
return CompressionStrategy(
|
||||
level=level,
|
||||
symbol_systems_enabled=config['symbol_systems'],
|
||||
abbreviation_systems_enabled=config['abbreviations'],
|
||||
structural_optimization=config['structural'],
|
||||
selective_preservation={},
|
||||
quality_threshold=config['quality_threshold']
|
||||
)
|
||||
```
|
||||
|
||||
## Quality Validation Framework
|
||||
|
||||
### Compression Quality Validation
|
||||
```python
|
||||
def _validate_compression_quality(self, original: str, compressed: str, strategy: CompressionStrategy) -> float:
|
||||
# Check if key information is preserved
|
||||
original_words = set(re.findall(r'\b\w+\b', original.lower()))
|
||||
compressed_words = set(re.findall(r'\b\w+\b', compressed.lower()))
|
||||
|
||||
# Word preservation ratio
|
||||
word_preservation = len(compressed_words & original_words) / len(original_words) if original_words else 1.0
|
||||
|
||||
# Length efficiency (not too aggressive)
|
||||
length_ratio = len(compressed) / len(original) if original else 1.0
|
||||
|
||||
# Penalize over-compression
|
||||
if length_ratio < 0.3:
|
||||
word_preservation *= 0.8
|
||||
|
||||
quality_score = (word_preservation * 0.7) + (min(length_ratio * 2, 1.0) * 0.3)
|
||||
|
||||
return min(quality_score, 1.0)
|
||||
```
|
||||
|
||||
### Information Preservation Score
|
||||
```python
|
||||
def _calculate_information_preservation(self, original: str, compressed: str) -> float:
|
||||
# Extract key concepts (capitalized words, technical terms)
|
||||
original_concepts = set(re.findall(r'\b[A-Z][a-z]+\b|\b\w+\.(js|py|md|yaml|json)\b', original))
|
||||
compressed_concepts = set(re.findall(r'\b[A-Z][a-z]+\b|\b\w+\.(js|py|md|yaml|json)\b', compressed))
|
||||
|
||||
if not original_concepts:
|
||||
return 1.0
|
||||
|
||||
preservation_ratio = len(compressed_concepts & original_concepts) / len(original_concepts)
|
||||
return preservation_ratio
|
||||
```
|
||||
|
||||
## Main Compression Interface
|
||||
|
||||
### compress_content()
|
||||
```python
|
||||
def compress_content(self,
|
||||
content: str,
|
||||
context: Dict[str, Any],
|
||||
metadata: Dict[str, Any] = None) -> CompressionResult:
|
||||
import time
|
||||
start_time = time.time()
|
||||
|
||||
if metadata is None:
|
||||
metadata = {}
|
||||
|
||||
# Classify content type
|
||||
content_type = self.classify_content(content, metadata)
|
||||
|
||||
# Framework content - no compression
|
||||
if content_type == ContentType.FRAMEWORK_CONTENT:
|
||||
return CompressionResult(
|
||||
original_length=len(content),
|
||||
compressed_length=len(content),
|
||||
compression_ratio=0.0,
|
||||
quality_score=1.0,
|
||||
techniques_used=['framework_exclusion'],
|
||||
preservation_score=1.0,
|
||||
processing_time_ms=(time.time() - start_time) * 1000
|
||||
)
|
||||
|
||||
# User content - minimal compression only
|
||||
if content_type == ContentType.USER_CONTENT:
|
||||
compression_level = CompressionLevel.MINIMAL
|
||||
else:
|
||||
compression_level = self.determine_compression_level(context)
|
||||
|
||||
# Create compression strategy
|
||||
strategy = self._create_compression_strategy(compression_level, content_type)
|
||||
|
||||
# Apply compression techniques
|
||||
compressed_content = content
|
||||
techniques_used = []
|
||||
|
||||
if strategy.symbol_systems_enabled:
|
||||
compressed_content, symbol_techniques = self._apply_symbol_systems(compressed_content)
|
||||
techniques_used.extend(symbol_techniques)
|
||||
|
||||
if strategy.abbreviation_systems_enabled:
|
||||
compressed_content, abbrev_techniques = self._apply_abbreviation_systems(compressed_content)
|
||||
techniques_used.extend(abbrev_techniques)
|
||||
|
||||
if strategy.structural_optimization:
|
||||
compressed_content, struct_techniques = self._apply_structural_optimization(
|
||||
compressed_content, compression_level
|
||||
)
|
||||
techniques_used.extend(struct_techniques)
|
||||
|
||||
# Calculate metrics
|
||||
original_length = len(content)
|
||||
compressed_length = len(compressed_content)
|
||||
compression_ratio = (original_length - compressed_length) / original_length if original_length > 0 else 0.0
|
||||
|
||||
# Quality validation
|
||||
quality_score = self._validate_compression_quality(content, compressed_content, strategy)
|
||||
preservation_score = self._calculate_information_preservation(content, compressed_content)
|
||||
|
||||
processing_time = (time.time() - start_time) * 1000
|
||||
|
||||
# Cache result for performance
|
||||
cache_key = hashlib.md5(content.encode()).hexdigest()
|
||||
self.compression_cache[cache_key] = compressed_content
|
||||
|
||||
return CompressionResult(
|
||||
original_length=original_length,
|
||||
compressed_length=compressed_length,
|
||||
compression_ratio=compression_ratio,
|
||||
quality_score=quality_score,
|
||||
techniques_used=techniques_used,
|
||||
preservation_score=preservation_score,
|
||||
processing_time_ms=processing_time
|
||||
)
|
||||
```
|
||||
|
||||
## Performance Monitoring and Recommendations
|
||||
|
||||
### get_compression_recommendations()
|
||||
```python
|
||||
def get_compression_recommendations(self, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
recommendations = []
|
||||
|
||||
current_level = self.determine_compression_level(context)
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
|
||||
# Resource-based recommendations
|
||||
if resource_usage > 85:
|
||||
recommendations.append("Enable emergency compression mode for critical resource constraints")
|
||||
elif resource_usage > 70:
|
||||
recommendations.append("Consider compressed mode for better resource efficiency")
|
||||
elif resource_usage < 40:
|
||||
recommendations.append("Resource usage low - minimal compression sufficient")
|
||||
|
||||
# Performance recommendations
|
||||
if context.get('processing_time_ms', 0) > 500:
|
||||
recommendations.append("Compression processing time high - consider caching strategies")
|
||||
|
||||
return {
|
||||
'current_level': current_level.value,
|
||||
'recommendations': recommendations,
|
||||
'estimated_savings': self._estimate_compression_savings(current_level),
|
||||
'quality_impact': self._estimate_quality_impact(current_level),
|
||||
'performance_metrics': self.performance_metrics
|
||||
}
|
||||
```
|
||||
|
||||
### Compression Savings Estimation
|
||||
```python
|
||||
def _estimate_compression_savings(self, level: CompressionLevel) -> Dict[str, float]:
|
||||
savings_map = {
|
||||
CompressionLevel.MINIMAL: {'token_reduction': 0.15, 'time_savings': 0.05},
|
||||
CompressionLevel.EFFICIENT: {'token_reduction': 0.40, 'time_savings': 0.15},
|
||||
CompressionLevel.COMPRESSED: {'token_reduction': 0.60, 'time_savings': 0.25},
|
||||
CompressionLevel.CRITICAL: {'token_reduction': 0.75, 'time_savings': 0.35},
|
||||
CompressionLevel.EMERGENCY: {'token_reduction': 0.85, 'time_savings': 0.45}
|
||||
}
|
||||
return savings_map.get(level, {'token_reduction': 0.0, 'time_savings': 0.0})
|
||||
```
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### Hook Usage Pattern
|
||||
```python
|
||||
# Initialize compression engine
|
||||
compression_engine = CompressionEngine()
|
||||
|
||||
# Compress content with context awareness
|
||||
context = {
|
||||
'resource_usage_percent': 75,
|
||||
'conversation_length': 120,
|
||||
'user_requests_brevity': False,
|
||||
'complexity_score': 0.5
|
||||
}
|
||||
|
||||
metadata = {
|
||||
'file_path': '/project/src/component.js',
|
||||
'context_type': 'user_content'
|
||||
}
|
||||
|
||||
result = compression_engine.compress_content(
|
||||
content="This is a complex React component implementation with multiple state management patterns and performance optimizations.",
|
||||
context=context,
|
||||
metadata=metadata
|
||||
)
|
||||
|
||||
print(f"Original length: {result.original_length}") # 142
|
||||
print(f"Compressed length: {result.compressed_length}") # 95
|
||||
print(f"Compression ratio: {result.compression_ratio:.2%}") # 33%
|
||||
print(f"Quality score: {result.quality_score:.2f}") # 0.95
|
||||
print(f"Preservation score: {result.preservation_score:.2f}") # 0.98
|
||||
print(f"Techniques used: {result.techniques_used}") # ['symbol_performance', 'abbrev_implementation']
|
||||
print(f"Processing time: {result.processing_time_ms:.1f}ms") # 15.2ms
|
||||
```
|
||||
|
||||
### Compression Strategy Analysis
|
||||
```python
|
||||
# Get compression recommendations
|
||||
recommendations = compression_engine.get_compression_recommendations(context)
|
||||
|
||||
print(f"Current level: {recommendations['current_level']}") # 'compressed'
|
||||
print(f"Recommendations: {recommendations['recommendations']}") # ['Consider compressed mode for better resource efficiency']
|
||||
print(f"Estimated savings: {recommendations['estimated_savings']}") # {'token_reduction': 0.6, 'time_savings': 0.25}
|
||||
print(f"Quality impact: {recommendations['quality_impact']}") # 0.90
|
||||
```
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Processing Performance
|
||||
- **Content Classification**: <5ms for typical content analysis
|
||||
- **Compression Level Determination**: <3ms for context evaluation
|
||||
- **Symbol System Application**: <10ms for comprehensive replacement
|
||||
- **Abbreviation System Application**: <8ms for domain-specific replacement
|
||||
- **Structural Optimization**: <15ms for aggressive optimization
|
||||
- **Quality Validation**: <20ms for comprehensive validation
|
||||
|
||||
### Memory Efficiency
|
||||
- **Symbol Mappings Cache**: ~2-3KB for all symbol definitions
|
||||
- **Abbreviation Cache**: ~1-2KB for abbreviation mappings
|
||||
- **Compression Cache**: Dynamic based on content, LRU eviction
|
||||
- **Strategy Objects**: ~100-200B per strategy instance
|
||||
|
||||
### Quality Metrics
|
||||
- **Information Preservation**: ≥95% for all compression levels
|
||||
- **Quality Score Accuracy**: 90%+ correlation with human assessment
|
||||
- **Processing Reliability**: <0.1% compression failures
|
||||
- **Cache Hit Rate**: 85%+ for repeated content compression
|
||||
|
||||
## Error Handling Strategies
|
||||
|
||||
### Compression Failures
|
||||
```python
|
||||
try:
|
||||
# Apply compression techniques
|
||||
compressed_content, techniques = self._apply_symbol_systems(content)
|
||||
except Exception as e:
|
||||
# Fall back to original content with warning
|
||||
logger.log_error("compression_engine", f"Symbol system application failed: {e}")
|
||||
compressed_content = content
|
||||
techniques = ['compression_failed']
|
||||
```
|
||||
|
||||
### Quality Validation Failures
|
||||
- **Invalid Quality Score**: Use fallback quality estimation
|
||||
- **Preservation Score Errors**: Default to 1.0 (full preservation)
|
||||
- **Validation Timeout**: Skip validation, proceed with compression
|
||||
|
||||
### Graceful Degradation
|
||||
- **Pattern Compilation Errors**: Skip problematic patterns, continue with others
|
||||
- **Resource Constraints**: Reduce compression level automatically
|
||||
- **Performance Issues**: Enable compression caching, reduce processing complexity
|
||||
|
||||
## Configuration Requirements
|
||||
|
||||
### Compression Configuration
|
||||
```yaml
|
||||
compression:
|
||||
enabled: true
|
||||
cache_size_mb: 10
|
||||
quality_threshold: 0.95
|
||||
processing_timeout_ms: 100
|
||||
|
||||
levels:
|
||||
minimal:
|
||||
symbol_systems: false
|
||||
abbreviations: false
|
||||
structural: false
|
||||
quality_threshold: 0.98
|
||||
|
||||
efficient:
|
||||
symbol_systems: true
|
||||
abbreviations: false
|
||||
structural: true
|
||||
quality_threshold: 0.95
|
||||
|
||||
compressed:
|
||||
symbol_systems: true
|
||||
abbreviations: true
|
||||
structural: true
|
||||
quality_threshold: 0.90
|
||||
```
|
||||
|
||||
### Content Classification Rules
|
||||
```yaml
|
||||
content_classification:
|
||||
framework_exclusions:
|
||||
- "~/.claude/"
|
||||
- "CLAUDE.md"
|
||||
- "FLAGS.md"
|
||||
- "PRINCIPLES.md"
|
||||
|
||||
compressible_patterns:
|
||||
- "session_metadata"
|
||||
- "checkpoint_data"
|
||||
- "analysis_results"
|
||||
|
||||
preserve_patterns:
|
||||
- "source_code"
|
||||
- "user_documentation"
|
||||
- "project_files"
|
||||
```
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Framework Content Protection
|
||||
```python
|
||||
result = compression_engine.compress_content(
|
||||
content="Content from ~/.claude/CLAUDE.md with framework patterns",
|
||||
context={'resource_usage_percent': 90},
|
||||
metadata={'file_path': '~/.claude/CLAUDE.md'}
|
||||
)
|
||||
|
||||
print(f"Compression ratio: {result.compression_ratio}") # 0.0 (no compression)
|
||||
print(f"Techniques used: {result.techniques_used}") # ['framework_exclusion']
|
||||
```
|
||||
|
||||
### Emergency Compression
|
||||
```python
|
||||
result = compression_engine.compress_content(
|
||||
content="This is a very long document with lots of redundant information that needs to be compressed for emergency situations where resources are critically constrained and every token matters.",
|
||||
context={'resource_usage_percent': 96},
|
||||
metadata={'context_type': 'session_data'}
|
||||
)
|
||||
|
||||
print(f"Compression ratio: {result.compression_ratio:.2%}") # 85%+ compression
|
||||
print(f"Quality preserved: {result.quality_score:.2f}") # ≥0.80
|
||||
```
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **yaml_loader**: Configuration loading for compression settings
|
||||
- **Standard Libraries**: re, json, hashlib, time, typing, dataclasses, enum
|
||||
|
||||
### Framework Integration
|
||||
- **MODE_Token_Efficiency.md**: Direct implementation of token optimization patterns
|
||||
- **Selective Compression**: Framework content protection with user content preservation
|
||||
- **Quality Gates**: Real-time validation with measurable preservation targets
|
||||
|
||||
### Hook Coordination
|
||||
- Used by all hooks for consistent token optimization
|
||||
- Provides standardized compression interface and quality validation
|
||||
- Enables cross-hook performance monitoring and efficiency tracking
|
||||
|
||||
---
|
||||
|
||||
*This module serves as the intelligent token optimization engine for the SuperClaude framework, ensuring efficient resource usage while maintaining information quality and framework compliance through selective, quality-gated compression strategies.*
|
||||
@@ -1,454 +0,0 @@
|
||||
# framework_logic.py - Core SuperClaude Framework Decision Engine
|
||||
|
||||
## Overview
|
||||
|
||||
The `framework_logic.py` module implements the core decision-making algorithms from the SuperClaude framework, translating RULES.md, PRINCIPLES.md, and ORCHESTRATOR.md patterns into executable intelligence. This module serves as the central nervous system for all hook operations, providing evidence-based decision making, complexity assessment, risk evaluation, and quality validation.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **Decision Algorithm Implementation**: Executable versions of SuperClaude framework rules
|
||||
- **Complexity Assessment**: Multi-factor scoring system for operation routing decisions
|
||||
- **Risk Evaluation**: Context-aware risk assessment with mitigation strategies
|
||||
- **Quality Validation**: Multi-step validation cycles with measurable quality scores
|
||||
- **Performance Estimation**: Resource impact prediction and optimization recommendations
|
||||
|
||||
### Framework Pattern Implementation
|
||||
- **RULES.md Compliance**: Read-before-write validation, systematic codebase changes, session lifecycle rules
|
||||
- **PRINCIPLES.md Integration**: Evidence-based decisions, quality standards, error handling patterns
|
||||
- **ORCHESTRATOR.md Logic**: Intelligent routing, resource management, quality gate enforcement
|
||||
|
||||
## Core Classes and Data Structures
|
||||
|
||||
### Enumerations
|
||||
|
||||
#### OperationType
|
||||
```python
|
||||
class OperationType(Enum):
|
||||
READ = "read" # File reading operations
|
||||
WRITE = "write" # File creation operations
|
||||
EDIT = "edit" # File modification operations
|
||||
ANALYZE = "analyze" # Code analysis operations
|
||||
BUILD = "build" # Build/compilation operations
|
||||
TEST = "test" # Testing operations
|
||||
DEPLOY = "deploy" # Deployment operations
|
||||
REFACTOR = "refactor" # Code restructuring operations
|
||||
```
|
||||
|
||||
#### RiskLevel
|
||||
```python
|
||||
class RiskLevel(Enum):
|
||||
LOW = "low" # Minimal impact, safe operations
|
||||
MEDIUM = "medium" # Moderate impact, requires validation
|
||||
HIGH = "high" # Significant impact, requires approval
|
||||
CRITICAL = "critical" # System-wide impact, maximum validation
|
||||
```
|
||||
|
||||
### Data Classes
|
||||
|
||||
#### OperationContext
|
||||
```python
|
||||
@dataclass
|
||||
class OperationContext:
|
||||
operation_type: OperationType # Type of operation being performed
|
||||
file_count: int # Number of files involved
|
||||
directory_count: int # Number of directories involved
|
||||
has_tests: bool # Whether tests are available
|
||||
is_production: bool # Production environment flag
|
||||
user_expertise: str # beginner|intermediate|expert
|
||||
project_type: str # web|api|cli|library|etc
|
||||
complexity_score: float # 0.0 to 1.0 complexity rating
|
||||
risk_level: RiskLevel # Assessed risk level
|
||||
```
|
||||
|
||||
#### ValidationResult
|
||||
```python
|
||||
@dataclass
|
||||
class ValidationResult:
|
||||
is_valid: bool # Overall validation status
|
||||
issues: List[str] # Critical issues found
|
||||
warnings: List[str] # Non-critical warnings
|
||||
suggestions: List[str] # Improvement recommendations
|
||||
quality_score: float # 0.0 to 1.0 quality rating
|
||||
```
|
||||
|
||||
## Core Methods and Algorithms
|
||||
|
||||
### Framework Rule Implementation
|
||||
|
||||
#### should_use_read_before_write()
|
||||
```python
|
||||
def should_use_read_before_write(self, context: OperationContext) -> bool:
|
||||
"""RULES.md: Always use Read tool before Write or Edit operations."""
|
||||
return context.operation_type in [OperationType.WRITE, OperationType.EDIT]
|
||||
```
|
||||
|
||||
**Implementation Details**:
|
||||
- Direct mapping from RULES.md operational security requirements
|
||||
- Returns True for any operation that modifies existing files
|
||||
- Used by hooks to enforce read-before-write validation
|
||||
|
||||
#### should_enable_validation()
|
||||
```python
|
||||
def should_enable_validation(self, context: OperationContext) -> bool:
|
||||
"""ORCHESTRATOR.md: Enable validation for production code or high-risk operations."""
|
||||
return (
|
||||
context.is_production or
|
||||
context.risk_level in [RiskLevel.HIGH, RiskLevel.CRITICAL] or
|
||||
context.operation_type in [OperationType.DEPLOY, OperationType.REFACTOR]
|
||||
)
|
||||
```
|
||||
|
||||
### Complexity Assessment Algorithm
|
||||
|
||||
#### calculate_complexity_score()
|
||||
Multi-factor complexity scoring with weighted components:
|
||||
|
||||
**File Count Factor (0.0 to 0.3)**:
|
||||
- 1 file: 0.0
|
||||
- 2-3 files: 0.1
|
||||
- 4-10 files: 0.2
|
||||
- 10+ files: 0.3
|
||||
|
||||
**Directory Factor (0.0 to 0.2)**:
|
||||
- 1 directory: 0.0
|
||||
- 2 directories: 0.1
|
||||
- 3+ directories: 0.2
|
||||
|
||||
**Operation Type Factor (0.0 to 0.3)**:
|
||||
- Refactor/Architecture: 0.3
|
||||
- Build/Implement/Migrate: 0.2
|
||||
- Fix/Update/Improve: 0.1
|
||||
- Read/Analyze: 0.0
|
||||
|
||||
**Language/Framework Factor (0.0 to 0.2)**:
|
||||
- Multi-language projects: 0.2
|
||||
- Framework changes: 0.1
|
||||
- Single language/no framework: 0.0
|
||||
|
||||
**Total Score**: Sum of all factors, capped at 1.0
|
||||
|
||||
### Risk Assessment Algorithm
|
||||
|
||||
#### assess_risk_level()
|
||||
Context-based risk evaluation with escalation rules:
|
||||
|
||||
1. **Production Environment**: Automatic HIGH risk
|
||||
2. **Complexity > 0.7**: HIGH risk
|
||||
3. **Complexity > 0.4**: MEDIUM risk
|
||||
4. **File Count > 10**: MEDIUM risk
|
||||
5. **Default**: LOW risk
|
||||
|
||||
### Quality Validation Framework
|
||||
|
||||
#### validate_operation()
|
||||
Multi-criteria validation with quality scoring:
|
||||
|
||||
**Evidence-Based Validation**:
|
||||
- Evidence provided: Quality maintained
|
||||
- No evidence: -0.1 quality score, warning generated
|
||||
|
||||
**Error Handling Validation**:
|
||||
- Write/Edit/Deploy operations require error handling
|
||||
- Missing error handling: -0.2 quality score, issue generated
|
||||
|
||||
**Test Coverage Validation**:
|
||||
- Logic changes should have tests
|
||||
- Missing tests: -0.1 quality score, suggestion generated
|
||||
|
||||
**Documentation Validation**:
|
||||
- Public APIs require documentation
|
||||
- Missing docs: -0.1 quality score, suggestion generated
|
||||
|
||||
**Security Validation**:
|
||||
- User input handling requires validation
|
||||
- Missing input validation: -0.3 quality score, critical issue
|
||||
|
||||
**Quality Thresholds**:
|
||||
- Valid operation: No issues AND quality_score ≥ 0.7
|
||||
- Final quality_score: max(calculated_score, 0.0)
|
||||
|
||||
### Thinking Mode Selection
|
||||
|
||||
#### determine_thinking_mode()
|
||||
Complexity-based thinking mode selection:
|
||||
|
||||
- **Complexity ≥ 0.8**: `--ultrathink` (32K token analysis)
|
||||
- **Complexity ≥ 0.6**: `--think-hard` (10K token analysis)
|
||||
- **Complexity ≥ 0.3**: `--think` (4K token analysis)
|
||||
- **Complexity < 0.3**: No thinking mode required
|
||||
|
||||
### Delegation Decision Logic
|
||||
|
||||
#### should_enable_delegation()
|
||||
Multi-factor delegation assessment:
|
||||
|
||||
```python
|
||||
def should_enable_delegation(self, context: OperationContext) -> Tuple[bool, str]:
|
||||
if context.file_count > 3:
|
||||
return True, "files" # File-based delegation
|
||||
elif context.directory_count > 2:
|
||||
return True, "folders" # Folder-based delegation
|
||||
elif context.complexity_score > 0.4:
|
||||
return True, "auto" # Automatic strategy selection
|
||||
else:
|
||||
return False, "none" # No delegation needed
|
||||
```
|
||||
|
||||
## Performance Target Management
|
||||
|
||||
### Configuration Integration
|
||||
```python
|
||||
def __init__(self):
|
||||
# Load performance targets from SuperClaude configuration
|
||||
self.performance_targets = {}
|
||||
|
||||
# Hook-specific targets
|
||||
self.performance_targets['session_start_ms'] = config_loader.get_hook_config(
|
||||
'session_start', 'performance_target_ms', 50
|
||||
)
|
||||
self.performance_targets['tool_routing_ms'] = config_loader.get_hook_config(
|
||||
'pre_tool_use', 'performance_target_ms', 200
|
||||
)
|
||||
# ... additional targets
|
||||
```
|
||||
|
||||
### Performance Impact Estimation
|
||||
```python
|
||||
def estimate_performance_impact(self, context: OperationContext) -> Dict[str, Any]:
|
||||
base_time = 100 # ms
|
||||
estimated_time = base_time * (1 + context.complexity_score * 3)
|
||||
|
||||
# Factor in file count impact
|
||||
if context.file_count > 5:
|
||||
estimated_time *= 1.5
|
||||
|
||||
# Generate optimization suggestions
|
||||
optimizations = []
|
||||
if context.file_count > 3:
|
||||
optimizations.append("Consider parallel processing")
|
||||
if context.complexity_score > 0.6:
|
||||
optimizations.append("Enable delegation mode")
|
||||
|
||||
return {
|
||||
'estimated_time_ms': int(estimated_time),
|
||||
'performance_risk': 'high' if estimated_time > 1000 else 'low',
|
||||
'suggested_optimizations': optimizations,
|
||||
'efficiency_gains_possible': len(optimizations) > 0
|
||||
}
|
||||
```
|
||||
|
||||
## Quality Gates Integration
|
||||
|
||||
### get_quality_gates()
|
||||
Dynamic quality gate selection based on operation context:
|
||||
|
||||
**Base Gates** (All Operations):
|
||||
- `syntax_validation`: Language-specific syntax checking
|
||||
|
||||
**Write/Edit Operations**:
|
||||
- `type_analysis`: Type compatibility validation
|
||||
- `code_quality`: Linting and style checking
|
||||
|
||||
**High-Risk Operations**:
|
||||
- `security_assessment`: Vulnerability scanning
|
||||
- `performance_analysis`: Performance impact analysis
|
||||
|
||||
**Test-Available Operations**:
|
||||
- `test_validation`: Test execution and coverage
|
||||
|
||||
**Deployment Operations**:
|
||||
- `integration_testing`: End-to-end validation
|
||||
- `deployment_validation`: Environment compatibility
|
||||
|
||||
## SuperClaude Principles Application
|
||||
|
||||
### apply_superclaude_principles()
|
||||
Automatic principle enforcement with recommendations:
|
||||
|
||||
**Evidence > Assumptions**:
|
||||
```python
|
||||
if 'assumptions' in enhanced_data and not enhanced_data.get('evidence'):
|
||||
enhanced_data['recommendations'].append(
|
||||
"Gather evidence to validate assumptions"
|
||||
)
|
||||
```
|
||||
|
||||
**Code > Documentation**:
|
||||
```python
|
||||
if enhanced_data.get('operation_type') == 'document' and not enhanced_data.get('has_working_code'):
|
||||
enhanced_data['warnings'].append(
|
||||
"Ensure working code exists before extensive documentation"
|
||||
)
|
||||
```
|
||||
|
||||
**Efficiency > Verbosity**:
|
||||
```python
|
||||
if enhanced_data.get('output_length', 0) > 1000 and not enhanced_data.get('justification_for_length'):
|
||||
enhanced_data['efficiency_suggestions'].append(
|
||||
"Consider token efficiency techniques for long outputs"
|
||||
)
|
||||
```
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### Hook Implementation Pattern
|
||||
```python
|
||||
# Hook initialization
|
||||
framework_logic = FrameworkLogic()
|
||||
|
||||
# Operation context creation
|
||||
context = OperationContext(
|
||||
operation_type=OperationType.EDIT,
|
||||
file_count=file_count,
|
||||
directory_count=dir_count,
|
||||
has_tests=has_tests,
|
||||
is_production=is_production,
|
||||
user_expertise="intermediate",
|
||||
project_type="web",
|
||||
complexity_score=0.0, # Will be calculated
|
||||
risk_level=RiskLevel.LOW # Will be assessed
|
||||
)
|
||||
|
||||
# Calculate complexity and assess risk
|
||||
context.complexity_score = framework_logic.calculate_complexity_score(operation_data)
|
||||
context.risk_level = framework_logic.assess_risk_level(context)
|
||||
|
||||
# Make framework-compliant decisions
|
||||
should_validate = framework_logic.should_enable_validation(context)
|
||||
should_delegate, delegation_strategy = framework_logic.should_enable_delegation(context)
|
||||
thinking_mode = framework_logic.determine_thinking_mode(context)
|
||||
|
||||
# Validate operation
|
||||
validation_result = framework_logic.validate_operation(operation_data)
|
||||
if not validation_result.is_valid:
|
||||
# Handle validation issues
|
||||
handle_validation_issues(validation_result)
|
||||
```
|
||||
|
||||
## Error Handling Strategies
|
||||
|
||||
### Graceful Degradation
|
||||
- **Configuration Errors**: Use default performance targets
|
||||
- **Calculation Errors**: Return safe default values
|
||||
- **Validation Failures**: Provide detailed error context
|
||||
|
||||
### Fallback Mechanisms
|
||||
- **Complexity Calculation**: Default to 0.5 if calculation fails
|
||||
- **Risk Assessment**: Default to MEDIUM risk if assessment fails
|
||||
- **Quality Validation**: Default to valid with warnings if validation fails
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Operation Timings
|
||||
- **Complexity Calculation**: <5ms for typical operations
|
||||
- **Risk Assessment**: <3ms for context evaluation
|
||||
- **Quality Validation**: <10ms for comprehensive validation
|
||||
- **Performance Estimation**: <2ms for impact calculation
|
||||
|
||||
### Memory Efficiency
|
||||
- **Context Objects**: ~200-400 bytes per context
|
||||
- **Validation Results**: ~500-1000 bytes with full details
|
||||
- **Configuration Cache**: ~1-2KB for performance targets
|
||||
|
||||
## Configuration Requirements
|
||||
|
||||
### Required Configuration Sections
|
||||
```yaml
|
||||
# Performance targets for each hook
|
||||
hook_configurations:
|
||||
session_start:
|
||||
performance_target_ms: 50
|
||||
pre_tool_use:
|
||||
performance_target_ms: 200
|
||||
post_tool_use:
|
||||
performance_target_ms: 100
|
||||
pre_compact:
|
||||
performance_target_ms: 150
|
||||
|
||||
# Global performance settings
|
||||
global_configuration:
|
||||
performance_monitoring:
|
||||
enabled: true
|
||||
target_percentile: 95
|
||||
alert_threshold_ms: 500
|
||||
```
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Decision Making
|
||||
```python
|
||||
framework_logic = FrameworkLogic()
|
||||
|
||||
# Create operation context
|
||||
context = OperationContext(
|
||||
operation_type=OperationType.REFACTOR,
|
||||
file_count=15,
|
||||
directory_count=3,
|
||||
has_tests=True,
|
||||
is_production=False,
|
||||
user_expertise="expert",
|
||||
project_type="web",
|
||||
complexity_score=0.0,
|
||||
risk_level=RiskLevel.LOW
|
||||
)
|
||||
|
||||
# Calculate complexity and assess risk
|
||||
context.complexity_score = framework_logic.calculate_complexity_score({
|
||||
'file_count': 15,
|
||||
'directory_count': 3,
|
||||
'operation_type': 'refactor',
|
||||
'multi_language': False,
|
||||
'framework_changes': True
|
||||
})
|
||||
|
||||
context.risk_level = framework_logic.assess_risk_level(context)
|
||||
|
||||
# Make decisions
|
||||
should_read_first = framework_logic.should_use_read_before_write(context) # False (refactor)
|
||||
should_validate = framework_logic.should_enable_validation(context) # True (refactor)
|
||||
should_delegate, strategy = framework_logic.should_enable_delegation(context) # True, "files"
|
||||
thinking_mode = framework_logic.determine_thinking_mode(context) # "--think-hard"
|
||||
```
|
||||
|
||||
### Quality Validation
|
||||
```python
|
||||
operation_data = {
|
||||
'operation_type': 'write',
|
||||
'affects_logic': True,
|
||||
'has_tests': False,
|
||||
'is_public_api': True,
|
||||
'has_documentation': False,
|
||||
'handles_user_input': True,
|
||||
'has_input_validation': False,
|
||||
'has_error_handling': True
|
||||
}
|
||||
|
||||
validation_result = framework_logic.validate_operation(operation_data)
|
||||
|
||||
print(f"Valid: {validation_result.is_valid}") # False
|
||||
print(f"Quality Score: {validation_result.quality_score}") # 0.4
|
||||
print(f"Issues: {validation_result.issues}") # ['User input handling without validation']
|
||||
print(f"Warnings: {validation_result.warnings}") # ['No tests found for logic changes', 'Public API lacks documentation']
|
||||
print(f"Suggestions: {validation_result.suggestions}") # ['Add unit tests for new logic', 'Add API documentation']
|
||||
```
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **yaml_loader**: Configuration loading and management
|
||||
- **Standard Libraries**: json, time, dataclasses, enum, typing
|
||||
|
||||
### Framework Integration
|
||||
- **RULES.md**: Direct implementation of operational rules
|
||||
- **PRINCIPLES.md**: Quality standards and decision-making principles
|
||||
- **ORCHESTRATOR.md**: Intelligent routing and resource management patterns
|
||||
|
||||
### Hook Coordination
|
||||
- Used by all 7 hooks for consistent decision-making
|
||||
- Provides standardized context and validation interfaces
|
||||
- Enables cross-hook performance monitoring and optimization
|
||||
|
||||
---
|
||||
|
||||
*This module serves as the foundational intelligence layer for the entire SuperClaude framework, ensuring that all hook operations are evidence-based, quality-validated, and optimally routed according to established patterns and principles.*
|
||||
@@ -1,459 +0,0 @@
|
||||
# intelligence_engine.py - Generic YAML Pattern Interpreter
|
||||
|
||||
## Overview
|
||||
|
||||
The `intelligence_engine.py` module provides a generic YAML pattern interpreter that enables hot-reloadable intelligence without code changes. This module consumes declarative YAML patterns to provide intelligent services, enabling the Framework-Hooks system to adapt behavior dynamically based on configuration rather than requiring code modifications.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **Hot-Reload YAML Intelligence Patterns**: Dynamically load and reload YAML configuration patterns
|
||||
- **Context-Aware Pattern Matching**: Evaluate contexts against patterns with intelligent matching logic
|
||||
- **Decision Tree Execution**: Execute complex decision trees defined in YAML configurations
|
||||
- **Recommendation Generation**: Generate intelligent recommendations based on pattern analysis
|
||||
- **Performance Optimization**: Cache pattern evaluations and optimize processing
|
||||
- **Multi-Pattern Coordination**: Coordinate multiple pattern types for comprehensive intelligence
|
||||
|
||||
### Intelligence Capabilities
|
||||
- **Pattern-Based Decision Making**: Executable intelligence defined in YAML rather than hardcoded logic
|
||||
- **Real-Time Pattern Updates**: Change intelligence behavior without code deployment
|
||||
- **Context Evaluation**: Smart context analysis with flexible condition matching
|
||||
- **Performance Caching**: Sub-300ms pattern evaluation with intelligent caching
|
||||
|
||||
## Core Classes and Data Structures
|
||||
|
||||
### IntelligenceEngine
|
||||
```python
|
||||
class IntelligenceEngine:
|
||||
"""
|
||||
Generic YAML pattern interpreter for declarative intelligence.
|
||||
|
||||
Features:
|
||||
- Hot-reload YAML intelligence patterns
|
||||
- Context-aware pattern matching
|
||||
- Decision tree execution
|
||||
- Recommendation generation
|
||||
- Performance optimization
|
||||
- Multi-pattern coordination
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.patterns: Dict[str, Dict[str, Any]] = {}
|
||||
self.pattern_cache: Dict[str, Any] = {}
|
||||
self.pattern_timestamps: Dict[str, float] = {}
|
||||
self.evaluation_cache: Dict[str, Tuple[Any, float]] = {}
|
||||
self.cache_duration = 300 # 5 minutes
|
||||
```
|
||||
|
||||
## Pattern Loading and Management
|
||||
|
||||
### _load_all_patterns()
|
||||
```python
|
||||
def _load_all_patterns(self):
|
||||
"""Load all intelligence pattern configurations."""
|
||||
pattern_files = [
|
||||
'intelligence_patterns',
|
||||
'mcp_orchestration',
|
||||
'hook_coordination',
|
||||
'performance_intelligence',
|
||||
'validation_intelligence',
|
||||
'user_experience'
|
||||
]
|
||||
|
||||
for pattern_file in pattern_files:
|
||||
try:
|
||||
patterns = config_loader.load_config(pattern_file)
|
||||
self.patterns[pattern_file] = patterns
|
||||
self.pattern_timestamps[pattern_file] = time.time()
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not load {pattern_file} patterns: {e}")
|
||||
self.patterns[pattern_file] = {}
|
||||
```
|
||||
|
||||
### reload_patterns()
|
||||
```python
|
||||
def reload_patterns(self, force: bool = False) -> bool:
|
||||
"""
|
||||
Reload patterns if they have changed.
|
||||
|
||||
Args:
|
||||
force: Force reload even if no changes detected
|
||||
|
||||
Returns:
|
||||
True if patterns were reloaded
|
||||
"""
|
||||
reloaded = False
|
||||
|
||||
for pattern_file in self.patterns.keys():
|
||||
try:
|
||||
if force:
|
||||
patterns = config_loader.load_config(pattern_file, force_reload=True)
|
||||
self.patterns[pattern_file] = patterns
|
||||
self.pattern_timestamps[pattern_file] = time.time()
|
||||
reloaded = True
|
||||
else:
|
||||
# Check if pattern file has been updated
|
||||
current_patterns = config_loader.load_config(pattern_file)
|
||||
pattern_hash = self._compute_pattern_hash(current_patterns)
|
||||
cached_hash = self.pattern_cache.get(f"{pattern_file}_hash")
|
||||
|
||||
if pattern_hash != cached_hash:
|
||||
self.patterns[pattern_file] = current_patterns
|
||||
self.pattern_cache[f"{pattern_file}_hash"] = pattern_hash
|
||||
self.pattern_timestamps[pattern_file] = time.time()
|
||||
reloaded = True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not reload {pattern_file} patterns: {e}")
|
||||
|
||||
if reloaded:
|
||||
# Clear evaluation cache when patterns change
|
||||
self.evaluation_cache.clear()
|
||||
|
||||
return reloaded
|
||||
```
|
||||
|
||||
## Context Evaluation Framework
|
||||
|
||||
### evaluate_context()
|
||||
```python
|
||||
def evaluate_context(self, context: Dict[str, Any], pattern_type: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Evaluate context against patterns to generate recommendations.
|
||||
|
||||
Args:
|
||||
context: Current operation context
|
||||
pattern_type: Type of patterns to evaluate (e.g., 'mcp_orchestration')
|
||||
|
||||
Returns:
|
||||
Dictionary with recommendations and metadata
|
||||
"""
|
||||
# Check cache first
|
||||
cache_key = f"{pattern_type}_{self._compute_context_hash(context)}"
|
||||
if cache_key in self.evaluation_cache:
|
||||
result, timestamp = self.evaluation_cache[cache_key]
|
||||
if time.time() - timestamp < self.cache_duration:
|
||||
return result
|
||||
|
||||
# Hot-reload patterns if needed
|
||||
self.reload_patterns()
|
||||
|
||||
# Get patterns for this type
|
||||
patterns = self.patterns.get(pattern_type, {})
|
||||
if not patterns:
|
||||
return {'recommendations': {}, 'confidence': 0.0, 'source': 'no_patterns'}
|
||||
|
||||
# Evaluate patterns
|
||||
recommendations = {}
|
||||
confidence_scores = []
|
||||
|
||||
if pattern_type == 'mcp_orchestration':
|
||||
recommendations = self._evaluate_mcp_patterns(context, patterns)
|
||||
elif pattern_type == 'hook_coordination':
|
||||
recommendations = self._evaluate_hook_patterns(context, patterns)
|
||||
elif pattern_type == 'performance_intelligence':
|
||||
recommendations = self._evaluate_performance_patterns(context, patterns)
|
||||
elif pattern_type == 'validation_intelligence':
|
||||
recommendations = self._evaluate_validation_patterns(context, patterns)
|
||||
elif pattern_type == 'user_experience':
|
||||
recommendations = self._evaluate_ux_patterns(context, patterns)
|
||||
elif pattern_type == 'intelligence_patterns':
|
||||
recommendations = self._evaluate_learning_patterns(context, patterns)
|
||||
|
||||
# Calculate overall confidence
|
||||
overall_confidence = max(confidence_scores) if confidence_scores else 0.0
|
||||
|
||||
result = {
|
||||
'recommendations': recommendations,
|
||||
'confidence': overall_confidence,
|
||||
'source': pattern_type,
|
||||
'timestamp': time.time()
|
||||
}
|
||||
|
||||
# Cache result
|
||||
self.evaluation_cache[cache_key] = (result, time.time())
|
||||
|
||||
return result
|
||||
```
|
||||
|
||||
## Pattern Evaluation Methods
|
||||
|
||||
### MCP Orchestration Pattern Evaluation
|
||||
```python
|
||||
def _evaluate_mcp_patterns(self, context: Dict[str, Any], patterns: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate MCP orchestration patterns."""
|
||||
server_selection = patterns.get('server_selection', {})
|
||||
decision_tree = server_selection.get('decision_tree', [])
|
||||
|
||||
recommendations = {
|
||||
'primary_server': None,
|
||||
'support_servers': [],
|
||||
'coordination_mode': 'sequential',
|
||||
'confidence': 0.0
|
||||
}
|
||||
|
||||
# Evaluate decision tree
|
||||
for rule in decision_tree:
|
||||
if self._matches_conditions(context, rule.get('conditions', {})):
|
||||
recommendations['primary_server'] = rule.get('primary_server')
|
||||
recommendations['support_servers'] = rule.get('support_servers', [])
|
||||
recommendations['coordination_mode'] = rule.get('coordination_mode', 'sequential')
|
||||
recommendations['confidence'] = rule.get('confidence', 0.5)
|
||||
break
|
||||
|
||||
# Apply fallback if no match
|
||||
if not recommendations['primary_server']:
|
||||
fallback = server_selection.get('fallback_chain', {})
|
||||
recommendations['primary_server'] = fallback.get('default_primary', 'sequential')
|
||||
recommendations['confidence'] = 0.3
|
||||
|
||||
return recommendations
|
||||
```
|
||||
|
||||
### Performance Intelligence Pattern Evaluation
|
||||
```python
|
||||
def _evaluate_performance_patterns(self, context: Dict[str, Any], patterns: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate performance intelligence patterns."""
|
||||
auto_optimization = patterns.get('auto_optimization', {})
|
||||
optimization_triggers = auto_optimization.get('optimization_triggers', [])
|
||||
|
||||
recommendations = {
|
||||
'optimizations': [],
|
||||
'resource_zone': 'green',
|
||||
'performance_actions': []
|
||||
}
|
||||
|
||||
# Check optimization triggers
|
||||
for trigger in optimization_triggers:
|
||||
if self._matches_conditions(context, trigger.get('condition', {})):
|
||||
recommendations['optimizations'].extend(trigger.get('actions', []))
|
||||
recommendations['performance_actions'].append({
|
||||
'trigger': trigger.get('name'),
|
||||
'urgency': trigger.get('urgency', 'medium')
|
||||
})
|
||||
|
||||
# Determine resource zone
|
||||
resource_usage = context.get('resource_usage', 0.5)
|
||||
resource_zones = patterns.get('resource_management', {}).get('resource_zones', {})
|
||||
|
||||
for zone_name, zone_config in resource_zones.items():
|
||||
threshold = zone_config.get('threshold', 1.0)
|
||||
if resource_usage <= threshold:
|
||||
recommendations['resource_zone'] = zone_name
|
||||
break
|
||||
|
||||
return recommendations
|
||||
```
|
||||
|
||||
## Condition Matching Logic
|
||||
|
||||
### _matches_conditions()
|
||||
```python
|
||||
def _matches_conditions(self, context: Dict[str, Any], conditions: Union[Dict, List]) -> bool:
|
||||
"""Check if context matches pattern conditions."""
|
||||
if isinstance(conditions, list):
|
||||
# List of conditions (AND logic)
|
||||
return all(self._matches_single_condition(context, cond) for cond in conditions)
|
||||
elif isinstance(conditions, dict):
|
||||
if 'AND' in conditions:
|
||||
return all(self._matches_single_condition(context, cond) for cond in conditions['AND'])
|
||||
elif 'OR' in conditions:
|
||||
return any(self._matches_single_condition(context, cond) for cond in conditions['OR'])
|
||||
else:
|
||||
return self._matches_single_condition(context, conditions)
|
||||
return False
|
||||
|
||||
def _matches_single_condition(self, context: Dict[str, Any], condition: Dict[str, Any]) -> bool:
|
||||
"""Check if context matches a single condition."""
|
||||
for key, expected_value in condition.items():
|
||||
context_value = context.get(key)
|
||||
|
||||
if context_value is None:
|
||||
return False
|
||||
|
||||
# Handle string operations
|
||||
if isinstance(expected_value, str):
|
||||
if expected_value.startswith('>'):
|
||||
threshold = float(expected_value[1:])
|
||||
return float(context_value) > threshold
|
||||
elif expected_value.startswith('<'):
|
||||
threshold = float(expected_value[1:])
|
||||
return float(context_value) < threshold
|
||||
elif isinstance(expected_value, list):
|
||||
return context_value in expected_value
|
||||
else:
|
||||
return context_value == expected_value
|
||||
elif isinstance(expected_value, list):
|
||||
return context_value in expected_value
|
||||
else:
|
||||
return context_value == expected_value
|
||||
|
||||
return True
|
||||
```
|
||||
|
||||
## Performance and Caching
|
||||
|
||||
### Pattern Hash Computation
|
||||
```python
|
||||
def _compute_pattern_hash(self, patterns: Dict[str, Any]) -> str:
|
||||
"""Compute hash of pattern configuration for change detection."""
|
||||
pattern_str = str(sorted(patterns.items()))
|
||||
return hashlib.md5(pattern_str.encode()).hexdigest()
|
||||
|
||||
def _compute_context_hash(self, context: Dict[str, Any]) -> str:
|
||||
"""Compute hash of context for caching."""
|
||||
context_str = str(sorted(context.items()))
|
||||
return hashlib.md5(context_str.encode()).hexdigest()[:8]
|
||||
```
|
||||
|
||||
### Intelligence Summary
|
||||
```python
|
||||
def get_intelligence_summary(self) -> Dict[str, Any]:
|
||||
"""Get summary of current intelligence state."""
|
||||
return {
|
||||
'loaded_patterns': list(self.patterns.keys()),
|
||||
'cache_entries': len(self.evaluation_cache),
|
||||
'last_reload': max(self.pattern_timestamps.values()) if self.pattern_timestamps else 0,
|
||||
'pattern_status': {name: 'loaded' for name in self.patterns.keys()}
|
||||
}
|
||||
```
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### Hook Usage Pattern
|
||||
```python
|
||||
# Initialize intelligence engine
|
||||
intelligence_engine = IntelligenceEngine()
|
||||
|
||||
# Evaluate MCP orchestration patterns
|
||||
context = {
|
||||
'operation_type': 'complex_analysis',
|
||||
'file_count': 15,
|
||||
'complexity_score': 0.8,
|
||||
'user_expertise': 'expert'
|
||||
}
|
||||
|
||||
mcp_recommendations = intelligence_engine.evaluate_context(context, 'mcp_orchestration')
|
||||
print(f"Primary server: {mcp_recommendations['recommendations']['primary_server']}")
|
||||
print(f"Support servers: {mcp_recommendations['recommendations']['support_servers']}")
|
||||
print(f"Confidence: {mcp_recommendations['confidence']}")
|
||||
|
||||
# Evaluate performance intelligence
|
||||
performance_recommendations = intelligence_engine.evaluate_context(context, 'performance_intelligence')
|
||||
print(f"Resource zone: {performance_recommendations['recommendations']['resource_zone']}")
|
||||
print(f"Optimizations: {performance_recommendations['recommendations']['optimizations']}")
|
||||
```
|
||||
|
||||
## YAML Pattern Examples
|
||||
|
||||
### MCP Orchestration Pattern
|
||||
```yaml
|
||||
server_selection:
|
||||
decision_tree:
|
||||
- conditions:
|
||||
operation_type: "complex_analysis"
|
||||
complexity_score: ">0.6"
|
||||
primary_server: "sequential"
|
||||
support_servers: ["context7", "serena"]
|
||||
coordination_mode: "parallel"
|
||||
confidence: 0.9
|
||||
|
||||
- conditions:
|
||||
operation_type: "ui_component"
|
||||
primary_server: "magic"
|
||||
support_servers: ["context7"]
|
||||
coordination_mode: "sequential"
|
||||
confidence: 0.8
|
||||
|
||||
fallback_chain:
|
||||
default_primary: "sequential"
|
||||
```
|
||||
|
||||
### Performance Intelligence Pattern
|
||||
```yaml
|
||||
auto_optimization:
|
||||
optimization_triggers:
|
||||
- name: "high_complexity_parallel"
|
||||
condition:
|
||||
complexity_score: ">0.7"
|
||||
file_count: ">5"
|
||||
actions:
|
||||
- "enable_parallel_processing"
|
||||
- "increase_cache_size"
|
||||
urgency: "high"
|
||||
|
||||
- name: "resource_constraint"
|
||||
condition:
|
||||
resource_usage: ">0.8"
|
||||
actions:
|
||||
- "enable_compression"
|
||||
- "reduce_verbosity"
|
||||
urgency: "critical"
|
||||
|
||||
resource_management:
|
||||
resource_zones:
|
||||
green:
|
||||
threshold: 0.6
|
||||
yellow:
|
||||
threshold: 0.75
|
||||
red:
|
||||
threshold: 0.9
|
||||
```
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Operation Timings
|
||||
- **Pattern Loading**: <50ms for complete pattern set
|
||||
- **Pattern Reload Check**: <5ms for change detection
|
||||
- **Context Evaluation**: <25ms for complex pattern matching
|
||||
- **Cache Lookup**: <1ms for cached results
|
||||
- **Pattern Hash Computation**: <3ms for configuration changes
|
||||
|
||||
### Memory Efficiency
|
||||
- **Pattern Storage**: ~2-10KB per pattern file depending on complexity
|
||||
- **Evaluation Cache**: ~500B-2KB per cached evaluation
|
||||
- **Pattern Cache**: ~1KB for pattern hashes and metadata
|
||||
- **Total Memory**: <50KB for typical pattern sets
|
||||
|
||||
### Quality Metrics
|
||||
- **Pattern Match Accuracy**: >95% correct pattern application
|
||||
- **Cache Hit Rate**: 85%+ for repeated evaluations
|
||||
- **Hot-Reload Responsiveness**: <1s pattern update detection
|
||||
- **Evaluation Reliability**: <0.1% pattern matching errors
|
||||
|
||||
## Error Handling Strategies
|
||||
|
||||
### Pattern Loading Failures
|
||||
- **Malformed YAML**: Skip problematic patterns, log warnings, continue with valid patterns
|
||||
- **Missing Pattern Files**: Use empty pattern sets with warnings
|
||||
- **Permission Errors**: Graceful fallback to default recommendations
|
||||
|
||||
### Evaluation Failures
|
||||
- **Invalid Context**: Return no-match result with appropriate metadata
|
||||
- **Pattern Execution Errors**: Log error, return fallback recommendations
|
||||
- **Cache Corruption**: Clear cache, re-evaluate patterns
|
||||
|
||||
### Performance Degradation
|
||||
- **Memory Pressure**: Reduce cache size, increase eviction frequency
|
||||
- **High Latency**: Skip non-critical pattern evaluations
|
||||
- **Resource Constraints**: Disable complex pattern matching temporarily
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **yaml_loader**: Configuration loading for YAML pattern files
|
||||
- **Standard Libraries**: time, hashlib, typing, pathlib
|
||||
|
||||
### Framework Integration
|
||||
- **YAML Configuration**: Consumes intelligence patterns from config/ directory
|
||||
- **Hot-Reload Capability**: Real-time pattern updates without code changes
|
||||
- **Performance Caching**: Optimized for hook performance requirements
|
||||
|
||||
### Hook Coordination
|
||||
- Used by hooks for intelligent decision making based on YAML patterns
|
||||
- Provides standardized pattern evaluation interface
|
||||
- Enables configuration-driven intelligence across all hook operations
|
||||
|
||||
---
|
||||
|
||||
*This module enables the SuperClaude framework to evolve its intelligence through configuration rather than code changes, providing hot-reloadable, pattern-based decision making that adapts to changing requirements and optimizes based on operational data.*
|
||||
@@ -1,760 +0,0 @@
|
||||
# learning_engine.py - Adaptive Learning and Feedback System
|
||||
|
||||
## Overview
|
||||
|
||||
The `learning_engine.py` module provides a cross-hook adaptation system that learns from user patterns, operation effectiveness, and system performance to continuously improve SuperClaude intelligence. It implements user preference learning, operation pattern recognition, performance feedback integration, and cross-hook coordination for personalized and project-specific adaptations.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **User Preference Learning**: Personalization based on effectiveness feedback and usage patterns
|
||||
- **Operation Pattern Recognition**: Identification and optimization of common workflows
|
||||
- **Performance Feedback Integration**: Continuous improvement through effectiveness metrics
|
||||
- **Cross-Hook Knowledge Sharing**: Shared learning across all hook implementations
|
||||
- **Effectiveness Measurement**: Validation of adaptation success and continuous refinement
|
||||
|
||||
### Intelligence Capabilities
|
||||
- **Pattern Signature Generation**: Unique identification of learning patterns for reuse
|
||||
- **Adaptation Creation**: Automatic generation of behavioral modifications from patterns
|
||||
- **Context Matching**: Intelligent matching of current context to learned adaptations
|
||||
- **Effectiveness Tracking**: Longitudinal monitoring of adaptation success rates
|
||||
|
||||
## Core Classes and Data Structures
|
||||
|
||||
### Enumerations
|
||||
|
||||
#### LearningType
|
||||
```python
|
||||
class LearningType(Enum):
|
||||
USER_PREFERENCE = "user_preference" # Personal preference patterns
|
||||
OPERATION_PATTERN = "operation_pattern" # Workflow optimization patterns
|
||||
PERFORMANCE_OPTIMIZATION = "performance_optimization" # Performance improvement patterns
|
||||
ERROR_RECOVERY = "error_recovery" # Error handling and recovery patterns
|
||||
EFFECTIVENESS_FEEDBACK = "effectiveness_feedback" # Feedback on adaptation effectiveness
|
||||
```
|
||||
|
||||
#### AdaptationScope
|
||||
```python
|
||||
class AdaptationScope(Enum):
|
||||
SESSION = "session" # Apply only to current session
|
||||
PROJECT = "project" # Apply to current project
|
||||
USER = "user" # Apply across all user sessions
|
||||
GLOBAL = "global" # Apply to all users (anonymized)
|
||||
```
|
||||
|
||||
### Data Classes
|
||||
|
||||
#### LearningRecord
|
||||
```python
|
||||
@dataclass
|
||||
class LearningRecord:
|
||||
timestamp: float # When the learning event occurred
|
||||
learning_type: LearningType # Type of learning pattern
|
||||
scope: AdaptationScope # Scope of application
|
||||
context: Dict[str, Any] # Context in which learning occurred
|
||||
pattern: Dict[str, Any] # The pattern or behavior observed
|
||||
effectiveness_score: float # 0.0 to 1.0 effectiveness rating
|
||||
confidence: float # 0.0 to 1.0 confidence in learning
|
||||
metadata: Dict[str, Any] # Additional learning metadata
|
||||
```
|
||||
|
||||
#### Adaptation
|
||||
```python
|
||||
@dataclass
|
||||
class Adaptation:
|
||||
adaptation_id: str # Unique adaptation identifier
|
||||
pattern_signature: str # Pattern signature for matching
|
||||
trigger_conditions: Dict[str, Any] # Conditions that trigger this adaptation
|
||||
modifications: Dict[str, Any] # Modifications to apply
|
||||
effectiveness_history: List[float] # Historical effectiveness scores
|
||||
usage_count: int # Number of times applied
|
||||
last_used: float # Timestamp of last usage
|
||||
confidence_score: float # Current confidence in adaptation
|
||||
```
|
||||
|
||||
#### LearningInsight
|
||||
```python
|
||||
@dataclass
|
||||
class LearningInsight:
|
||||
insight_type: str # Type of insight discovered
|
||||
description: str # Human-readable description
|
||||
evidence: List[str] # Supporting evidence for insight
|
||||
recommendations: List[str] # Actionable recommendations
|
||||
confidence: float # Confidence in insight accuracy
|
||||
impact_score: float # Expected impact of implementing insight
|
||||
```
|
||||
|
||||
## Learning Record Management
|
||||
|
||||
### record_learning_event()
|
||||
```python
|
||||
def record_learning_event(self,
|
||||
learning_type: LearningType,
|
||||
scope: AdaptationScope,
|
||||
context: Dict[str, Any],
|
||||
pattern: Dict[str, Any],
|
||||
effectiveness_score: float,
|
||||
confidence: float = 1.0,
|
||||
metadata: Dict[str, Any] = None) -> str:
|
||||
|
||||
record = LearningRecord(
|
||||
timestamp=time.time(),
|
||||
learning_type=learning_type,
|
||||
scope=scope,
|
||||
context=context,
|
||||
pattern=pattern,
|
||||
effectiveness_score=effectiveness_score,
|
||||
confidence=confidence,
|
||||
metadata=metadata
|
||||
)
|
||||
|
||||
self.learning_records.append(record)
|
||||
|
||||
# Trigger adaptation creation if pattern is significant
|
||||
if effectiveness_score > 0.7 and confidence > 0.6:
|
||||
self._create_adaptation_from_record(record)
|
||||
|
||||
self._save_learning_data()
|
||||
return f"learning_{int(record.timestamp)}"
|
||||
```
|
||||
|
||||
**Learning Event Processing**:
|
||||
1. **Record Creation**: Capture learning event with full context
|
||||
2. **Significance Assessment**: Evaluate effectiveness and confidence thresholds
|
||||
3. **Adaptation Trigger**: Create adaptations for significant patterns
|
||||
4. **Persistence**: Save learning data for future sessions
|
||||
5. **ID Generation**: Return unique learning record identifier
|
||||
|
||||
## Pattern Recognition and Adaptation
|
||||
|
||||
### Pattern Signature Generation
|
||||
```python
|
||||
def _generate_pattern_signature(self, pattern: Dict[str, Any], context: Dict[str, Any]) -> str:
|
||||
key_elements = []
|
||||
|
||||
# Pattern type
|
||||
if 'type' in pattern:
|
||||
key_elements.append(f"type:{pattern['type']}")
|
||||
|
||||
# Context elements
|
||||
if 'operation_type' in context:
|
||||
key_elements.append(f"op:{context['operation_type']}")
|
||||
|
||||
if 'complexity_score' in context:
|
||||
complexity_bucket = int(context['complexity_score'] * 10) / 10 # Round to 0.1
|
||||
key_elements.append(f"complexity:{complexity_bucket}")
|
||||
|
||||
if 'file_count' in context:
|
||||
file_bucket = min(context['file_count'], 10) # Cap at 10 for grouping
|
||||
key_elements.append(f"files:{file_bucket}")
|
||||
|
||||
# Pattern-specific elements
|
||||
for key in ['mcp_server', 'mode', 'compression_level', 'delegation_strategy']:
|
||||
if key in pattern:
|
||||
key_elements.append(f"{key}:{pattern[key]}")
|
||||
|
||||
return "_".join(sorted(key_elements))
|
||||
```
|
||||
|
||||
**Signature Components**:
|
||||
- **Pattern Type**: Core pattern classification
|
||||
- **Operation Context**: Operation type, complexity, file count
|
||||
- **Domain Elements**: MCP server, mode, compression level, delegation strategy
|
||||
- **Normalization**: Bucketing and sorting for consistent matching
|
||||
|
||||
### Adaptation Creation
|
||||
```python
|
||||
def _create_adaptation_from_record(self, record: LearningRecord):
|
||||
pattern_signature = self._generate_pattern_signature(record.pattern, record.context)
|
||||
|
||||
# Check if adaptation already exists
|
||||
if pattern_signature in self.adaptations:
|
||||
adaptation = self.adaptations[pattern_signature]
|
||||
adaptation.effectiveness_history.append(record.effectiveness_score)
|
||||
adaptation.usage_count += 1
|
||||
adaptation.last_used = record.timestamp
|
||||
|
||||
# Update confidence based on consistency
|
||||
if len(adaptation.effectiveness_history) > 1:
|
||||
consistency = 1.0 - statistics.stdev(adaptation.effectiveness_history[-5:]) / max(statistics.mean(adaptation.effectiveness_history[-5:]), 0.1)
|
||||
adaptation.confidence_score = min(consistency * record.confidence, 1.0)
|
||||
else:
|
||||
# Create new adaptation
|
||||
adaptation_id = f"adapt_{int(record.timestamp)}_{len(self.adaptations)}"
|
||||
|
||||
adaptation = Adaptation(
|
||||
adaptation_id=adaptation_id,
|
||||
pattern_signature=pattern_signature,
|
||||
trigger_conditions=self._extract_trigger_conditions(record.context),
|
||||
modifications=self._extract_modifications(record.pattern),
|
||||
effectiveness_history=[record.effectiveness_score],
|
||||
usage_count=1,
|
||||
last_used=record.timestamp,
|
||||
confidence_score=record.confidence
|
||||
)
|
||||
|
||||
self.adaptations[pattern_signature] = adaptation
|
||||
```
|
||||
|
||||
**Adaptation Logic**:
|
||||
- **Existing Adaptation**: Update effectiveness history and confidence based on consistency
|
||||
- **New Adaptation**: Create adaptation with initial effectiveness and confidence scores
|
||||
- **Confidence Calculation**: Based on consistency of effectiveness scores over time
|
||||
|
||||
## Context Matching and Application
|
||||
|
||||
### Context Matching
|
||||
```python
|
||||
def _matches_trigger_conditions(self, conditions: Dict[str, Any], context: Dict[str, Any]) -> bool:
|
||||
for key, expected_value in conditions.items():
|
||||
if key not in context:
|
||||
continue
|
||||
|
||||
context_value = context[key]
|
||||
|
||||
# Exact match for strings and booleans
|
||||
if isinstance(expected_value, (str, bool)):
|
||||
if context_value != expected_value:
|
||||
return False
|
||||
|
||||
# Range match for numbers
|
||||
elif isinstance(expected_value, (int, float)):
|
||||
tolerance = 0.1 if isinstance(expected_value, float) else 1
|
||||
if abs(context_value - expected_value) > tolerance:
|
||||
return False
|
||||
|
||||
return True
|
||||
```
|
||||
|
||||
**Matching Strategies**:
|
||||
- **Exact Match**: String and boolean values must match exactly
|
||||
- **Range Match**: Numeric values within tolerance (0.1 for floats, 1 for integers)
|
||||
- **Missing Values**: Ignore missing context keys (graceful degradation)
|
||||
|
||||
### Adaptation Application
|
||||
```python
|
||||
def apply_adaptations(self,
|
||||
context: Dict[str, Any],
|
||||
base_recommendations: Dict[str, Any]) -> Dict[str, Any]:
|
||||
|
||||
relevant_adaptations = self.get_adaptations_for_context(context)
|
||||
enhanced_recommendations = base_recommendations.copy()
|
||||
|
||||
for adaptation in relevant_adaptations:
|
||||
# Apply modifications from adaptation
|
||||
for modification_type, modification_value in adaptation.modifications.items():
|
||||
if modification_type == 'preferred_mcp_server':
|
||||
# Enhance MCP server selection
|
||||
if 'recommended_mcp_servers' not in enhanced_recommendations:
|
||||
enhanced_recommendations['recommended_mcp_servers'] = []
|
||||
|
||||
servers = enhanced_recommendations['recommended_mcp_servers']
|
||||
if modification_value not in servers:
|
||||
servers.insert(0, modification_value) # Prioritize learned preference
|
||||
|
||||
elif modification_type == 'preferred_mode':
|
||||
# Enhance mode selection
|
||||
if 'recommended_modes' not in enhanced_recommendations:
|
||||
enhanced_recommendations['recommended_modes'] = []
|
||||
|
||||
modes = enhanced_recommendations['recommended_modes']
|
||||
if modification_value not in modes:
|
||||
modes.insert(0, modification_value)
|
||||
|
||||
elif modification_type == 'suggested_flags':
|
||||
# Enhance flag suggestions
|
||||
if 'suggested_flags' not in enhanced_recommendations:
|
||||
enhanced_recommendations['suggested_flags'] = []
|
||||
|
||||
for flag in modification_value:
|
||||
if flag not in enhanced_recommendations['suggested_flags']:
|
||||
enhanced_recommendations['suggested_flags'].append(flag)
|
||||
|
||||
# Update usage tracking
|
||||
adaptation.usage_count += 1
|
||||
adaptation.last_used = time.time()
|
||||
|
||||
return enhanced_recommendations
|
||||
```
|
||||
|
||||
**Application Process**:
|
||||
1. **Context Matching**: Find adaptations that match current context
|
||||
2. **Recommendation Enhancement**: Apply learned preferences to base recommendations
|
||||
3. **Prioritization**: Insert learned preferences at the beginning of recommendation lists
|
||||
4. **Usage Tracking**: Update usage statistics for applied adaptations
|
||||
5. **Metadata Addition**: Include adaptation metadata in enhanced recommendations
|
||||
|
||||
## Learning Insights Generation
|
||||
|
||||
### generate_learning_insights()
|
||||
```python
|
||||
def generate_learning_insights(self) -> List[LearningInsight]:
|
||||
insights = []
|
||||
|
||||
# User preference insights
|
||||
insights.extend(self._analyze_user_preferences())
|
||||
|
||||
# Performance pattern insights
|
||||
insights.extend(self._analyze_performance_patterns())
|
||||
|
||||
# Error pattern insights
|
||||
insights.extend(self._analyze_error_patterns())
|
||||
|
||||
# Effectiveness insights
|
||||
insights.extend(self._analyze_effectiveness_patterns())
|
||||
|
||||
return insights
|
||||
```
|
||||
|
||||
### User Preference Analysis
|
||||
```python
|
||||
def _analyze_user_preferences(self) -> List[LearningInsight]:
|
||||
insights = []
|
||||
|
||||
# Analyze MCP server preferences
|
||||
mcp_usage = {}
|
||||
for record in self.learning_records:
|
||||
if record.learning_type == LearningType.USER_PREFERENCE:
|
||||
server = record.pattern.get('mcp_server')
|
||||
if server:
|
||||
if server not in mcp_usage:
|
||||
mcp_usage[server] = []
|
||||
mcp_usage[server].append(record.effectiveness_score)
|
||||
|
||||
if mcp_usage:
|
||||
# Find most effective server
|
||||
server_effectiveness = {
|
||||
server: statistics.mean(scores)
|
||||
for server, scores in mcp_usage.items()
|
||||
if len(scores) >= 3
|
||||
}
|
||||
|
||||
if server_effectiveness:
|
||||
best_server = max(server_effectiveness, key=server_effectiveness.get)
|
||||
best_score = server_effectiveness[best_server]
|
||||
|
||||
if best_score > 0.8:
|
||||
insights.append(LearningInsight(
|
||||
insight_type="user_preference",
|
||||
description=f"User consistently prefers {best_server} MCP server",
|
||||
evidence=[f"Effectiveness score: {best_score:.2f}", f"Usage count: {len(mcp_usage[best_server])}"],
|
||||
recommendations=[f"Auto-suggest {best_server} for similar operations"],
|
||||
confidence=min(best_score, 1.0),
|
||||
impact_score=0.7
|
||||
))
|
||||
|
||||
return insights
|
||||
```
|
||||
|
||||
### Performance Pattern Analysis
|
||||
```python
|
||||
def _analyze_performance_patterns(self) -> List[LearningInsight]:
|
||||
insights = []
|
||||
|
||||
# Analyze delegation effectiveness
|
||||
delegation_records = [
|
||||
r for r in self.learning_records
|
||||
if r.learning_type == LearningType.PERFORMANCE_OPTIMIZATION
|
||||
and 'delegation' in r.pattern
|
||||
]
|
||||
|
||||
if len(delegation_records) >= 5:
|
||||
avg_effectiveness = statistics.mean([r.effectiveness_score for r in delegation_records])
|
||||
|
||||
if avg_effectiveness > 0.75:
|
||||
insights.append(LearningInsight(
|
||||
insight_type="performance_optimization",
|
||||
description="Delegation consistently improves performance",
|
||||
evidence=[f"Average effectiveness: {avg_effectiveness:.2f}", f"Sample size: {len(delegation_records)}"],
|
||||
recommendations=["Enable delegation for multi-file operations", "Lower delegation threshold"],
|
||||
confidence=avg_effectiveness,
|
||||
impact_score=0.8
|
||||
))
|
||||
|
||||
return insights
|
||||
```
|
||||
|
||||
### Error Pattern Analysis
|
||||
```python
|
||||
def _analyze_error_patterns(self) -> List[LearningInsight]:
|
||||
insights = []
|
||||
|
||||
error_records = [
|
||||
r for r in self.learning_records
|
||||
if r.learning_type == LearningType.ERROR_RECOVERY
|
||||
]
|
||||
|
||||
if len(error_records) >= 3:
|
||||
# Analyze common error contexts
|
||||
error_contexts = {}
|
||||
for record in error_records:
|
||||
context_key = record.context.get('operation_type', 'unknown')
|
||||
if context_key not in error_contexts:
|
||||
error_contexts[context_key] = []
|
||||
error_contexts[context_key].append(record)
|
||||
|
||||
for context, records in error_contexts.items():
|
||||
if len(records) >= 2:
|
||||
avg_recovery_effectiveness = statistics.mean([r.effectiveness_score for r in records])
|
||||
|
||||
insights.append(LearningInsight(
|
||||
insight_type="error_recovery",
|
||||
description=f"Error patterns identified for {context} operations",
|
||||
evidence=[f"Occurrence count: {len(records)}", f"Recovery effectiveness: {avg_recovery_effectiveness:.2f}"],
|
||||
recommendations=[f"Add proactive validation for {context} operations"],
|
||||
confidence=min(len(records) / 5, 1.0),
|
||||
impact_score=0.6
|
||||
))
|
||||
|
||||
return insights
|
||||
```
|
||||
|
||||
### Effectiveness Trend Analysis
|
||||
```python
|
||||
def _analyze_effectiveness_patterns(self) -> List[LearningInsight]:
|
||||
insights = []
|
||||
|
||||
if len(self.learning_records) >= 10:
|
||||
recent_records = sorted(self.learning_records, key=lambda r: r.timestamp)[-10:]
|
||||
avg_effectiveness = statistics.mean([r.effectiveness_score for r in recent_records])
|
||||
|
||||
if avg_effectiveness > 0.8:
|
||||
insights.append(LearningInsight(
|
||||
insight_type="effectiveness_trend",
|
||||
description="SuperClaude effectiveness is high and improving",
|
||||
evidence=[f"Recent average effectiveness: {avg_effectiveness:.2f}"],
|
||||
recommendations=["Continue current learning patterns", "Consider expanding adaptation scope"],
|
||||
confidence=avg_effectiveness,
|
||||
impact_score=0.9
|
||||
))
|
||||
elif avg_effectiveness < 0.6:
|
||||
insights.append(LearningInsight(
|
||||
insight_type="effectiveness_concern",
|
||||
description="SuperClaude effectiveness below optimal",
|
||||
evidence=[f"Recent average effectiveness: {avg_effectiveness:.2f}"],
|
||||
recommendations=["Review recent adaptations", "Gather more user feedback", "Adjust learning thresholds"],
|
||||
confidence=1.0 - avg_effectiveness,
|
||||
impact_score=0.8
|
||||
))
|
||||
|
||||
return insights
|
||||
```
|
||||
|
||||
## Effectiveness Feedback Integration
|
||||
|
||||
### record_effectiveness_feedback()
|
||||
```python
|
||||
def record_effectiveness_feedback(self,
|
||||
adaptation_ids: List[str],
|
||||
effectiveness_score: float,
|
||||
context: Dict[str, Any]):
|
||||
|
||||
for adaptation_id in adaptation_ids:
|
||||
# Find adaptation by ID
|
||||
adaptation = None
|
||||
for adapt in self.adaptations.values():
|
||||
if adapt.adaptation_id == adaptation_id:
|
||||
adaptation = adapt
|
||||
break
|
||||
|
||||
if adaptation:
|
||||
adaptation.effectiveness_history.append(effectiveness_score)
|
||||
|
||||
# Update confidence based on consistency
|
||||
if len(adaptation.effectiveness_history) > 2:
|
||||
recent_scores = adaptation.effectiveness_history[-5:]
|
||||
consistency = 1.0 - statistics.stdev(recent_scores) / max(statistics.mean(recent_scores), 0.1)
|
||||
adaptation.confidence_score = min(consistency, 1.0)
|
||||
|
||||
# Record learning event
|
||||
self.record_learning_event(
|
||||
LearningType.EFFECTIVENESS_FEEDBACK,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{'adaptation_id': adaptation_id},
|
||||
effectiveness_score,
|
||||
adaptation.confidence_score
|
||||
)
|
||||
```
|
||||
|
||||
**Feedback Processing**:
|
||||
1. **Adaptation Lookup**: Find adaptation by unique ID
|
||||
2. **Effectiveness Update**: Append new effectiveness score to history
|
||||
3. **Confidence Recalculation**: Update confidence based on score consistency
|
||||
4. **Learning Event Recording**: Create feedback learning record for future analysis
|
||||
|
||||
## Data Persistence and Management
|
||||
|
||||
### Data Storage
|
||||
```python
|
||||
def _save_learning_data(self):
|
||||
try:
|
||||
# Save learning records
|
||||
records_file = self.cache_dir / "learning_records.json"
|
||||
with open(records_file, 'w') as f:
|
||||
json.dump([asdict(record) for record in self.learning_records], f, indent=2)
|
||||
|
||||
# Save adaptations
|
||||
adaptations_file = self.cache_dir / "adaptations.json"
|
||||
with open(adaptations_file, 'w') as f:
|
||||
json.dump({k: asdict(v) for k, v in self.adaptations.items()}, f, indent=2)
|
||||
|
||||
# Save user preferences
|
||||
preferences_file = self.cache_dir / "user_preferences.json"
|
||||
with open(preferences_file, 'w') as f:
|
||||
json.dump(self.user_preferences, f, indent=2)
|
||||
|
||||
# Save project patterns
|
||||
patterns_file = self.cache_dir / "project_patterns.json"
|
||||
with open(patterns_file, 'w') as f:
|
||||
json.dump(self.project_patterns, f, indent=2)
|
||||
|
||||
except Exception as e:
|
||||
pass # Silent fail for cache operations
|
||||
```
|
||||
|
||||
### Data Cleanup
|
||||
```python
|
||||
def cleanup_old_data(self, max_age_days: int = 30):
|
||||
cutoff_time = time.time() - (max_age_days * 24 * 60 * 60)
|
||||
|
||||
# Remove old learning records
|
||||
self.learning_records = [
|
||||
record for record in self.learning_records
|
||||
if record.timestamp > cutoff_time
|
||||
]
|
||||
|
||||
# Remove unused adaptations
|
||||
self.adaptations = {
|
||||
k: v for k, v in self.adaptations.items()
|
||||
if v.last_used > cutoff_time or v.usage_count > 5
|
||||
}
|
||||
|
||||
self._save_learning_data()
|
||||
```
|
||||
|
||||
**Cleanup Strategy**:
|
||||
- **Learning Records**: Remove records older than max_age_days
|
||||
- **Adaptations**: Keep adaptations used within max_age_days OR with usage_count > 5
|
||||
- **Automatic Cleanup**: Triggered during initialization and periodically
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### Hook Usage Pattern
|
||||
```python
|
||||
# Initialize learning engine
|
||||
learning_engine = LearningEngine(cache_dir=Path("cache"))
|
||||
|
||||
# Record learning event during hook operation
|
||||
learning_engine.record_learning_event(
|
||||
learning_type=LearningType.USER_PREFERENCE,
|
||||
scope=AdaptationScope.USER,
|
||||
context={
|
||||
'operation_type': 'build',
|
||||
'complexity_score': 0.6,
|
||||
'file_count': 8,
|
||||
'user_expertise': 'intermediate'
|
||||
},
|
||||
pattern={
|
||||
'mcp_server': 'serena',
|
||||
'mode': 'task_management',
|
||||
'flags': ['--delegate', '--think-hard']
|
||||
},
|
||||
effectiveness_score=0.85,
|
||||
confidence=0.9
|
||||
)
|
||||
|
||||
# Apply learned adaptations to recommendations
|
||||
base_recommendations = {
|
||||
'recommended_mcp_servers': ['morphllm'],
|
||||
'recommended_modes': ['brainstorming'],
|
||||
'suggested_flags': ['--think']
|
||||
}
|
||||
|
||||
enhanced_recommendations = learning_engine.apply_adaptations(
|
||||
context={
|
||||
'operation_type': 'build',
|
||||
'complexity_score': 0.6,
|
||||
'file_count': 8
|
||||
},
|
||||
base_recommendations=base_recommendations
|
||||
)
|
||||
|
||||
print(f"Enhanced servers: {enhanced_recommendations['recommended_mcp_servers']}") # ['serena', 'morphllm']
|
||||
print(f"Enhanced modes: {enhanced_recommendations['recommended_modes']}") # ['task_management', 'brainstorming']
|
||||
print(f"Enhanced flags: {enhanced_recommendations['suggested_flags']}") # ['--delegate', '--think-hard', '--think']
|
||||
```
|
||||
|
||||
### Learning Insights Usage
|
||||
```python
|
||||
# Generate insights from learning patterns
|
||||
insights = learning_engine.generate_learning_insights()
|
||||
|
||||
for insight in insights:
|
||||
print(f"Insight Type: {insight.insight_type}")
|
||||
print(f"Description: {insight.description}")
|
||||
print(f"Evidence: {insight.evidence}")
|
||||
print(f"Recommendations: {insight.recommendations}")
|
||||
print(f"Confidence: {insight.confidence:.2f}")
|
||||
print(f"Impact Score: {insight.impact_score:.2f}")
|
||||
print("---")
|
||||
```
|
||||
|
||||
### Effectiveness Feedback Integration
|
||||
```python
|
||||
# Record effectiveness feedback after operation completion
|
||||
adaptation_ids = enhanced_recommendations.get('applied_adaptations', [])
|
||||
if adaptation_ids:
|
||||
adaptation_ids_list = [adapt['id'] for adapt in adaptation_ids]
|
||||
learning_engine.record_effectiveness_feedback(
|
||||
adaptation_ids=adaptation_ids_list,
|
||||
effectiveness_score=0.92,
|
||||
context={'operation_result': 'success', 'user_satisfaction': 'high'}
|
||||
)
|
||||
```
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Learning Operations
|
||||
- **Learning Event Recording**: <5ms for single event with persistence
|
||||
- **Pattern Signature Generation**: <3ms for typical context and pattern
|
||||
- **Adaptation Creation**: <10ms including condition extraction and modification setup
|
||||
- **Context Matching**: <2ms per adaptation for trigger condition evaluation
|
||||
- **Adaptation Application**: <15ms for typical enhancement with multiple adaptations
|
||||
|
||||
### Memory Efficiency
|
||||
- **Learning Records**: ~500B per record with full context and metadata
|
||||
- **Adaptations**: ~300-500B per adaptation with effectiveness history
|
||||
- **Pattern Signatures**: ~50-100B per signature for matching
|
||||
- **Cache Storage**: JSON serialization with compression for large datasets
|
||||
|
||||
### Effectiveness Metrics
|
||||
- **Adaptation Accuracy**: >85% correct context matching for learned adaptations
|
||||
- **Effectiveness Prediction**: 80%+ correlation between predicted and actual effectiveness
|
||||
- **Learning Convergence**: 3-5 similar events required for stable adaptation creation
|
||||
- **Data Persistence Reliability**: <0.1% data loss rate with automatic recovery
|
||||
|
||||
## Error Handling Strategies
|
||||
|
||||
### Learning Event Failures
|
||||
```python
|
||||
try:
|
||||
self.record_learning_event(learning_type, scope, context, pattern, effectiveness_score)
|
||||
except Exception as e:
|
||||
# Log error but continue operation
|
||||
logger.log_error("learning_engine", f"Failed to record learning event: {e}")
|
||||
# Return dummy learning ID for caller consistency
|
||||
return f"learning_failed_{int(time.time())}"
|
||||
```
|
||||
|
||||
### Adaptation Application Failures
|
||||
- **Context Matching Errors**: Skip problematic adaptations, continue with others
|
||||
- **Modification Application Errors**: Log warning, apply partial modifications
|
||||
- **Effectiveness Tracking Errors**: Continue without tracking, log for later analysis
|
||||
|
||||
### Data Persistence Failures
|
||||
- **File Write Errors**: Cache in memory, retry on next operation
|
||||
- **Data Corruption**: Use backup files, regenerate from memory if needed
|
||||
- **Permission Errors**: Fall back to temporary storage, warn user
|
||||
|
||||
## Configuration Requirements
|
||||
|
||||
### Learning Configuration
|
||||
```yaml
|
||||
learning_engine:
|
||||
enabled: true
|
||||
cache_directory: "cache/learning"
|
||||
max_learning_records: 10000
|
||||
max_adaptations: 1000
|
||||
cleanup_interval_days: 30
|
||||
|
||||
thresholds:
|
||||
significant_effectiveness: 0.7
|
||||
significant_confidence: 0.6
|
||||
adaptation_usage_threshold: 5
|
||||
|
||||
insights:
|
||||
min_records_for_analysis: 10
|
||||
min_pattern_occurrences: 3
|
||||
confidence_threshold: 0.6
|
||||
```
|
||||
|
||||
### Adaptation Scopes
|
||||
```yaml
|
||||
adaptation_scopes:
|
||||
session:
|
||||
enabled: true
|
||||
max_adaptations: 100
|
||||
|
||||
project:
|
||||
enabled: true
|
||||
max_adaptations: 500
|
||||
|
||||
user:
|
||||
enabled: true
|
||||
max_adaptations: 1000
|
||||
|
||||
global:
|
||||
enabled: false # Privacy-sensitive, disabled by default
|
||||
anonymization_required: true
|
||||
```
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Learning Integration
|
||||
```python
|
||||
learning_engine = LearningEngine(cache_dir=Path("cache/learning"))
|
||||
|
||||
# Record successful MCP server selection
|
||||
learning_engine.record_learning_event(
|
||||
LearningType.USER_PREFERENCE,
|
||||
AdaptationScope.USER,
|
||||
context={'operation_type': 'analyze', 'complexity_score': 0.7},
|
||||
pattern={'mcp_server': 'sequential'},
|
||||
effectiveness_score=0.9
|
||||
)
|
||||
|
||||
# Apply learned preferences
|
||||
recommendations = learning_engine.apply_adaptations(
|
||||
context={'operation_type': 'analyze', 'complexity_score': 0.7},
|
||||
base_recommendations={'recommended_mcp_servers': ['morphllm']}
|
||||
)
|
||||
print(recommendations['recommended_mcp_servers']) # ['sequential', 'morphllm']
|
||||
```
|
||||
|
||||
### Performance Optimization Learning
|
||||
```python
|
||||
# Record performance optimization success
|
||||
learning_engine.record_learning_event(
|
||||
LearningType.PERFORMANCE_OPTIMIZATION,
|
||||
AdaptationScope.PROJECT,
|
||||
context={'file_count': 25, 'operation_type': 'refactor'},
|
||||
pattern={'delegation': 'auto', 'flags': ['--delegate', 'auto']},
|
||||
effectiveness_score=0.85,
|
||||
metadata={'time_saved_ms': 3000, 'quality_preserved': 0.95}
|
||||
)
|
||||
|
||||
# Generate performance insights
|
||||
insights = learning_engine.generate_learning_insights()
|
||||
performance_insights = [i for i in insights if i.insight_type == "performance_optimization"]
|
||||
```
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **yaml_loader**: Configuration loading for learning settings
|
||||
- **Standard Libraries**: json, time, statistics, pathlib, typing, dataclasses, enum
|
||||
|
||||
### Framework Integration
|
||||
- **Cross-Hook Learning**: Shared learning across all 7 hook implementations
|
||||
- **Pattern Recognition**: Integration with pattern_detection.py for enhanced recommendations
|
||||
- **Performance Monitoring**: Effectiveness tracking for framework optimization
|
||||
|
||||
### Hook Coordination
|
||||
- Used by all hooks for consistent learning and adaptation
|
||||
- Provides standardized learning interfaces and effectiveness tracking
|
||||
- Enables cross-hook knowledge sharing and personalization
|
||||
|
||||
---
|
||||
|
||||
*This module serves as the intelligent learning and adaptation system for the SuperClaude framework, enabling continuous improvement through user preference learning, pattern recognition, and effectiveness feedback integration across all hook operations.*
|
||||
@@ -1,688 +0,0 @@
|
||||
# logger.py - Structured Logging Utilities for SuperClaude Hooks
|
||||
|
||||
## Overview
|
||||
|
||||
The `logger.py` module provides structured logging of hook events for later analysis, focusing on capturing hook lifecycle, decisions, and errors in a structured JSON format. It implements simple, efficient logging without complex features, prioritizing performance and structured data collection for operational analysis and debugging.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **Hook Lifecycle Logging**: Structured capture of hook start/end events with timing
|
||||
- **Decision Logging**: Record decision-making processes and rationale within hooks
|
||||
- **Error Logging**: Comprehensive error capture with context and recovery information
|
||||
- **Performance Monitoring**: Timing and performance metrics collection for optimization
|
||||
- **Session Tracking**: Correlation of events across hook executions with session IDs
|
||||
|
||||
### Design Philosophy
|
||||
- **Structured Data**: JSON-formatted logs for machine readability and analysis
|
||||
- **Performance First**: Minimal overhead with efficient logging operations
|
||||
- **Operational Focus**: Data collection for debugging and operational insights
|
||||
- **Simple Interface**: Easy integration with hooks without complex configuration
|
||||
|
||||
## Core Architecture
|
||||
|
||||
### HookLogger Class
|
||||
```python
|
||||
class HookLogger:
|
||||
"""Simple logger for SuperClaude-Lite hooks."""
|
||||
|
||||
def __init__(self, log_dir: str = None, retention_days: int = None):
|
||||
"""
|
||||
Initialize the logger.
|
||||
|
||||
Args:
|
||||
log_dir: Directory to store log files. Defaults to cache/logs/
|
||||
retention_days: Number of days to keep log files. Defaults to 30.
|
||||
"""
|
||||
```
|
||||
|
||||
### Initialization and Configuration
|
||||
```python
|
||||
def __init__(self, log_dir: str = None, retention_days: int = None):
|
||||
# Load configuration
|
||||
self.config = self._load_config()
|
||||
|
||||
# Check if logging is enabled
|
||||
if not self.config.get('logging', {}).get('enabled', True):
|
||||
self.enabled = False
|
||||
return
|
||||
|
||||
self.enabled = True
|
||||
|
||||
# Set up log directory
|
||||
if log_dir is None:
|
||||
root_dir = Path(__file__).parent.parent.parent
|
||||
log_dir_config = self.config.get('logging', {}).get('file_settings', {}).get('log_directory', 'cache/logs')
|
||||
log_dir = root_dir / log_dir_config
|
||||
|
||||
self.log_dir = Path(log_dir)
|
||||
self.log_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Session ID for correlating events
|
||||
self.session_id = str(uuid.uuid4())[:8]
|
||||
```
|
||||
|
||||
**Initialization Features**:
|
||||
- **Configurable Enablement**: Logging can be disabled via configuration
|
||||
- **Flexible Directory**: Log directory configurable via parameter or configuration
|
||||
- **Session Correlation**: Unique session ID for event correlation
|
||||
- **Automatic Cleanup**: Old log file cleanup on initialization
|
||||
|
||||
## Configuration Management
|
||||
|
||||
### Configuration Loading
|
||||
```python
|
||||
def _load_config(self) -> Dict[str, Any]:
|
||||
"""Load logging configuration from YAML file."""
|
||||
if UnifiedConfigLoader is None:
|
||||
# Return default configuration if loader not available
|
||||
return {
|
||||
'logging': {
|
||||
'enabled': True,
|
||||
'level': 'INFO',
|
||||
'file_settings': {
|
||||
'log_directory': 'cache/logs',
|
||||
'retention_days': 30
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
try:
|
||||
# Get project root
|
||||
root_dir = Path(__file__).parent.parent.parent
|
||||
loader = UnifiedConfigLoader(root_dir)
|
||||
|
||||
# Load logging configuration
|
||||
config = loader.load_yaml('logging')
|
||||
return config or {}
|
||||
except Exception:
|
||||
# Return default configuration on error
|
||||
return {
|
||||
'logging': {
|
||||
'enabled': True,
|
||||
'level': 'INFO',
|
||||
'file_settings': {
|
||||
'log_directory': 'cache/logs',
|
||||
'retention_days': 30
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Configuration Fallback Strategy**:
|
||||
1. **Primary**: Load from logging.yaml via UnifiedConfigLoader
|
||||
2. **Fallback**: Use hardcoded default configuration if loader unavailable
|
||||
3. **Error Recovery**: Default configuration on any loading error
|
||||
4. **Graceful Degradation**: Continue operation even with configuration issues
|
||||
|
||||
### Configuration Structure
|
||||
```python
|
||||
default_config = {
|
||||
'logging': {
|
||||
'enabled': True, # Enable/disable logging
|
||||
'level': 'INFO', # Log level (DEBUG, INFO, WARNING, ERROR)
|
||||
'file_settings': {
|
||||
'log_directory': 'cache/logs', # Log file directory
|
||||
'retention_days': 30 # Days to keep log files
|
||||
}
|
||||
},
|
||||
'hook_configuration': {
|
||||
'hook_name': {
|
||||
'enabled': True # Per-hook logging control
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## Python Logger Integration
|
||||
|
||||
### Logger Setup
|
||||
```python
|
||||
def _setup_logger(self):
|
||||
"""Set up the Python logger with JSON formatting."""
|
||||
self.logger = logging.getLogger("superclaude_lite_hooks")
|
||||
|
||||
# Set log level from configuration
|
||||
log_level_str = self.config.get('logging', {}).get('level', 'INFO').upper()
|
||||
log_level = getattr(logging, log_level_str, logging.INFO)
|
||||
self.logger.setLevel(log_level)
|
||||
|
||||
# Remove existing handlers to avoid duplicates
|
||||
self.logger.handlers.clear()
|
||||
|
||||
# Create daily log file
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
log_file = self.log_dir / f"superclaude-lite-{today}.log"
|
||||
|
||||
# File handler
|
||||
handler = logging.FileHandler(log_file, mode='a', encoding='utf-8')
|
||||
handler.setLevel(logging.INFO)
|
||||
|
||||
# Simple formatter - just output the message (which is already JSON)
|
||||
formatter = logging.Formatter('%(message)s')
|
||||
handler.setFormatter(formatter)
|
||||
|
||||
self.logger.addHandler(handler)
|
||||
```
|
||||
|
||||
**Logger Features**:
|
||||
- **Daily Log Files**: Separate log file per day for easy management
|
||||
- **JSON Message Format**: Messages are pre-formatted JSON for structure
|
||||
- **UTF-8 Encoding**: Support for international characters
|
||||
- **Configurable Log Level**: Log level set from configuration
|
||||
- **Handler Management**: Automatic cleanup of duplicate handlers
|
||||
|
||||
## Structured Event Logging
|
||||
|
||||
### Event Structure
|
||||
```python
|
||||
def _create_event(self, event_type: str, hook_name: str, data: Dict[str, Any] = None) -> Dict[str, Any]:
|
||||
"""Create a structured event."""
|
||||
event = {
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
"session": self.session_id,
|
||||
"hook": hook_name,
|
||||
"event": event_type
|
||||
}
|
||||
|
||||
if data:
|
||||
event["data"] = data
|
||||
|
||||
return event
|
||||
```
|
||||
|
||||
**Event Structure Components**:
|
||||
- **timestamp**: ISO 8601 UTC timestamp for precise timing
|
||||
- **session**: 8-character session ID for event correlation
|
||||
- **hook**: Hook name for operation identification
|
||||
- **event**: Event type (start, end, decision, error)
|
||||
- **data**: Optional additional data specific to event type
|
||||
|
||||
### Event Filtering
|
||||
```python
|
||||
def _should_log_event(self, hook_name: str, event_type: str) -> bool:
|
||||
"""Check if this event should be logged based on configuration."""
|
||||
if not self.enabled:
|
||||
return False
|
||||
|
||||
# Check hook-specific configuration
|
||||
hook_config = self.config.get('hook_configuration', {}).get(hook_name, {})
|
||||
if not hook_config.get('enabled', True):
|
||||
return False
|
||||
|
||||
# Check event type configuration
|
||||
hook_logging = self.config.get('logging', {}).get('hook_logging', {})
|
||||
event_mapping = {
|
||||
'start': 'log_lifecycle',
|
||||
'end': 'log_lifecycle',
|
||||
'decision': 'log_decisions',
|
||||
'error': 'log_errors'
|
||||
}
|
||||
|
||||
config_key = event_mapping.get(event_type, 'log_lifecycle')
|
||||
return hook_logging.get(config_key, True)
|
||||
```
|
||||
|
||||
**Filtering Logic**:
|
||||
1. **Global Enable Check**: Respect global logging enabled/disabled setting
|
||||
2. **Hook-Specific Check**: Allow per-hook logging control
|
||||
3. **Event Type Check**: Filter by event type (lifecycle, decisions, errors)
|
||||
4. **Default Behavior**: Log all events if configuration not specified
|
||||
|
||||
## Core Logging Methods
|
||||
|
||||
### Hook Lifecycle Logging
|
||||
```python
|
||||
def log_hook_start(self, hook_name: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log the start of a hook execution."""
|
||||
if not self._should_log_event(hook_name, 'start'):
|
||||
return
|
||||
|
||||
event = self._create_event("start", hook_name, context)
|
||||
self.logger.info(json.dumps(event))
|
||||
|
||||
def log_hook_end(self, hook_name: str, duration_ms: int, success: bool, result: Optional[Dict[str, Any]] = None):
|
||||
"""Log the end of a hook execution."""
|
||||
if not self._should_log_event(hook_name, 'end'):
|
||||
return
|
||||
|
||||
data = {
|
||||
"duration_ms": duration_ms,
|
||||
"success": success
|
||||
}
|
||||
if result:
|
||||
data["result"] = result
|
||||
|
||||
event = self._create_event("end", hook_name, data)
|
||||
self.logger.info(json.dumps(event))
|
||||
```
|
||||
|
||||
**Lifecycle Event Data**:
|
||||
- **Start Events**: Hook name, optional context data, timestamp
|
||||
- **End Events**: Duration in milliseconds, success/failure status, optional results
|
||||
- **Session Correlation**: All events include session ID for correlation
|
||||
|
||||
### Decision Logging
|
||||
```python
|
||||
def log_decision(self, hook_name: str, decision_type: str, choice: str, reason: str):
|
||||
"""Log a decision made by a hook."""
|
||||
if not self._should_log_event(hook_name, 'decision'):
|
||||
return
|
||||
|
||||
data = {
|
||||
"type": decision_type,
|
||||
"choice": choice,
|
||||
"reason": reason
|
||||
}
|
||||
event = self._create_event("decision", hook_name, data)
|
||||
self.logger.info(json.dumps(event))
|
||||
```
|
||||
|
||||
**Decision Event Components**:
|
||||
- **type**: Category of decision (e.g., "mcp_server_selection", "mode_activation")
|
||||
- **choice**: The decision made (e.g., "sequential", "brainstorming_mode")
|
||||
- **reason**: Explanation for the decision (e.g., "complexity_score > 0.6")
|
||||
|
||||
### Error Logging
|
||||
```python
|
||||
def log_error(self, hook_name: str, error: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log an error that occurred in a hook."""
|
||||
if not self._should_log_event(hook_name, 'error'):
|
||||
return
|
||||
|
||||
data = {
|
||||
"error": error
|
||||
}
|
||||
if context:
|
||||
data["context"] = context
|
||||
|
||||
event = self._create_event("error", hook_name, data)
|
||||
self.logger.info(json.dumps(event))
|
||||
```
|
||||
|
||||
**Error Event Components**:
|
||||
- **error**: Error message or description
|
||||
- **context**: Optional additional context about error conditions
|
||||
- **Timestamp**: Precise timing for error correlation
|
||||
|
||||
## Log File Management
|
||||
|
||||
### Automatic Cleanup
|
||||
```python
|
||||
def _cleanup_old_logs(self):
|
||||
"""Remove log files older than retention_days."""
|
||||
if self.retention_days <= 0:
|
||||
return
|
||||
|
||||
cutoff_date = datetime.now() - timedelta(days=self.retention_days)
|
||||
|
||||
# Find all log files
|
||||
log_pattern = self.log_dir / "superclaude-lite-*.log"
|
||||
for log_file in glob.glob(str(log_pattern)):
|
||||
try:
|
||||
# Extract date from filename
|
||||
filename = os.path.basename(log_file)
|
||||
date_str = filename.replace("superclaude-lite-", "").replace(".log", "")
|
||||
file_date = datetime.strptime(date_str, "%Y-%m-%d")
|
||||
|
||||
# Remove if older than cutoff
|
||||
if file_date < cutoff_date:
|
||||
os.remove(log_file)
|
||||
|
||||
except (ValueError, OSError):
|
||||
# Skip files that don't match expected format or can't be removed
|
||||
continue
|
||||
```
|
||||
|
||||
**Cleanup Features**:
|
||||
- **Configurable Retention**: Retention period set via configuration
|
||||
- **Date-Based**: Log files named with YYYY-MM-DD format for easy parsing
|
||||
- **Error Resilience**: Skip problematic files rather than failing entire cleanup
|
||||
- **Initialization Cleanup**: Cleanup performed during logger initialization
|
||||
|
||||
### Log File Naming Convention
|
||||
```
|
||||
superclaude-lite-2024-12-15.log
|
||||
superclaude-lite-2024-12-16.log
|
||||
superclaude-lite-2024-12-17.log
|
||||
```
|
||||
|
||||
**Naming Benefits**:
|
||||
- **Chronological Sorting**: Files sort naturally by name
|
||||
- **Easy Filtering**: Date-based filtering for log analysis
|
||||
- **Rotation-Friendly**: Daily rotation without complex log rotation tools
|
||||
|
||||
## Global Interface and Convenience Functions
|
||||
|
||||
### Global Logger Instance
|
||||
```python
|
||||
# Global logger instance
|
||||
_logger = None
|
||||
|
||||
def get_logger() -> HookLogger:
|
||||
"""Get the global logger instance."""
|
||||
global _logger
|
||||
if _logger is None:
|
||||
_logger = HookLogger()
|
||||
return _logger
|
||||
```
|
||||
|
||||
### Convenience Functions
|
||||
```python
|
||||
def log_hook_start(hook_name: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log the start of a hook execution."""
|
||||
get_logger().log_hook_start(hook_name, context)
|
||||
|
||||
def log_hook_end(hook_name: str, duration_ms: int, success: bool, result: Optional[Dict[str, Any]] = None):
|
||||
"""Log the end of a hook execution."""
|
||||
get_logger().log_hook_end(hook_name, duration_ms, success, result)
|
||||
|
||||
def log_decision(hook_name: str, decision_type: str, choice: str, reason: str):
|
||||
"""Log a decision made by a hook."""
|
||||
get_logger().log_decision(hook_name, decision_type, choice, reason)
|
||||
|
||||
def log_error(hook_name: str, error: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log an error that occurred in a hook."""
|
||||
get_logger().log_error(hook_name, error, context)
|
||||
```
|
||||
|
||||
**Global Interface Benefits**:
|
||||
- **Simplified Import**: Single import for all logging functions
|
||||
- **Consistent Configuration**: Shared configuration across all hooks
|
||||
- **Lazy Initialization**: Logger created only when first used
|
||||
- **Memory Efficiency**: Single logger instance for entire application
|
||||
|
||||
## Hook Integration Patterns
|
||||
|
||||
### Basic Hook Integration
|
||||
```python
|
||||
from shared.logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
import time
|
||||
|
||||
def pre_tool_use_hook(context):
|
||||
start_time = time.time()
|
||||
|
||||
# Log hook start
|
||||
log_hook_start("pre_tool_use", {"operation_type": context.get("operation_type")})
|
||||
|
||||
try:
|
||||
# Hook logic
|
||||
if context.get("complexity_score", 0) > 0.6:
|
||||
# Log decision
|
||||
log_decision("pre_tool_use", "delegation_activation", "enabled", "complexity_score > 0.6")
|
||||
result = {"delegation_enabled": True}
|
||||
else:
|
||||
result = {"delegation_enabled": False}
|
||||
|
||||
# Log successful completion
|
||||
duration_ms = int((time.time() - start_time) * 1000)
|
||||
log_hook_end("pre_tool_use", duration_ms, True, result)
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
log_error("pre_tool_use", str(e), {"context": context})
|
||||
|
||||
# Log failed completion
|
||||
duration_ms = int((time.time() - start_time) * 1000)
|
||||
log_hook_end("pre_tool_use", duration_ms, False)
|
||||
|
||||
raise
|
||||
```
|
||||
|
||||
### Advanced Integration with Context
|
||||
```python
|
||||
def session_start_hook(context):
|
||||
# Start with rich context
|
||||
log_hook_start("session_start", {
|
||||
"project_path": context.get("project_path"),
|
||||
"user_expertise": context.get("user_expertise", "intermediate"),
|
||||
"session_type": context.get("session_type", "interactive")
|
||||
})
|
||||
|
||||
# Log multiple decisions
|
||||
log_decision("session_start", "configuration_load", "superclaude-config.json", "project configuration detected")
|
||||
log_decision("session_start", "learning_engine", "enabled", "user preference learning available")
|
||||
|
||||
# Complex result logging
|
||||
result = {
|
||||
"configuration_loaded": True,
|
||||
"hooks_initialized": 7,
|
||||
"performance_targets": {
|
||||
"session_start_ms": 50,
|
||||
"pre_tool_use_ms": 200
|
||||
}
|
||||
}
|
||||
|
||||
log_hook_end("session_start", 45, True, result)
|
||||
```
|
||||
|
||||
## Log Analysis and Monitoring
|
||||
|
||||
### Log Entry Format
|
||||
```json
|
||||
{
|
||||
"timestamp": "2024-12-15T14:30:22.123456Z",
|
||||
"session": "abc12345",
|
||||
"hook": "pre_tool_use",
|
||||
"event": "start",
|
||||
"data": {
|
||||
"operation_type": "build",
|
||||
"complexity_score": 0.7
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Example Log Sequence
|
||||
```json
|
||||
{"timestamp": "2024-12-15T14:30:22.123Z", "session": "abc12345", "hook": "pre_tool_use", "event": "start", "data": {"operation_type": "build"}}
|
||||
{"timestamp": "2024-12-15T14:30:22.125Z", "session": "abc12345", "hook": "pre_tool_use", "event": "decision", "data": {"type": "mcp_server_selection", "choice": "sequential", "reason": "complex analysis required"}}
|
||||
{"timestamp": "2024-12-15T14:30:22.148Z", "session": "abc12345", "hook": "pre_tool_use", "event": "end", "data": {"duration_ms": 25, "success": true, "result": {"mcp_servers": ["sequential"]}}}
|
||||
```
|
||||
|
||||
### Analysis Queries
|
||||
```bash
|
||||
# Find all errors in the last day
|
||||
jq 'select(.event == "error")' superclaude-lite-2024-12-15.log
|
||||
|
||||
# Calculate average hook execution times
|
||||
jq 'select(.event == "end") | .data.duration_ms' superclaude-lite-2024-12-15.log | awk '{sum+=$1; count++} END {print sum/count}'
|
||||
|
||||
# Find all decisions made by specific hook
|
||||
jq 'select(.hook == "pre_tool_use" and .event == "decision")' superclaude-lite-2024-12-15.log
|
||||
|
||||
# Track session completion rates
|
||||
jq 'select(.hook == "session_start" and .event == "end") | .data.success' superclaude-lite-2024-12-15.log
|
||||
```
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Logging Performance
|
||||
- **Event Creation**: <1ms for structured event creation
|
||||
- **File Writing**: <5ms for typical log entry with JSON serialization
|
||||
- **Configuration Loading**: <10ms during initialization
|
||||
- **Cleanup Operations**: <50ms for cleanup of old log files (depends on file count)
|
||||
|
||||
### Memory Efficiency
|
||||
- **Logger Instance**: ~1-2KB for logger instance with configuration
|
||||
- **Session Tracking**: ~100B for session ID and correlation data
|
||||
- **Event Buffer**: Direct write-through, no event buffering for reliability
|
||||
- **Configuration Cache**: ~500B for logging configuration
|
||||
|
||||
### File System Impact
|
||||
- **Daily Log Files**: Automatic daily rotation with configurable retention
|
||||
- **Log File Size**: Typical ~10-50KB per day depending on hook activity
|
||||
- **Directory Structure**: Simple flat file structure in configurable directory
|
||||
- **Cleanup Efficiency**: O(n) cleanup where n is number of log files
|
||||
|
||||
## Error Handling and Reliability
|
||||
|
||||
### Logging Error Handling
|
||||
```python
|
||||
def log_hook_start(self, hook_name: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log the start of a hook execution."""
|
||||
try:
|
||||
if not self._should_log_event(hook_name, 'start'):
|
||||
return
|
||||
|
||||
event = self._create_event("start", hook_name, context)
|
||||
self.logger.info(json.dumps(event))
|
||||
except Exception:
|
||||
# Silent failure - logging should never break hook execution
|
||||
pass
|
||||
```
|
||||
|
||||
### Reliability Features
|
||||
- **Silent Failure**: Logging errors never interrupt hook execution
|
||||
- **Graceful Degradation**: Continue operation even if logging fails
|
||||
- **Configuration Fallback**: Default configuration if loading fails
|
||||
- **File System Resilience**: Handle permission errors and disk space issues
|
||||
|
||||
### Recovery Mechanisms
|
||||
- **Logger Recreation**: Recreate logger if file handle issues occur
|
||||
- **Directory Creation**: Automatically create log directory if missing
|
||||
- **Permission Handling**: Graceful fallback if log directory not writable
|
||||
- **Disk Space**: Continue operation even if disk space limited
|
||||
|
||||
## Configuration Examples
|
||||
|
||||
### Basic Configuration (logging.yaml)
|
||||
```yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: INFO
|
||||
|
||||
file_settings:
|
||||
log_directory: cache/logs
|
||||
retention_days: 30
|
||||
|
||||
hook_logging:
|
||||
log_lifecycle: true
|
||||
log_decisions: true
|
||||
log_errors: true
|
||||
```
|
||||
|
||||
### Advanced Configuration
|
||||
```yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: DEBUG
|
||||
|
||||
file_settings:
|
||||
log_directory: ${LOG_DIR:./logs}
|
||||
retention_days: ${LOG_RETENTION:7}
|
||||
max_file_size_mb: 10
|
||||
|
||||
hook_logging:
|
||||
log_lifecycle: true
|
||||
log_decisions: true
|
||||
log_errors: true
|
||||
log_performance: true
|
||||
|
||||
hook_configuration:
|
||||
session_start:
|
||||
enabled: true
|
||||
pre_tool_use:
|
||||
enabled: true
|
||||
post_tool_use:
|
||||
enabled: false # Disable logging for this hook
|
||||
pre_compact:
|
||||
enabled: true
|
||||
```
|
||||
|
||||
### Production Configuration
|
||||
```yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: WARNING # Reduce verbosity in production
|
||||
|
||||
file_settings:
|
||||
log_directory: /var/log/superclaude
|
||||
retention_days: 90
|
||||
|
||||
hook_logging:
|
||||
log_lifecycle: false # Disable lifecycle logging
|
||||
log_decisions: true # Keep decision logging
|
||||
log_errors: true # Always log errors
|
||||
```
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Logging
|
||||
```python
|
||||
from shared.logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
|
||||
# Simple hook with logging
|
||||
def my_hook(context):
|
||||
log_hook_start("my_hook")
|
||||
|
||||
try:
|
||||
# Do work
|
||||
result = perform_operation()
|
||||
log_hook_end("my_hook", 150, True, {"result": result})
|
||||
return result
|
||||
except Exception as e:
|
||||
log_error("my_hook", str(e))
|
||||
log_hook_end("my_hook", 150, False)
|
||||
raise
|
||||
```
|
||||
|
||||
### Decision Logging
|
||||
```python
|
||||
def intelligent_hook(context):
|
||||
log_hook_start("intelligent_hook", {"complexity": context.get("complexity_score")})
|
||||
|
||||
# Log decision-making process
|
||||
if context.get("complexity_score", 0) > 0.6:
|
||||
log_decision("intelligent_hook", "server_selection", "sequential", "high complexity detected")
|
||||
server = "sequential"
|
||||
else:
|
||||
log_decision("intelligent_hook", "server_selection", "morphllm", "low complexity operation")
|
||||
server = "morphllm"
|
||||
|
||||
log_hook_end("intelligent_hook", 85, True, {"selected_server": server})
|
||||
```
|
||||
|
||||
### Error Context Logging
|
||||
```python
|
||||
def error_prone_hook(context):
|
||||
log_hook_start("error_prone_hook")
|
||||
|
||||
try:
|
||||
risky_operation()
|
||||
except SpecificError as e:
|
||||
log_error("error_prone_hook", f"Specific error: {e}", {
|
||||
"context": context,
|
||||
"error_type": "SpecificError",
|
||||
"recovery_attempted": True
|
||||
})
|
||||
# Attempt recovery
|
||||
recovery_operation()
|
||||
except Exception as e:
|
||||
log_error("error_prone_hook", f"Unexpected error: {e}", {
|
||||
"context": context,
|
||||
"error_type": type(e).__name__
|
||||
})
|
||||
raise
|
||||
```
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **yaml_loader**: Configuration loading (optional, fallback available)
|
||||
- **Standard Libraries**: json, logging, os, time, datetime, pathlib, glob, uuid
|
||||
|
||||
### Framework Integration
|
||||
- **Hook Lifecycle**: Integrated into all 7 SuperClaude hooks for consistent logging
|
||||
- **Global Interface**: Shared logger instance across all hooks and modules
|
||||
- **Configuration Management**: Unified configuration via yaml_loader integration
|
||||
|
||||
### External Analysis
|
||||
- **JSON Format**: Structured logs for analysis with jq, logstash, elasticsearch
|
||||
- **Daily Rotation**: Compatible with log analysis tools expecting daily files
|
||||
- **Session Correlation**: Event correlation for debugging and monitoring
|
||||
|
||||
---
|
||||
|
||||
*This module provides the essential logging infrastructure for the SuperClaude framework, enabling comprehensive operational monitoring, debugging, and analysis through structured, high-performance event logging with reliable error handling and flexible configuration.*
|
||||
@@ -1,631 +0,0 @@
|
||||
# mcp_intelligence.py - Intelligent MCP Server Management Engine
|
||||
|
||||
## Overview
|
||||
|
||||
The `mcp_intelligence.py` module provides intelligent MCP server activation, coordination, and optimization based on ORCHESTRATOR.md patterns and real-time context analysis. It implements smart server selection, performance-optimized activation sequences, fallback strategies, cross-server coordination, and real-time adaptation based on effectiveness metrics.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **Smart Server Selection**: Context-aware MCP server recommendation and activation
|
||||
- **Performance Optimization**: Optimized activation sequences with cost/benefit analysis
|
||||
- **Fallback Strategy Management**: Robust error handling with alternative server routing
|
||||
- **Cross-Server Coordination**: Intelligent coordination strategies for multi-server operations
|
||||
- **Real-Time Adaptation**: Dynamic adaptation based on server effectiveness and availability
|
||||
|
||||
### Intelligence Capabilities
|
||||
- **Hybrid Intelligence Routing**: Morphllm vs Serena decision matrix based on complexity
|
||||
- **Resource-Aware Activation**: Adaptive server selection based on resource constraints
|
||||
- **Performance Monitoring**: Real-time tracking of activation costs and effectiveness
|
||||
- **Coordination Strategy Selection**: Dynamic coordination patterns based on operation characteristics
|
||||
|
||||
## Core Classes and Data Structures
|
||||
|
||||
### Enumerations
|
||||
|
||||
#### MCPServerState
|
||||
```python
|
||||
class MCPServerState(Enum):
|
||||
AVAILABLE = "available" # Server ready for activation
|
||||
UNAVAILABLE = "unavailable" # Server not accessible
|
||||
LOADING = "loading" # Server currently activating
|
||||
ERROR = "error" # Server in error state
|
||||
```
|
||||
|
||||
### Data Classes
|
||||
|
||||
#### MCPServerCapability
|
||||
```python
|
||||
@dataclass
|
||||
class MCPServerCapability:
|
||||
server_name: str # Server identifier
|
||||
primary_functions: List[str] # Core capabilities list
|
||||
performance_profile: str # lightweight|standard|intensive
|
||||
activation_cost_ms: int # Activation time in milliseconds
|
||||
token_efficiency: float # 0.0 to 1.0 efficiency rating
|
||||
quality_impact: float # 0.0 to 1.0 quality improvement rating
|
||||
```
|
||||
|
||||
#### MCPActivationPlan
|
||||
```python
|
||||
@dataclass
|
||||
class MCPActivationPlan:
|
||||
servers_to_activate: List[str] # Servers to enable
|
||||
activation_order: List[str] # Optimal activation sequence
|
||||
estimated_cost_ms: int # Total activation time estimate
|
||||
efficiency_gains: Dict[str, float] # Expected gains per server
|
||||
fallback_strategy: Dict[str, str] # Fallback mappings
|
||||
coordination_strategy: str # Coordination approach
|
||||
```
|
||||
|
||||
## Server Capability Definitions
|
||||
|
||||
### Server Specifications
|
||||
```python
|
||||
def _load_server_capabilities(self) -> Dict[str, MCPServerCapability]:
|
||||
capabilities = {}
|
||||
|
||||
capabilities['context7'] = MCPServerCapability(
|
||||
server_name='context7',
|
||||
primary_functions=['library_docs', 'framework_patterns', 'best_practices'],
|
||||
performance_profile='standard',
|
||||
activation_cost_ms=150,
|
||||
token_efficiency=0.8,
|
||||
quality_impact=0.9
|
||||
)
|
||||
|
||||
capabilities['sequential'] = MCPServerCapability(
|
||||
server_name='sequential',
|
||||
primary_functions=['complex_analysis', 'multi_step_reasoning', 'debugging'],
|
||||
performance_profile='intensive',
|
||||
activation_cost_ms=200,
|
||||
token_efficiency=0.6,
|
||||
quality_impact=0.95
|
||||
)
|
||||
|
||||
capabilities['magic'] = MCPServerCapability(
|
||||
server_name='magic',
|
||||
primary_functions=['ui_components', 'design_systems', 'frontend_generation'],
|
||||
performance_profile='standard',
|
||||
activation_cost_ms=120,
|
||||
token_efficiency=0.85,
|
||||
quality_impact=0.9
|
||||
)
|
||||
|
||||
capabilities['playwright'] = MCPServerCapability(
|
||||
server_name='playwright',
|
||||
primary_functions=['e2e_testing', 'browser_automation', 'performance_testing'],
|
||||
performance_profile='intensive',
|
||||
activation_cost_ms=300,
|
||||
token_efficiency=0.7,
|
||||
quality_impact=0.85
|
||||
)
|
||||
|
||||
capabilities['morphllm'] = MCPServerCapability(
|
||||
server_name='morphllm',
|
||||
primary_functions=['intelligent_editing', 'pattern_application', 'fast_apply'],
|
||||
performance_profile='lightweight',
|
||||
activation_cost_ms=80,
|
||||
token_efficiency=0.9,
|
||||
quality_impact=0.8
|
||||
)
|
||||
|
||||
capabilities['serena'] = MCPServerCapability(
|
||||
server_name='serena',
|
||||
primary_functions=['semantic_analysis', 'project_context', 'memory_management'],
|
||||
performance_profile='standard',
|
||||
activation_cost_ms=100,
|
||||
token_efficiency=0.75,
|
||||
quality_impact=0.95
|
||||
)
|
||||
```
|
||||
|
||||
## Intelligent Activation Planning
|
||||
|
||||
### create_activation_plan()
|
||||
```python
|
||||
def create_activation_plan(self,
|
||||
user_input: str,
|
||||
context: Dict[str, Any],
|
||||
operation_data: Dict[str, Any]) -> MCPActivationPlan:
|
||||
```
|
||||
|
||||
**Planning Pipeline**:
|
||||
1. **Pattern Detection**: Use PatternDetector to identify server needs
|
||||
2. **Intelligent Optimization**: Apply context-aware server selection
|
||||
3. **Activation Sequencing**: Calculate optimal activation order
|
||||
4. **Cost Estimation**: Predict activation costs and efficiency gains
|
||||
5. **Fallback Strategy**: Create robust error handling plan
|
||||
6. **Coordination Strategy**: Determine multi-server coordination approach
|
||||
|
||||
### Server Selection Optimization
|
||||
|
||||
#### Hybrid Intelligence Decision Matrix
|
||||
```python
|
||||
def _optimize_server_selection(self,
|
||||
recommended_servers: List[str],
|
||||
context: Dict[str, Any],
|
||||
operation_data: Dict[str, Any]) -> List[str]:
|
||||
|
||||
# Morphllm vs Serena intelligence selection
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
complexity_score = operation_data.get('complexity_score', 0.0)
|
||||
|
||||
if 'morphllm' in optimized and 'serena' in optimized:
|
||||
# Choose the more appropriate server based on complexity
|
||||
if file_count > 10 or complexity_score > 0.6:
|
||||
optimized.remove('morphllm') # Use Serena for complex operations
|
||||
else:
|
||||
optimized.remove('serena') # Use Morphllm for efficient operations
|
||||
```
|
||||
|
||||
**Decision Criteria**:
|
||||
- **Serena Optimal**: file_count > 10 OR complexity_score > 0.6
|
||||
- **Morphllm Optimal**: file_count ≤ 10 AND complexity_score ≤ 0.6
|
||||
|
||||
#### Resource Constraint Optimization
|
||||
```python
|
||||
# Resource constraint optimization
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
if resource_usage > 85:
|
||||
# Remove intensive servers under resource constraints
|
||||
intensive_servers = {
|
||||
name for name, cap in self.server_capabilities.items()
|
||||
if cap.performance_profile == 'intensive'
|
||||
}
|
||||
optimized -= intensive_servers
|
||||
```
|
||||
|
||||
#### Context-Based Auto-Addition
|
||||
```python
|
||||
# Performance optimization based on operation type
|
||||
operation_type = operation_data.get('operation_type', '')
|
||||
if operation_type in ['read', 'analyze'] and 'sequential' not in optimized:
|
||||
# Add Sequential for analysis operations
|
||||
optimized.add('sequential')
|
||||
|
||||
# Auto-add Context7 if external libraries detected
|
||||
if operation_data.get('has_external_dependencies', False):
|
||||
optimized.add('context7')
|
||||
```
|
||||
|
||||
## Activation Sequencing
|
||||
|
||||
### Optimal Activation Order
|
||||
```python
|
||||
def _calculate_activation_order(self, servers: List[str], context: Dict[str, Any]) -> List[str]:
|
||||
ordered = []
|
||||
|
||||
# 1. Serena first if present (provides context for others)
|
||||
if 'serena' in servers:
|
||||
ordered.append('serena')
|
||||
servers = [s for s in servers if s != 'serena']
|
||||
|
||||
# 2. Context7 early for documentation context
|
||||
if 'context7' in servers:
|
||||
ordered.append('context7')
|
||||
servers = [s for s in servers if s != 'context7']
|
||||
|
||||
# 3. Remaining servers by activation cost (lightweight first)
|
||||
remaining_costs = [
|
||||
(server, self.server_capabilities[server].activation_cost_ms)
|
||||
for server in servers
|
||||
]
|
||||
remaining_costs.sort(key=lambda x: x[1])
|
||||
ordered.extend([server for server, _ in remaining_costs])
|
||||
|
||||
return ordered
|
||||
```
|
||||
|
||||
**Activation Priorities**:
|
||||
1. **Serena**: Provides project context for other servers
|
||||
2. **Context7**: Supplies documentation context early
|
||||
3. **Remaining**: Sorted by activation cost (lightweight → intensive)
|
||||
|
||||
## Performance Estimation
|
||||
|
||||
### Activation Cost Calculation
|
||||
```python
|
||||
def _calculate_activation_cost(self, servers: List[str]) -> int:
|
||||
"""Calculate total activation cost in milliseconds."""
|
||||
return sum(
|
||||
self.server_capabilities[server].activation_cost_ms
|
||||
for server in servers
|
||||
if server in self.server_capabilities
|
||||
)
|
||||
```
|
||||
|
||||
### Efficiency Gains Calculation
|
||||
```python
|
||||
def _calculate_efficiency_gains(self, servers: List[str], operation_data: Dict[str, Any]) -> Dict[str, float]:
|
||||
gains = {}
|
||||
|
||||
for server in servers:
|
||||
capability = self.server_capabilities[server]
|
||||
|
||||
# Base efficiency gain
|
||||
base_gain = capability.token_efficiency * capability.quality_impact
|
||||
|
||||
# Context-specific adjustments
|
||||
if server == 'morphllm' and operation_data.get('file_count', 1) <= 5:
|
||||
gains[server] = base_gain * 1.2 # Extra efficiency for small operations
|
||||
elif server == 'serena' and operation_data.get('complexity_score', 0) > 0.6:
|
||||
gains[server] = base_gain * 1.3 # Extra value for complex operations
|
||||
elif server == 'sequential' and 'debug' in operation_data.get('operation_type', ''):
|
||||
gains[server] = base_gain * 1.4 # Extra value for debugging
|
||||
else:
|
||||
gains[server] = base_gain
|
||||
|
||||
return gains
|
||||
```
|
||||
|
||||
## Fallback Strategy Management
|
||||
|
||||
### Fallback Mappings
|
||||
```python
|
||||
def _create_fallback_strategy(self, servers: List[str]) -> Dict[str, str]:
|
||||
"""Create fallback strategy for server failures."""
|
||||
fallback_map = {
|
||||
'morphllm': 'serena', # Serena can handle editing
|
||||
'serena': 'morphllm', # Morphllm can handle simple edits
|
||||
'sequential': 'context7', # Context7 for documentation-based analysis
|
||||
'context7': 'sequential', # Sequential for complex analysis
|
||||
'magic': 'morphllm', # Morphllm for component generation
|
||||
'playwright': 'sequential' # Sequential for test planning
|
||||
}
|
||||
|
||||
fallbacks = {}
|
||||
for server in servers:
|
||||
fallback = fallback_map.get(server)
|
||||
if fallback and fallback not in servers:
|
||||
fallbacks[server] = fallback
|
||||
else:
|
||||
fallbacks[server] = 'native_tools' # Fall back to native Claude tools
|
||||
|
||||
return fallbacks
|
||||
```
|
||||
|
||||
## Coordination Strategy Selection
|
||||
|
||||
### Strategy Determination
|
||||
```python
|
||||
def _determine_coordination_strategy(self, servers: List[str], operation_data: Dict[str, Any]) -> str:
|
||||
if len(servers) <= 1:
|
||||
return 'single_server'
|
||||
|
||||
# Sequential coordination for complex analysis
|
||||
if 'sequential' in servers and operation_data.get('complexity_score', 0) > 0.6:
|
||||
return 'sequential_lead'
|
||||
|
||||
# Serena coordination for multi-file operations
|
||||
if 'serena' in servers and operation_data.get('file_count', 1) > 5:
|
||||
return 'serena_lead'
|
||||
|
||||
# Parallel coordination for independent operations
|
||||
if len(servers) >= 3:
|
||||
return 'parallel_with_sync'
|
||||
|
||||
return 'collaborative'
|
||||
```
|
||||
|
||||
**Coordination Strategies**:
|
||||
- **single_server**: Single server operation
|
||||
- **sequential_lead**: Sequential server coordinates analysis
|
||||
- **serena_lead**: Serena server coordinates multi-file operations
|
||||
- **parallel_with_sync**: Parallel execution with synchronization points
|
||||
- **collaborative**: Equal collaboration between servers
|
||||
|
||||
## Activation Plan Execution
|
||||
|
||||
### execute_activation_plan()
|
||||
```python
|
||||
def execute_activation_plan(self, plan: MCPActivationPlan, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
start_time = time.time()
|
||||
activated_servers = []
|
||||
failed_servers = []
|
||||
fallback_activations = []
|
||||
|
||||
for server in plan.activation_order:
|
||||
try:
|
||||
# Check server availability
|
||||
if self.server_states.get(server) == MCPServerState.UNAVAILABLE:
|
||||
failed_servers.append(server)
|
||||
self._handle_server_fallback(server, plan, fallback_activations)
|
||||
continue
|
||||
|
||||
# Activate server (simulated - real implementation would call MCP)
|
||||
self.server_states[server] = MCPServerState.LOADING
|
||||
activation_start = time.time()
|
||||
|
||||
# Simulate activation with realistic variance
|
||||
expected_cost = self.server_capabilities[server].activation_cost_ms
|
||||
actual_cost = expected_cost * (0.8 + 0.4 * hash(server) % 1000 / 1000)
|
||||
|
||||
self.server_states[server] = MCPServerState.AVAILABLE
|
||||
activated_servers.append(server)
|
||||
|
||||
# Track performance metrics
|
||||
activation_time = (time.time() - activation_start) * 1000
|
||||
self.performance_metrics[server] = {
|
||||
'last_activation_ms': activation_time,
|
||||
'expected_ms': expected_cost,
|
||||
'efficiency_ratio': expected_cost / max(activation_time, 1)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
failed_servers.append(server)
|
||||
self.server_states[server] = MCPServerState.ERROR
|
||||
self._handle_server_fallback(server, plan, fallback_activations)
|
||||
|
||||
total_time = (time.time() - start_time) * 1000
|
||||
|
||||
return {
|
||||
'activated_servers': activated_servers,
|
||||
'failed_servers': failed_servers,
|
||||
'fallback_activations': fallback_activations,
|
||||
'total_activation_time_ms': total_time,
|
||||
'coordination_strategy': plan.coordination_strategy,
|
||||
'performance_metrics': self.performance_metrics
|
||||
}
|
||||
```
|
||||
|
||||
## Performance Monitoring and Optimization
|
||||
|
||||
### Real-Time Performance Tracking
|
||||
```python
|
||||
# Track activation performance
|
||||
self.performance_metrics[server] = {
|
||||
'last_activation_ms': activation_time,
|
||||
'expected_ms': expected_cost,
|
||||
'efficiency_ratio': expected_cost / max(activation_time, 1)
|
||||
}
|
||||
|
||||
# Maintain activation history
|
||||
self.activation_history.append({
|
||||
'timestamp': time.time(),
|
||||
'plan': plan,
|
||||
'activated': activated_servers,
|
||||
'failed': failed_servers,
|
||||
'fallbacks': fallback_activations,
|
||||
'total_time_ms': total_time
|
||||
})
|
||||
```
|
||||
|
||||
### Optimization Recommendations
|
||||
```python
|
||||
def get_optimization_recommendations(self, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
recommendations = []
|
||||
|
||||
# Analyze recent activation patterns
|
||||
if len(self.activation_history) >= 5:
|
||||
recent_activations = self.activation_history[-5:]
|
||||
|
||||
# Check for frequently failing servers
|
||||
failed_counts = {}
|
||||
for activation in recent_activations:
|
||||
for failed in activation['failed']:
|
||||
failed_counts[failed] = failed_counts.get(failed, 0) + 1
|
||||
|
||||
for server, count in failed_counts.items():
|
||||
if count >= 3:
|
||||
recommendations.append(f"Server {server} failing frequently - consider fallback strategy")
|
||||
|
||||
# Check for performance issues
|
||||
avg_times = {}
|
||||
for activation in recent_activations:
|
||||
total_time = activation['total_time_ms']
|
||||
server_count = len(activation['activated'])
|
||||
if server_count > 0:
|
||||
avg_time_per_server = total_time / server_count
|
||||
avg_times[len(activation['activated'])] = avg_time_per_server
|
||||
|
||||
if avg_times and max(avg_times.values()) > 500:
|
||||
recommendations.append("Consider reducing concurrent server activations for better performance")
|
||||
|
||||
return {
|
||||
'recommendations': recommendations,
|
||||
'performance_metrics': self.performance_metrics,
|
||||
'server_states': {k: v.value for k, v in self.server_states.items()},
|
||||
'efficiency_score': self._calculate_overall_efficiency()
|
||||
}
|
||||
```
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### Hook Usage Pattern
|
||||
```python
|
||||
# Initialize MCP intelligence
|
||||
mcp_intelligence = MCPIntelligence()
|
||||
|
||||
# Create activation plan
|
||||
activation_plan = mcp_intelligence.create_activation_plan(
|
||||
user_input="I need to analyze this complex React application and optimize its performance",
|
||||
context={
|
||||
'resource_usage_percent': 65,
|
||||
'user_expertise': 'intermediate',
|
||||
'project_type': 'web'
|
||||
},
|
||||
operation_data={
|
||||
'file_count': 25,
|
||||
'complexity_score': 0.7,
|
||||
'operation_type': 'analyze',
|
||||
'has_external_dependencies': True
|
||||
}
|
||||
)
|
||||
|
||||
# Execute activation plan
|
||||
execution_result = mcp_intelligence.execute_activation_plan(activation_plan, context)
|
||||
|
||||
# Process results
|
||||
activated_servers = execution_result['activated_servers'] # ['serena', 'context7', 'sequential']
|
||||
coordination_strategy = execution_result['coordination_strategy'] # 'sequential_lead'
|
||||
total_time = execution_result['total_activation_time_ms'] # 450ms
|
||||
```
|
||||
|
||||
### Activation Plan Analysis
|
||||
```python
|
||||
print(f"Servers to activate: {activation_plan.servers_to_activate}")
|
||||
print(f"Activation order: {activation_plan.activation_order}")
|
||||
print(f"Estimated cost: {activation_plan.estimated_cost_ms}ms")
|
||||
print(f"Efficiency gains: {activation_plan.efficiency_gains}")
|
||||
print(f"Fallback strategy: {activation_plan.fallback_strategy}")
|
||||
print(f"Coordination: {activation_plan.coordination_strategy}")
|
||||
```
|
||||
|
||||
### Performance Optimization
|
||||
```python
|
||||
# Get optimization recommendations
|
||||
recommendations = mcp_intelligence.get_optimization_recommendations(context)
|
||||
|
||||
print(f"Recommendations: {recommendations['recommendations']}")
|
||||
print(f"Efficiency score: {recommendations['efficiency_score']}")
|
||||
print(f"Server states: {recommendations['server_states']}")
|
||||
```
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Activation Planning
|
||||
- **Pattern Detection Integration**: <25ms for pattern analysis
|
||||
- **Server Selection Optimization**: <10ms for decision matrix
|
||||
- **Activation Sequencing**: <5ms for ordering calculation
|
||||
- **Cost Estimation**: <3ms for performance prediction
|
||||
|
||||
### Execution Performance
|
||||
- **Single Server Activation**: 80-300ms depending on server type
|
||||
- **Multi-Server Coordination**: 200-800ms for parallel activation
|
||||
- **Fallback Handling**: <50ms additional overhead per failure
|
||||
- **Performance Tracking**: <5ms per server for metrics collection
|
||||
|
||||
### Memory Efficiency
|
||||
- **Server Capability Cache**: ~2-3KB for all server definitions
|
||||
- **Activation History**: ~500B per activation record
|
||||
- **Performance Metrics**: ~200B per server per activation
|
||||
- **State Tracking**: ~100B per server state
|
||||
|
||||
## Error Handling Strategies
|
||||
|
||||
### Server Failure Handling
|
||||
```python
|
||||
def _handle_server_fallback(self, failed_server: str, plan: MCPActivationPlan, fallback_activations: List[str]):
|
||||
"""Handle server activation failure with fallback strategy."""
|
||||
fallback = plan.fallback_strategy.get(failed_server)
|
||||
|
||||
if fallback and fallback != 'native_tools' and fallback not in plan.servers_to_activate:
|
||||
# Try to activate fallback server
|
||||
if self.server_states.get(fallback) == MCPServerState.AVAILABLE:
|
||||
fallback_activations.append(f"{failed_server}->{fallback}")
|
||||
```
|
||||
|
||||
### Graceful Degradation
|
||||
- **Server Unavailable**: Use fallback server or native tools
|
||||
- **Activation Timeout**: Mark as failed, attempt fallback
|
||||
- **Performance Issues**: Recommend optimization strategies
|
||||
- **Resource Constraints**: Auto-disable intensive servers
|
||||
|
||||
### Recovery Mechanisms
|
||||
- **Automatic Retry**: One retry attempt for transient failures
|
||||
- **State Reset**: Clear error states after successful operations
|
||||
- **History Cleanup**: Remove old activation history to prevent memory issues
|
||||
- **Performance Adjustment**: Adapt expectations based on actual performance
|
||||
|
||||
## Configuration Requirements
|
||||
|
||||
### MCP Server Configuration
|
||||
```yaml
|
||||
mcp_server_integration:
|
||||
servers:
|
||||
context7:
|
||||
enabled: true
|
||||
activation_cost_ms: 150
|
||||
performance_profile: "standard"
|
||||
primary_functions:
|
||||
- "library_docs"
|
||||
- "framework_patterns"
|
||||
- "best_practices"
|
||||
|
||||
sequential:
|
||||
enabled: true
|
||||
activation_cost_ms: 200
|
||||
performance_profile: "intensive"
|
||||
primary_functions:
|
||||
- "complex_analysis"
|
||||
- "multi_step_reasoning"
|
||||
- "debugging"
|
||||
```
|
||||
|
||||
### Orchestrator Configuration
|
||||
```yaml
|
||||
routing_patterns:
|
||||
complexity_thresholds:
|
||||
serena_threshold: 0.6
|
||||
morphllm_threshold: 0.6
|
||||
file_count_threshold: 10
|
||||
|
||||
resource_constraints:
|
||||
intensive_disable_threshold: 85
|
||||
performance_warning_threshold: 75
|
||||
|
||||
coordination_strategies:
|
||||
sequential_lead_complexity: 0.6
|
||||
serena_lead_files: 5
|
||||
parallel_threshold: 3
|
||||
```
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Activation Planning
|
||||
```python
|
||||
mcp_intelligence = MCPIntelligence()
|
||||
|
||||
plan = mcp_intelligence.create_activation_plan(
|
||||
user_input="Build a responsive React component with accessibility features",
|
||||
context={'resource_usage_percent': 40, 'user_expertise': 'expert'},
|
||||
operation_data={'file_count': 3, 'complexity_score': 0.4, 'operation_type': 'build'}
|
||||
)
|
||||
|
||||
print(f"Recommended servers: {plan.servers_to_activate}") # ['magic', 'morphllm']
|
||||
print(f"Activation order: {plan.activation_order}") # ['morphllm', 'magic']
|
||||
print(f"Coordination: {plan.coordination_strategy}") # 'collaborative'
|
||||
print(f"Estimated cost: {plan.estimated_cost_ms}ms") # 200ms
|
||||
```
|
||||
|
||||
### Complex Multi-Server Operation
|
||||
```python
|
||||
plan = mcp_intelligence.create_activation_plan(
|
||||
user_input="Analyze and refactor this large codebase with comprehensive testing",
|
||||
context={'resource_usage_percent': 30, 'is_production': True},
|
||||
operation_data={
|
||||
'file_count': 50,
|
||||
'complexity_score': 0.8,
|
||||
'operation_type': 'refactor',
|
||||
'has_tests': True,
|
||||
'has_external_dependencies': True
|
||||
}
|
||||
)
|
||||
|
||||
print(f"Servers: {plan.servers_to_activate}") # ['serena', 'context7', 'sequential', 'playwright']
|
||||
print(f"Order: {plan.activation_order}") # ['serena', 'context7', 'sequential', 'playwright']
|
||||
print(f"Strategy: {plan.coordination_strategy}") # 'serena_lead'
|
||||
print(f"Cost: {plan.estimated_cost_ms}ms") # 750ms
|
||||
```
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **pattern_detection**: PatternDetector for intelligent server selection
|
||||
- **yaml_loader**: Configuration loading for server capabilities
|
||||
- **Standard Libraries**: time, typing, dataclasses, enum
|
||||
|
||||
### Framework Integration
|
||||
- **ORCHESTRATOR.md**: Intelligent routing and coordination patterns
|
||||
- **Performance Targets**: Sub-200ms activation goals with optimization
|
||||
- **Quality Gates**: Server activation validation and monitoring
|
||||
|
||||
### Hook Coordination
|
||||
- Used by all hooks for consistent MCP server management
|
||||
- Provides standardized activation planning and execution
|
||||
- Enables cross-hook performance monitoring and optimization
|
||||
|
||||
---
|
||||
|
||||
*This module serves as the intelligent orchestration layer for MCP server management, ensuring optimal server selection, efficient activation sequences, and robust error handling for all SuperClaude hook operations.*
|
||||
@@ -1,557 +0,0 @@
|
||||
# pattern_detection.py - Intelligent Pattern Recognition Engine
|
||||
|
||||
## Overview
|
||||
|
||||
The `pattern_detection.py` module provides intelligent pattern detection for automatic mode activation, MCP server selection, and operational optimization. It analyzes user input, context, and operation patterns to make smart recommendations about which SuperClaude modes should be activated, which MCP servers are needed, and what optimization flags to apply.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **Mode Trigger Detection**: Automatic identification of when SuperClaude modes should be activated
|
||||
- **MCP Server Selection**: Context-aware recommendation of which MCP servers to enable
|
||||
- **Complexity Analysis**: Pattern-based complexity assessment and scoring
|
||||
- **Persona Recognition**: Detection of domain expertise hints in user requests
|
||||
- **Performance Optimization**: Pattern-based performance optimization recommendations
|
||||
|
||||
### Intelligence Capabilities
|
||||
- **Regex Pattern Matching**: Compiled patterns for efficient text analysis
|
||||
- **Context-Aware Analysis**: Integration of user input, session context, and operation data
|
||||
- **Confidence Scoring**: Probabilistic assessment of pattern matches
|
||||
- **Multi-Factor Decision Making**: Combination of multiple pattern types for comprehensive analysis
|
||||
|
||||
## Core Classes and Data Structures
|
||||
|
||||
### Enumerations
|
||||
|
||||
#### PatternType
|
||||
```python
|
||||
class PatternType(Enum):
|
||||
MODE_TRIGGER = "mode_trigger" # SuperClaude mode activation patterns
|
||||
MCP_SERVER = "mcp_server" # MCP server selection patterns
|
||||
OPERATION_TYPE = "operation_type" # Operation classification patterns
|
||||
COMPLEXITY_INDICATOR = "complexity_indicator" # Complexity assessment patterns
|
||||
PERSONA_HINT = "persona_hint" # Domain expertise detection patterns
|
||||
PERFORMANCE_HINT = "performance_hint" # Performance optimization patterns
|
||||
```
|
||||
|
||||
### Data Classes
|
||||
|
||||
#### PatternMatch
|
||||
```python
|
||||
@dataclass
|
||||
class PatternMatch:
|
||||
pattern_type: PatternType # Type of pattern detected
|
||||
pattern_name: str # Specific pattern identifier
|
||||
confidence: float # 0.0 to 1.0 confidence score
|
||||
matched_text: str # Text that triggered the match
|
||||
suggestions: List[str] # Actionable recommendations
|
||||
metadata: Dict[str, Any] # Additional pattern-specific data
|
||||
```
|
||||
|
||||
#### DetectionResult
|
||||
```python
|
||||
@dataclass
|
||||
class DetectionResult:
|
||||
matches: List[PatternMatch] # All detected pattern matches
|
||||
recommended_modes: List[str] # SuperClaude modes to activate
|
||||
recommended_mcp_servers: List[str] # MCP servers to enable
|
||||
suggested_flags: List[str] # Command-line flags to apply
|
||||
complexity_score: float # Overall complexity assessment
|
||||
confidence_score: float # Overall confidence in detection
|
||||
```
|
||||
|
||||
## Pattern Detection Engine
|
||||
|
||||
### Initialization and Configuration
|
||||
```python
|
||||
def __init__(self):
|
||||
self.patterns = config_loader.load_config('modes')
|
||||
self.mcp_patterns = config_loader.load_config('orchestrator')
|
||||
self._compile_patterns()
|
||||
```
|
||||
|
||||
**Pattern Compilation Process**:
|
||||
1. Load mode detection patterns from YAML configuration
|
||||
2. Load MCP routing patterns from orchestrator configuration
|
||||
3. Compile regex patterns for efficient matching
|
||||
4. Cache compiled patterns for performance
|
||||
|
||||
### Core Detection Method
|
||||
|
||||
#### detect_patterns()
|
||||
```python
|
||||
def detect_patterns(self,
|
||||
user_input: str,
|
||||
context: Dict[str, Any],
|
||||
operation_data: Dict[str, Any]) -> DetectionResult:
|
||||
```
|
||||
|
||||
**Detection Pipeline**:
|
||||
1. **Mode Pattern Detection**: Identify SuperClaude mode triggers
|
||||
2. **MCP Server Pattern Detection**: Determine required MCP servers
|
||||
3. **Complexity Pattern Detection**: Assess operation complexity indicators
|
||||
4. **Persona Pattern Detection**: Detect domain expertise hints
|
||||
5. **Score Calculation**: Compute overall complexity and confidence scores
|
||||
6. **Recommendation Generation**: Generate actionable recommendations
|
||||
|
||||
## Mode Detection Patterns
|
||||
|
||||
### Brainstorming Mode Detection
|
||||
**Trigger Indicators**:
|
||||
```python
|
||||
brainstorm_indicators = [
|
||||
r"(?:i want to|thinking about|not sure|maybe|could we)\s+(?:build|create|make)",
|
||||
r"(?:brainstorm|explore|figure out|discuss)",
|
||||
r"(?:new project|startup idea|feature concept)",
|
||||
r"(?:ambiguous|uncertain|unclear)\s+(?:requirements|needs)"
|
||||
]
|
||||
```
|
||||
|
||||
**Pattern Match Example**:
|
||||
```python
|
||||
PatternMatch(
|
||||
pattern_type=PatternType.MODE_TRIGGER,
|
||||
pattern_name="brainstorming",
|
||||
confidence=0.8,
|
||||
matched_text="thinking about building",
|
||||
suggestions=["Enable brainstorming mode for requirements discovery"],
|
||||
metadata={"mode": "brainstorming", "auto_activate": True}
|
||||
)
|
||||
```
|
||||
|
||||
### Task Management Mode Detection
|
||||
**Trigger Indicators**:
|
||||
```python
|
||||
task_management_indicators = [
|
||||
r"(?:multiple|many|several)\s+(?:tasks|files|components)",
|
||||
r"(?:build|implement|create)\s+(?:system|feature|application)",
|
||||
r"(?:complex|comprehensive|large-scale)",
|
||||
r"(?:manage|coordinate|orchestrate)\s+(?:work|tasks|operations)"
|
||||
]
|
||||
```
|
||||
|
||||
### Token Efficiency Mode Detection
|
||||
**Trigger Indicators**:
|
||||
```python
|
||||
efficiency_indicators = [
|
||||
r"(?:brief|concise|compressed|short)",
|
||||
r"(?:token|resource|memory)\s+(?:limit|constraint|optimization)",
|
||||
r"(?:efficient|optimized|minimal)\s+(?:output|response)"
|
||||
]
|
||||
```
|
||||
|
||||
**Automatic Resource-Based Activation**:
|
||||
```python
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
if resource_usage > 75:
|
||||
# Auto-enable token efficiency mode
|
||||
match = PatternMatch(
|
||||
pattern_type=PatternType.MODE_TRIGGER,
|
||||
pattern_name="token_efficiency",
|
||||
confidence=0.85,
|
||||
matched_text="high_resource_usage",
|
||||
suggestions=["Auto-enable token efficiency due to resource constraints"],
|
||||
metadata={"mode": "token_efficiency", "trigger": "resource_constraint"}
|
||||
)
|
||||
```
|
||||
|
||||
## MCP Server Detection Patterns
|
||||
|
||||
### Context7 (Library Documentation)
|
||||
**Trigger Patterns**:
|
||||
```python
|
||||
context7_patterns = [
|
||||
r"(?:library|framework|package)\s+(?:documentation|docs|patterns)",
|
||||
r"(?:react|vue|angular|express|django|flask)",
|
||||
r"(?:import|require|install|dependency)",
|
||||
r"(?:official|standard|best practice)\s+(?:way|pattern|approach)"
|
||||
]
|
||||
```
|
||||
|
||||
### Sequential (Complex Analysis)
|
||||
**Trigger Patterns**:
|
||||
```python
|
||||
sequential_patterns = [
|
||||
r"(?:analyze|debug|troubleshoot|investigate)",
|
||||
r"(?:complex|complicated|multi-step|systematic)",
|
||||
r"(?:architecture|system|design)\s+(?:review|analysis)",
|
||||
r"(?:root cause|performance|bottleneck)"
|
||||
]
|
||||
```
|
||||
|
||||
### Magic (UI Components)
|
||||
**Trigger Patterns**:
|
||||
```python
|
||||
magic_patterns = [
|
||||
r"(?:component|button|form|modal|dialog)",
|
||||
r"(?:ui|frontend|interface|design)",
|
||||
r"(?:react|vue|angular)\s+(?:component|element)",
|
||||
r"(?:responsive|mobile|accessibility)"
|
||||
]
|
||||
```
|
||||
|
||||
### Playwright (Testing)
|
||||
**Trigger Patterns**:
|
||||
```python
|
||||
playwright_patterns = [
|
||||
r"(?:test|testing|e2e|end-to-end)",
|
||||
r"(?:browser|cross-browser|automation)",
|
||||
r"(?:performance|visual|regression)\s+(?:test|testing)",
|
||||
r"(?:validate|verify|check)\s+(?:functionality|behavior)"
|
||||
]
|
||||
```
|
||||
|
||||
### Hybrid Intelligence Selection (Morphllm vs Serena)
|
||||
```python
|
||||
def _detect_mcp_patterns(self, user_input: str, context: Dict[str, Any], operation_data: Dict[str, Any]):
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
complexity = operation_data.get('complexity_score', 0.0)
|
||||
|
||||
if file_count > 10 or complexity > 0.6:
|
||||
# Recommend Serena for complex operations
|
||||
return PatternMatch(
|
||||
pattern_type=PatternType.MCP_SERVER,
|
||||
pattern_name="serena",
|
||||
confidence=0.9,
|
||||
matched_text="high_complexity_operation",
|
||||
suggestions=["Use Serena for complex multi-file operations"],
|
||||
metadata={"mcp_server": "serena", "reason": "complexity_threshold"}
|
||||
)
|
||||
elif file_count <= 10 and complexity <= 0.6:
|
||||
# Recommend Morphllm for efficient operations
|
||||
return PatternMatch(
|
||||
pattern_type=PatternType.MCP_SERVER,
|
||||
pattern_name="morphllm",
|
||||
confidence=0.8,
|
||||
matched_text="moderate_complexity_operation",
|
||||
suggestions=["Use Morphllm for efficient editing operations"],
|
||||
metadata={"mcp_server": "morphllm", "reason": "efficiency_optimized"}
|
||||
)
|
||||
```
|
||||
|
||||
## Complexity Detection Patterns
|
||||
|
||||
### High Complexity Indicators
|
||||
```python
|
||||
high_complexity_patterns = [
|
||||
r"(?:entire|whole|complete)\s+(?:codebase|system|application)",
|
||||
r"(?:refactor|migrate|restructure)\s+(?:all|everything|entire)",
|
||||
r"(?:architecture|system-wide|comprehensive)\s+(?:change|update|redesign)",
|
||||
r"(?:complex|complicated|sophisticated)\s+(?:logic|algorithm|system)"
|
||||
]
|
||||
```
|
||||
|
||||
**Pattern Processing**:
|
||||
```python
|
||||
for pattern in high_complexity_patterns:
|
||||
if re.search(pattern, user_input, re.IGNORECASE):
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.COMPLEXITY_INDICATOR,
|
||||
pattern_name="high_complexity",
|
||||
confidence=0.8,
|
||||
matched_text=re.search(pattern, user_input, re.IGNORECASE).group(),
|
||||
suggestions=["Consider delegation and thinking modes"],
|
||||
metadata={"complexity_level": "high", "score_boost": 0.3}
|
||||
))
|
||||
```
|
||||
|
||||
### File Count-Based Complexity
|
||||
```python
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
if file_count > 5:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.COMPLEXITY_INDICATOR,
|
||||
pattern_name="multi_file_operation",
|
||||
confidence=0.9,
|
||||
matched_text=f"{file_count}_files",
|
||||
suggestions=["Enable delegation for multi-file operations"],
|
||||
metadata={"file_count": file_count, "delegation_recommended": True}
|
||||
))
|
||||
```
|
||||
|
||||
## Persona Detection Patterns
|
||||
|
||||
### Domain-Specific Patterns
|
||||
```python
|
||||
persona_patterns = {
|
||||
"architect": [r"(?:architecture|design|structure|system)\s+(?:review|analysis|planning)"],
|
||||
"performance": [r"(?:performance|optimization|speed|efficiency|bottleneck)"],
|
||||
"security": [r"(?:security|vulnerability|audit|secure|safety)"],
|
||||
"frontend": [r"(?:ui|frontend|interface|component|design|responsive)"],
|
||||
"backend": [r"(?:api|server|database|backend|service)"],
|
||||
"devops": [r"(?:deploy|deployment|ci|cd|infrastructure|docker|kubernetes)"],
|
||||
"testing": [r"(?:test|testing|qa|quality|coverage|validation)"]
|
||||
}
|
||||
```
|
||||
|
||||
**Pattern Matching Process**:
|
||||
```python
|
||||
for persona, patterns in persona_patterns.items():
|
||||
for pattern in patterns:
|
||||
if re.search(pattern, user_input, re.IGNORECASE):
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.PERSONA_HINT,
|
||||
pattern_name=persona,
|
||||
confidence=0.7,
|
||||
matched_text=re.search(pattern, user_input, re.IGNORECASE).group(),
|
||||
suggestions=[f"Consider {persona} persona for specialized expertise"],
|
||||
metadata={"persona": persona, "domain_specific": True}
|
||||
))
|
||||
```
|
||||
|
||||
## Scoring Algorithms
|
||||
|
||||
### Complexity Score Calculation
|
||||
```python
|
||||
def _calculate_complexity_score(self, matches: List[PatternMatch], operation_data: Dict[str, Any]) -> float:
|
||||
base_score = operation_data.get('complexity_score', 0.0)
|
||||
|
||||
# Add complexity from pattern matches
|
||||
for match in matches:
|
||||
if match.pattern_type == PatternType.COMPLEXITY_INDICATOR:
|
||||
score_boost = match.metadata.get('score_boost', 0.1)
|
||||
base_score += score_boost
|
||||
|
||||
return min(base_score, 1.0)
|
||||
```
|
||||
|
||||
### Confidence Score Calculation
|
||||
```python
|
||||
def _calculate_confidence_score(self, matches: List[PatternMatch]) -> float:
|
||||
if not matches:
|
||||
return 0.0
|
||||
|
||||
total_confidence = sum(match.confidence for match in matches)
|
||||
return min(total_confidence / len(matches), 1.0)
|
||||
```
|
||||
|
||||
## Recommendation Generation
|
||||
|
||||
### Mode Recommendations
|
||||
```python
|
||||
def _get_recommended_modes(self, matches: List[PatternMatch], complexity_score: float) -> List[str]:
|
||||
modes = set()
|
||||
|
||||
# Add modes from pattern matches
|
||||
for match in matches:
|
||||
if match.pattern_type == PatternType.MODE_TRIGGER:
|
||||
modes.add(match.pattern_name)
|
||||
|
||||
# Auto-activate based on complexity
|
||||
if complexity_score > 0.6:
|
||||
modes.add("task_management")
|
||||
|
||||
return list(modes)
|
||||
```
|
||||
|
||||
### Flag Suggestions
|
||||
```python
|
||||
def _get_suggested_flags(self, matches: List[PatternMatch], complexity_score: float, context: Dict[str, Any]) -> List[str]:
|
||||
flags = []
|
||||
|
||||
# Thinking flags based on complexity
|
||||
if complexity_score >= 0.8:
|
||||
flags.append("--ultrathink")
|
||||
elif complexity_score >= 0.6:
|
||||
flags.append("--think-hard")
|
||||
elif complexity_score >= 0.3:
|
||||
flags.append("--think")
|
||||
|
||||
# Delegation flags
|
||||
for match in matches:
|
||||
if match.metadata.get("delegation_recommended"):
|
||||
flags.append("--delegate auto")
|
||||
break
|
||||
|
||||
# Efficiency flags
|
||||
for match in matches:
|
||||
if match.metadata.get("compression_needed") or context.get('resource_usage_percent', 0) > 75:
|
||||
flags.append("--uc")
|
||||
break
|
||||
|
||||
# Validation flags for high-risk operations
|
||||
if complexity_score > 0.7 or context.get('is_production', False):
|
||||
flags.append("--validate")
|
||||
|
||||
return flags
|
||||
```
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Pattern Compilation
|
||||
- **Initialization Time**: <50ms for full pattern compilation
|
||||
- **Memory Usage**: ~5-10KB for compiled pattern cache
|
||||
- **Pattern Count**: ~50-100 patterns across all categories
|
||||
|
||||
### Detection Performance
|
||||
- **Single Pattern Match**: <1ms average
|
||||
- **Full Detection Pipeline**: <25ms for complex analysis
|
||||
- **Regex Operations**: Optimized with compiled patterns
|
||||
- **Context Processing**: <5ms for typical context sizes
|
||||
|
||||
### Cache Efficiency
|
||||
- **Pattern Reuse**: 95%+ pattern reuse across requests
|
||||
- **Compilation Avoidance**: Patterns compiled once per session
|
||||
- **Memory Efficiency**: Patterns shared across all detection calls
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### Hook Usage Pattern
|
||||
```python
|
||||
# Initialize pattern detector
|
||||
pattern_detector = PatternDetector()
|
||||
|
||||
# Perform pattern detection
|
||||
detection_result = pattern_detector.detect_patterns(
|
||||
user_input="I want to build a complex web application with multiple components",
|
||||
context={
|
||||
'resource_usage_percent': 45,
|
||||
'conversation_length': 25,
|
||||
'user_expertise': 'intermediate'
|
||||
},
|
||||
operation_data={
|
||||
'file_count': 12,
|
||||
'complexity_score': 0.0, # Will be enhanced by detection
|
||||
'operation_type': 'build'
|
||||
}
|
||||
)
|
||||
|
||||
# Apply recommendations
|
||||
recommended_modes = detection_result.recommended_modes # ['brainstorming', 'task_management']
|
||||
recommended_servers = detection_result.recommended_mcp_servers # ['serena', 'magic']
|
||||
suggested_flags = detection_result.suggested_flags # ['--think-hard', '--delegate auto']
|
||||
complexity_score = detection_result.complexity_score # 0.7
|
||||
```
|
||||
|
||||
### Pattern Match Processing
|
||||
```python
|
||||
for match in detection_result.matches:
|
||||
if match.pattern_type == PatternType.MODE_TRIGGER:
|
||||
# Activate detected modes
|
||||
activate_mode(match.pattern_name)
|
||||
elif match.pattern_type == PatternType.MCP_SERVER:
|
||||
# Enable recommended MCP servers
|
||||
enable_mcp_server(match.pattern_name)
|
||||
elif match.pattern_type == PatternType.COMPLEXITY_INDICATOR:
|
||||
# Apply complexity-based optimizations
|
||||
apply_complexity_optimizations(match.metadata)
|
||||
```
|
||||
|
||||
## Configuration Requirements
|
||||
|
||||
### Mode Configuration (modes.yaml)
|
||||
```yaml
|
||||
mode_detection:
|
||||
brainstorming:
|
||||
trigger_patterns:
|
||||
- "(?:i want to|thinking about|not sure)\\s+(?:build|create)"
|
||||
- "(?:brainstorm|explore|figure out)"
|
||||
- "(?:new project|startup idea)"
|
||||
confidence_threshold: 0.7
|
||||
|
||||
task_management:
|
||||
trigger_patterns:
|
||||
- "(?:multiple|many)\\s+(?:files|components)"
|
||||
- "(?:complex|comprehensive)"
|
||||
- "(?:build|implement)\\s+(?:system|feature)"
|
||||
confidence_threshold: 0.6
|
||||
```
|
||||
|
||||
### MCP Routing Configuration (orchestrator.yaml)
|
||||
```yaml
|
||||
routing_patterns:
|
||||
context7:
|
||||
triggers:
|
||||
- "(?:library|framework)\\s+(?:docs|patterns)"
|
||||
- "(?:react|vue|angular)"
|
||||
- "(?:official|standard)\\s+(?:way|approach)"
|
||||
activation_threshold: 0.8
|
||||
|
||||
sequential:
|
||||
triggers:
|
||||
- "(?:analyze|debug|troubleshoot)"
|
||||
- "(?:complex|multi-step)"
|
||||
- "(?:architecture|system)\\s+(?:analysis|review)"
|
||||
activation_threshold: 0.75
|
||||
```
|
||||
|
||||
## Error Handling Strategies
|
||||
|
||||
### Pattern Compilation Errors
|
||||
```python
|
||||
def _compile_patterns(self):
|
||||
"""Compile regex patterns for efficient matching."""
|
||||
self.compiled_patterns = {}
|
||||
|
||||
try:
|
||||
# Compile patterns with error handling
|
||||
for mode_name, mode_config in self.patterns.get('mode_detection', {}).items():
|
||||
patterns = mode_config.get('trigger_patterns', [])
|
||||
self.compiled_patterns[f"mode_{mode_name}"] = [
|
||||
re.compile(pattern, re.IGNORECASE) for pattern in patterns
|
||||
]
|
||||
except re.error as e:
|
||||
# Log pattern compilation error and continue with empty patterns
|
||||
logger.log_error("pattern_detection", f"Pattern compilation error: {e}")
|
||||
self.compiled_patterns[f"mode_{mode_name}"] = []
|
||||
```
|
||||
|
||||
### Detection Failures
|
||||
- **Regex Errors**: Skip problematic patterns, continue with others
|
||||
- **Context Errors**: Use default values for missing context keys
|
||||
- **Scoring Errors**: Return safe default scores (0.5 complexity, 0.0 confidence)
|
||||
|
||||
### Graceful Degradation
|
||||
- **Configuration Missing**: Use hardcoded fallback patterns
|
||||
- **Pattern Compilation Failed**: Continue with available patterns
|
||||
- **Performance Issues**: Implement timeout mechanisms for complex patterns
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Pattern Detection
|
||||
```python
|
||||
detector = PatternDetector()
|
||||
|
||||
result = detector.detect_patterns(
|
||||
user_input="I need to analyze the performance bottlenecks in this complex React application",
|
||||
context={'resource_usage_percent': 60, 'user_expertise': 'expert'},
|
||||
operation_data={'file_count': 25, 'operation_type': 'analyze'}
|
||||
)
|
||||
|
||||
print(f"Detected modes: {result.recommended_modes}") # ['task_management']
|
||||
print(f"MCP servers: {result.recommended_mcp_servers}") # ['sequential', 'context7']
|
||||
print(f"Suggested flags: {result.suggested_flags}") # ['--think-hard', '--delegate auto']
|
||||
print(f"Complexity score: {result.complexity_score}") # 0.7
|
||||
```
|
||||
|
||||
### Pattern Match Analysis
|
||||
```python
|
||||
for match in result.matches:
|
||||
print(f"Pattern: {match.pattern_name}")
|
||||
print(f"Type: {match.pattern_type.value}")
|
||||
print(f"Confidence: {match.confidence}")
|
||||
print(f"Matched text: {match.matched_text}")
|
||||
print(f"Suggestions: {match.suggestions}")
|
||||
print(f"Metadata: {match.metadata}")
|
||||
print("---")
|
||||
```
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **yaml_loader**: Configuration loading for pattern definitions
|
||||
- **Standard Libraries**: re, json, typing, dataclasses, enum
|
||||
|
||||
### Framework Integration
|
||||
- **MODE Detection**: Triggers for SuperClaude behavioral modes
|
||||
- **MCP Coordination**: Server selection for intelligent tool routing
|
||||
- **Performance Optimization**: Flag suggestions for efficiency improvements
|
||||
|
||||
### Hook Coordination
|
||||
- Used by all hooks for consistent pattern-based decision making
|
||||
- Provides standardized detection interface and result formats
|
||||
- Enables cross-hook pattern learning and optimization
|
||||
|
||||
---
|
||||
|
||||
*This module serves as the intelligent pattern recognition system that transforms user input and context into actionable recommendations, enabling SuperClaude to automatically adapt its behavior based on detected patterns and requirements.*
|
||||
@@ -1,621 +0,0 @@
|
||||
# validate_system.py - YAML-Driven System Validation Engine
|
||||
|
||||
## Overview
|
||||
|
||||
The `validate_system.py` module provides a comprehensive YAML-driven system validation engine for the SuperClaude Framework-Hooks. This module implements intelligent health scoring, proactive diagnostics, and predictive analysis by consuming declarative YAML patterns from validation_intelligence.yaml, enabling comprehensive system health monitoring without hardcoded validation logic.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **YAML-Driven Validation Patterns**: Hot-reloadable validation patterns for comprehensive system analysis
|
||||
- **Health Scoring**: Weighted component-based health scoring with configurable thresholds
|
||||
- **Proactive Diagnostic Pattern Matching**: Early warning system based on pattern recognition
|
||||
- **Predictive Health Analysis**: Trend analysis and predictive health assessments
|
||||
- **Automated Remediation Suggestions**: Intelligence-driven remediation recommendations
|
||||
- **Continuous Validation Cycles**: Ongoing system health monitoring and alerting
|
||||
|
||||
### Intelligence Capabilities
|
||||
- **Pattern-Based Health Assessment**: Configurable health scoring based on YAML intelligence patterns
|
||||
- **Component-Weighted Scoring**: Intelligent weighting of system components for overall health
|
||||
- **Proactive Issue Detection**: Early warning patterns that predict potential system issues
|
||||
- **Automated Fix Application**: Safe auto-remediation for known fixable issues
|
||||
|
||||
## Core Classes and Data Structures
|
||||
|
||||
### Enumerations
|
||||
|
||||
#### ValidationSeverity
|
||||
```python
|
||||
class ValidationSeverity(Enum):
|
||||
INFO = "info" # Informational notices
|
||||
LOW = "low" # Minor issues, no immediate action required
|
||||
MEDIUM = "medium" # Moderate issues, should be addressed
|
||||
HIGH = "high" # Significant issues, requires attention
|
||||
CRITICAL = "critical" # System-threatening issues, immediate action required
|
||||
```
|
||||
|
||||
#### HealthStatus
|
||||
```python
|
||||
class HealthStatus(Enum):
|
||||
HEALTHY = "healthy" # System operating normally
|
||||
WARNING = "warning" # Some issues detected, monitoring needed
|
||||
CRITICAL = "critical" # Serious issues, immediate intervention required
|
||||
UNKNOWN = "unknown" # Health status cannot be determined
|
||||
```
|
||||
|
||||
### Data Classes
|
||||
|
||||
#### ValidationIssue
|
||||
```python
|
||||
@dataclass
|
||||
class ValidationIssue:
|
||||
component: str # System component with the issue
|
||||
issue_type: str # Type of issue identified
|
||||
severity: ValidationSeverity # Severity level of the issue
|
||||
description: str # Human-readable description
|
||||
evidence: List[str] # Supporting evidence for the issue
|
||||
recommendations: List[str] # Suggested remediation actions
|
||||
remediation_action: Optional[str] # Automated fix action if available
|
||||
auto_fixable: bool # Whether the issue can be auto-fixed
|
||||
timestamp: float # When the issue was detected
|
||||
```
|
||||
|
||||
#### HealthScore
|
||||
```python
|
||||
@dataclass
|
||||
class HealthScore:
|
||||
component: str # Component name
|
||||
score: float # Health score 0.0 to 1.0
|
||||
status: HealthStatus # Overall health status
|
||||
contributing_factors: List[str] # Factors that influenced the score
|
||||
trend: str # improving|stable|degrading
|
||||
last_updated: float # Timestamp of last update
|
||||
```
|
||||
|
||||
#### DiagnosticResult
|
||||
```python
|
||||
@dataclass
|
||||
class DiagnosticResult:
|
||||
component: str # Component being diagnosed
|
||||
diagnosis: str # Diagnostic conclusion
|
||||
confidence: float # Confidence in diagnosis (0.0 to 1.0)
|
||||
symptoms: List[str] # Observed symptoms
|
||||
root_cause: Optional[str] # Identified root cause
|
||||
recommendations: List[str] # Recommended actions
|
||||
predicted_impact: str # Expected impact if not addressed
|
||||
timeline: str # Timeline for resolution
|
||||
```
|
||||
|
||||
## Core Validation Engine
|
||||
|
||||
### YAMLValidationEngine
|
||||
```python
|
||||
class YAMLValidationEngine:
|
||||
"""
|
||||
YAML-driven validation engine that consumes intelligence patterns.
|
||||
|
||||
Features:
|
||||
- Hot-reloadable YAML validation patterns
|
||||
- Component-based health scoring
|
||||
- Proactive diagnostic pattern matching
|
||||
- Predictive health analysis
|
||||
- Intelligent remediation suggestions
|
||||
"""
|
||||
|
||||
def __init__(self, framework_root: Path, fix_issues: bool = False):
|
||||
self.framework_root = Path(framework_root)
|
||||
self.fix_issues = fix_issues
|
||||
self.cache_dir = self.framework_root / "cache"
|
||||
self.config_dir = self.framework_root / "config"
|
||||
|
||||
# Initialize intelligence engine for YAML patterns
|
||||
self.intelligence_engine = IntelligenceEngine()
|
||||
|
||||
# Validation state
|
||||
self.issues: List[ValidationIssue] = []
|
||||
self.fixes_applied: List[str] = []
|
||||
self.health_scores: Dict[str, HealthScore] = {}
|
||||
self.diagnostic_results: List[DiagnosticResult] = []
|
||||
|
||||
# Load validation intelligence patterns
|
||||
self.validation_patterns = self._load_validation_patterns()
|
||||
```
|
||||
|
||||
## System Context Gathering
|
||||
|
||||
### _gather_system_context()
|
||||
```python
|
||||
def _gather_system_context(self) -> Dict[str, Any]:
|
||||
"""Gather current system context for validation analysis."""
|
||||
context = {
|
||||
'timestamp': time.time(),
|
||||
'framework_root': str(self.framework_root),
|
||||
'cache_directory_exists': self.cache_dir.exists(),
|
||||
'config_directory_exists': self.config_dir.exists(),
|
||||
}
|
||||
|
||||
# Learning system context
|
||||
learning_records_path = self.cache_dir / "learning_records.json"
|
||||
if learning_records_path.exists():
|
||||
try:
|
||||
with open(learning_records_path, 'r') as f:
|
||||
records = json.load(f)
|
||||
context['learning_records_count'] = len(records)
|
||||
if records:
|
||||
context['recent_learning_activity'] = len([
|
||||
r for r in records
|
||||
if r.get('timestamp', 0) > time.time() - 86400 # Last 24h
|
||||
])
|
||||
except:
|
||||
context['learning_records_count'] = 0
|
||||
context['recent_learning_activity'] = 0
|
||||
|
||||
# Adaptations context
|
||||
adaptations_path = self.cache_dir / "adaptations.json"
|
||||
if adaptations_path.exists():
|
||||
try:
|
||||
with open(adaptations_path, 'r') as f:
|
||||
adaptations = json.load(f)
|
||||
context['adaptations_count'] = len(adaptations)
|
||||
|
||||
# Calculate effectiveness statistics
|
||||
all_effectiveness = []
|
||||
for adaptation in adaptations.values():
|
||||
history = adaptation.get('effectiveness_history', [])
|
||||
all_effectiveness.extend(history)
|
||||
|
||||
if all_effectiveness:
|
||||
context['average_effectiveness'] = statistics.mean(all_effectiveness)
|
||||
context['effectiveness_variance'] = statistics.variance(all_effectiveness) if len(all_effectiveness) > 1 else 0
|
||||
context['perfect_score_count'] = sum(1 for score in all_effectiveness if score == 1.0)
|
||||
except:
|
||||
context['adaptations_count'] = 0
|
||||
|
||||
# Configuration files context
|
||||
yaml_files = list(self.config_dir.glob("*.yaml")) if self.config_dir.exists() else []
|
||||
context['yaml_config_count'] = len(yaml_files)
|
||||
context['intelligence_patterns_available'] = len([
|
||||
f for f in yaml_files
|
||||
if f.name in ['intelligence_patterns.yaml', 'mcp_orchestration.yaml',
|
||||
'hook_coordination.yaml', 'performance_intelligence.yaml',
|
||||
'validation_intelligence.yaml', 'user_experience.yaml']
|
||||
])
|
||||
|
||||
return context
|
||||
```
|
||||
|
||||
## Component Validation Methods
|
||||
|
||||
### Learning System Validation
|
||||
```python
|
||||
def _validate_learning_system(self, context: Dict[str, Any], intelligence: Dict[str, Any]):
|
||||
"""Validate learning system using YAML patterns."""
|
||||
print("📊 Validating learning system...")
|
||||
|
||||
component_weight = self.validation_patterns.get('component_weights', {}).get('learning_system', 0.25)
|
||||
scoring_metrics = self.validation_patterns.get('scoring_metrics', {}).get('learning_system', {})
|
||||
|
||||
issues = []
|
||||
score_factors = []
|
||||
|
||||
# Pattern diversity validation
|
||||
adaptations_count = context.get('adaptations_count', 0)
|
||||
if adaptations_count > 0:
|
||||
# Simplified diversity calculation
|
||||
diversity_score = min(adaptations_count / 50.0, 0.95) # Cap at 0.95
|
||||
pattern_diversity_config = scoring_metrics.get('pattern_diversity', {})
|
||||
healthy_range = pattern_diversity_config.get('healthy_range', [0.6, 0.95])
|
||||
|
||||
if diversity_score < healthy_range[0]:
|
||||
issues.append(ValidationIssue(
|
||||
component="learning_system",
|
||||
issue_type="pattern_diversity",
|
||||
severity=ValidationSeverity.MEDIUM,
|
||||
description=f"Pattern diversity low: {diversity_score:.2f}",
|
||||
evidence=[f"Only {adaptations_count} unique patterns learned"],
|
||||
recommendations=["Expose system to more diverse operational patterns"]
|
||||
))
|
||||
score_factors.append(diversity_score)
|
||||
|
||||
# Effectiveness consistency validation
|
||||
effectiveness_variance = context.get('effectiveness_variance', 0)
|
||||
if effectiveness_variance is not None:
|
||||
consistency_score = max(0, 1.0 - effectiveness_variance)
|
||||
effectiveness_config = scoring_metrics.get('effectiveness_consistency', {})
|
||||
healthy_range = effectiveness_config.get('healthy_range', [0.7, 0.9])
|
||||
|
||||
if consistency_score < healthy_range[0]:
|
||||
issues.append(ValidationIssue(
|
||||
component="learning_system",
|
||||
issue_type="effectiveness_consistency",
|
||||
severity=ValidationSeverity.LOW,
|
||||
description=f"Effectiveness variance high: {effectiveness_variance:.3f}",
|
||||
evidence=[f"Effectiveness consistency score: {consistency_score:.2f}"],
|
||||
recommendations=["Review learning patterns for instability"]
|
||||
))
|
||||
score_factors.append(consistency_score)
|
||||
|
||||
# Calculate health score
|
||||
component_health = statistics.mean(score_factors) if score_factors else 0.5
|
||||
health_status = (
|
||||
HealthStatus.HEALTHY if component_health >= 0.8 else
|
||||
HealthStatus.WARNING if component_health >= 0.6 else
|
||||
HealthStatus.CRITICAL
|
||||
)
|
||||
|
||||
self.health_scores['learning_system'] = HealthScore(
|
||||
component='learning_system',
|
||||
score=component_health,
|
||||
status=health_status,
|
||||
contributing_factors=[f"pattern_diversity", "effectiveness_consistency"],
|
||||
trend="stable" # Would need historical data to determine trend
|
||||
)
|
||||
|
||||
self.issues.extend(issues)
|
||||
```
|
||||
|
||||
### Configuration System Validation
|
||||
```python
|
||||
def _validate_configuration_system(self, context: Dict[str, Any], intelligence: Dict[str, Any]):
|
||||
"""Validate configuration system using YAML patterns."""
|
||||
print("📝 Validating configuration system...")
|
||||
|
||||
issues = []
|
||||
score_factors = []
|
||||
|
||||
# Check YAML configuration files
|
||||
expected_intelligence_files = [
|
||||
'intelligence_patterns.yaml',
|
||||
'mcp_orchestration.yaml',
|
||||
'hook_coordination.yaml',
|
||||
'performance_intelligence.yaml',
|
||||
'validation_intelligence.yaml',
|
||||
'user_experience.yaml'
|
||||
]
|
||||
|
||||
available_files = [f.name for f in self.config_dir.glob("*.yaml")] if self.config_dir.exists() else []
|
||||
missing_files = [f for f in expected_intelligence_files if f not in available_files]
|
||||
|
||||
if missing_files:
|
||||
issues.append(ValidationIssue(
|
||||
component="configuration_system",
|
||||
issue_type="missing_intelligence_configs",
|
||||
severity=ValidationSeverity.HIGH,
|
||||
description=f"Missing {len(missing_files)} intelligence configuration files",
|
||||
evidence=[f"Missing files: {', '.join(missing_files)}"],
|
||||
recommendations=["Ensure all intelligence pattern files are available"]
|
||||
))
|
||||
score_factors.append(0.5)
|
||||
else:
|
||||
score_factors.append(0.9)
|
||||
|
||||
# Validate YAML syntax
|
||||
yaml_issues = 0
|
||||
if self.config_dir.exists():
|
||||
for yaml_file in self.config_dir.glob("*.yaml"):
|
||||
try:
|
||||
with open(yaml_file, 'r') as f:
|
||||
config_loader.load_config(yaml_file.stem)
|
||||
except Exception as e:
|
||||
yaml_issues += 1
|
||||
issues.append(ValidationIssue(
|
||||
component="configuration_system",
|
||||
issue_type="yaml_syntax_error",
|
||||
severity=ValidationSeverity.HIGH,
|
||||
description=f"YAML syntax error in {yaml_file.name}",
|
||||
evidence=[f"Error: {str(e)}"],
|
||||
recommendations=[f"Fix YAML syntax in {yaml_file.name}"]
|
||||
))
|
||||
|
||||
syntax_score = max(0, 1.0 - yaml_issues * 0.2)
|
||||
score_factors.append(syntax_score)
|
||||
|
||||
overall_score = statistics.mean(score_factors) if score_factors else 0.5
|
||||
|
||||
self.health_scores['configuration_system'] = HealthScore(
|
||||
component='configuration_system',
|
||||
score=overall_score,
|
||||
status=HealthStatus.HEALTHY if overall_score >= 0.8 else
|
||||
HealthStatus.WARNING if overall_score >= 0.6 else
|
||||
HealthStatus.CRITICAL,
|
||||
contributing_factors=["file_availability", "yaml_syntax", "intelligence_patterns"],
|
||||
trend="stable"
|
||||
)
|
||||
|
||||
self.issues.extend(issues)
|
||||
```
|
||||
|
||||
## Proactive Diagnostics
|
||||
|
||||
### _run_proactive_diagnostics()
|
||||
```python
|
||||
def _run_proactive_diagnostics(self, context: Dict[str, Any]):
|
||||
"""Run proactive diagnostic pattern matching from YAML."""
|
||||
print("🔮 Running proactive diagnostics...")
|
||||
|
||||
# Get early warning patterns from YAML
|
||||
early_warning_patterns = self.validation_patterns.get(
|
||||
'proactive_diagnostics', {}
|
||||
).get('early_warning_patterns', {})
|
||||
|
||||
# Check learning system warnings
|
||||
learning_warnings = early_warning_patterns.get('learning_system_warnings', [])
|
||||
for warning_pattern in learning_warnings:
|
||||
if self._matches_warning_pattern(context, warning_pattern):
|
||||
severity_map = {
|
||||
'low': ValidationSeverity.LOW,
|
||||
'medium': ValidationSeverity.MEDIUM,
|
||||
'high': ValidationSeverity.HIGH,
|
||||
'critical': ValidationSeverity.CRITICAL
|
||||
}
|
||||
|
||||
self.issues.append(ValidationIssue(
|
||||
component="learning_system",
|
||||
issue_type=warning_pattern.get('name', 'unknown_warning'),
|
||||
severity=severity_map.get(warning_pattern.get('severity', 'medium'), ValidationSeverity.MEDIUM),
|
||||
description=f"Proactive warning: {warning_pattern.get('name')}",
|
||||
evidence=[f"Pattern matched: {warning_pattern.get('pattern', {})}"],
|
||||
recommendations=[warning_pattern.get('recommendation', 'Review system state')],
|
||||
remediation_action=warning_pattern.get('remediation')
|
||||
))
|
||||
```
|
||||
|
||||
## Health Score Calculation
|
||||
|
||||
### _calculate_overall_health_score()
|
||||
```python
|
||||
def _calculate_overall_health_score(self):
|
||||
"""Calculate overall system health score using YAML component weights."""
|
||||
component_weights = self.validation_patterns.get('component_weights', {
|
||||
'learning_system': 0.25,
|
||||
'performance_system': 0.20,
|
||||
'mcp_coordination': 0.20,
|
||||
'hook_system': 0.15,
|
||||
'configuration_system': 0.10,
|
||||
'cache_system': 0.10
|
||||
})
|
||||
|
||||
weighted_score = 0.0
|
||||
total_weight = 0.0
|
||||
|
||||
for component, weight in component_weights.items():
|
||||
if component in self.health_scores:
|
||||
weighted_score += self.health_scores[component].score * weight
|
||||
total_weight += weight
|
||||
|
||||
overall_score = weighted_score / total_weight if total_weight > 0 else 0.0
|
||||
|
||||
overall_status = (
|
||||
HealthStatus.HEALTHY if overall_score >= 0.8 else
|
||||
HealthStatus.WARNING if overall_score >= 0.6 else
|
||||
HealthStatus.CRITICAL
|
||||
)
|
||||
|
||||
self.health_scores['overall'] = HealthScore(
|
||||
component='overall_system',
|
||||
score=overall_score,
|
||||
status=overall_status,
|
||||
contributing_factors=list(component_weights.keys()),
|
||||
trend="stable"
|
||||
)
|
||||
```
|
||||
|
||||
## Automated Remediation
|
||||
|
||||
### _generate_remediation_suggestions()
|
||||
```python
|
||||
def _generate_remediation_suggestions(self):
|
||||
"""Generate intelligent remediation suggestions based on issues found."""
|
||||
auto_fixable_issues = [issue for issue in self.issues if issue.auto_fixable]
|
||||
|
||||
if auto_fixable_issues and self.fix_issues:
|
||||
for issue in auto_fixable_issues:
|
||||
if issue.remediation_action == "create_cache_directory":
|
||||
try:
|
||||
self.cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.fixes_applied.append(f"✅ Created cache directory: {self.cache_dir}")
|
||||
except Exception as e:
|
||||
print(f"Failed to create cache directory: {e}")
|
||||
```
|
||||
|
||||
## Main Validation Interface
|
||||
|
||||
### validate_all()
|
||||
```python
|
||||
def validate_all(self) -> Tuple[List[ValidationIssue], List[str], Dict[str, HealthScore]]:
|
||||
"""
|
||||
Run comprehensive YAML-driven validation.
|
||||
|
||||
Returns:
|
||||
Tuple of (issues, fixes_applied, health_scores)
|
||||
"""
|
||||
print("🔍 Starting YAML-driven framework validation...")
|
||||
|
||||
# Clear previous state
|
||||
self.issues.clear()
|
||||
self.fixes_applied.clear()
|
||||
self.health_scores.clear()
|
||||
self.diagnostic_results.clear()
|
||||
|
||||
# Get current system context
|
||||
context = self._gather_system_context()
|
||||
|
||||
# Run validation intelligence analysis
|
||||
validation_intelligence = self.intelligence_engine.evaluate_context(
|
||||
context, 'validation_intelligence'
|
||||
)
|
||||
|
||||
# Core component validations using YAML patterns
|
||||
self._validate_learning_system(context, validation_intelligence)
|
||||
self._validate_performance_system(context, validation_intelligence)
|
||||
self._validate_mcp_coordination(context, validation_intelligence)
|
||||
self._validate_hook_system(context, validation_intelligence)
|
||||
self._validate_configuration_system(context, validation_intelligence)
|
||||
self._validate_cache_system(context, validation_intelligence)
|
||||
|
||||
# Run proactive diagnostics
|
||||
self._run_proactive_diagnostics(context)
|
||||
|
||||
# Calculate overall health score
|
||||
self._calculate_overall_health_score()
|
||||
|
||||
# Generate remediation recommendations
|
||||
self._generate_remediation_suggestions()
|
||||
|
||||
return self.issues, self.fixes_applied, self.health_scores
|
||||
```
|
||||
|
||||
## Results Reporting
|
||||
|
||||
### print_results()
|
||||
```python
|
||||
def print_results(self, verbose: bool = False):
|
||||
"""Print comprehensive validation results."""
|
||||
print("\n" + "="*70)
|
||||
print("🎯 YAML-DRIVEN VALIDATION RESULTS")
|
||||
print("="*70)
|
||||
|
||||
# Overall health score
|
||||
overall_health = self.health_scores.get('overall')
|
||||
if overall_health:
|
||||
status_emoji = {
|
||||
HealthStatus.HEALTHY: "🟢",
|
||||
HealthStatus.WARNING: "🟡",
|
||||
HealthStatus.CRITICAL: "🔴",
|
||||
HealthStatus.UNKNOWN: "⚪"
|
||||
}
|
||||
print(f"\n{status_emoji.get(overall_health.status, '⚪')} Overall Health Score: {overall_health.score:.2f}/1.0 ({overall_health.status.value})")
|
||||
|
||||
# Component health scores
|
||||
if verbose and len(self.health_scores) > 1:
|
||||
print(f"\n📊 Component Health Scores:")
|
||||
for component, health in self.health_scores.items():
|
||||
if component != 'overall':
|
||||
status_emoji = {
|
||||
HealthStatus.HEALTHY: "🟢",
|
||||
HealthStatus.WARNING: "🟡",
|
||||
HealthStatus.CRITICAL: "🔴"
|
||||
}
|
||||
print(f" {status_emoji.get(health.status, '⚪')} {component}: {health.score:.2f}")
|
||||
|
||||
# Issues found
|
||||
if not self.issues:
|
||||
print("\n✅ All validations passed! System appears healthy.")
|
||||
else:
|
||||
severity_counts = {}
|
||||
for issue in self.issues:
|
||||
severity_counts[issue.severity] = severity_counts.get(issue.severity, 0) + 1
|
||||
|
||||
print(f"\n🔍 Found {len(self.issues)} issues:")
|
||||
for severity in [ValidationSeverity.CRITICAL, ValidationSeverity.HIGH,
|
||||
ValidationSeverity.MEDIUM, ValidationSeverity.LOW, ValidationSeverity.INFO]:
|
||||
if severity in severity_counts:
|
||||
severity_emoji = {
|
||||
ValidationSeverity.CRITICAL: "🚨",
|
||||
ValidationSeverity.HIGH: "⚠️ ",
|
||||
ValidationSeverity.MEDIUM: "🟡",
|
||||
ValidationSeverity.LOW: "ℹ️ ",
|
||||
ValidationSeverity.INFO: "💡"
|
||||
}
|
||||
print(f" {severity_emoji.get(severity, '')} {severity.value.title()}: {severity_counts[severity]}")
|
||||
```
|
||||
|
||||
## CLI Interface
|
||||
|
||||
### main()
|
||||
```python
|
||||
def main():
|
||||
"""Main entry point for YAML-driven validation."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="YAML-driven Framework-Hooks validation engine"
|
||||
)
|
||||
parser.add_argument("--fix", action="store_true",
|
||||
help="Attempt to fix auto-fixable issues")
|
||||
parser.add_argument("--verbose", action="store_true",
|
||||
help="Verbose output with detailed results")
|
||||
parser.add_argument("--framework-root",
|
||||
default=".",
|
||||
help="Path to Framework-Hooks directory")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
framework_root = Path(args.framework_root).resolve()
|
||||
if not framework_root.exists():
|
||||
print(f"❌ Framework root directory not found: {framework_root}")
|
||||
sys.exit(1)
|
||||
|
||||
# Initialize YAML-driven validation engine
|
||||
validator = YAMLValidationEngine(framework_root, args.fix)
|
||||
|
||||
# Run comprehensive validation
|
||||
issues, fixes, health_scores = validator.validate_all()
|
||||
|
||||
# Print results
|
||||
validator.print_results(args.verbose)
|
||||
|
||||
# Exit with health score as return code (0 = perfect, higher = issues)
|
||||
overall_health = health_scores.get('overall')
|
||||
health_score = overall_health.score if overall_health else 0.0
|
||||
exit_code = max(0, min(10, int((1.0 - health_score) * 10))) # 0-10 range
|
||||
|
||||
sys.exit(exit_code)
|
||||
```
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Operation Timings
|
||||
- **System Context Gathering**: <50ms for comprehensive context analysis
|
||||
- **Component Validation**: <100ms per component with full pattern matching
|
||||
- **Proactive Diagnostics**: <25ms for early warning pattern evaluation
|
||||
- **Health Score Calculation**: <10ms for weighted component scoring
|
||||
- **Remediation Generation**: <15ms for intelligent suggestion generation
|
||||
|
||||
### Memory Efficiency
|
||||
- **Validation State**: ~5-15KB for complete validation run
|
||||
- **Health Scores**: ~200-500B per component score
|
||||
- **Issue Storage**: ~500B-2KB per validation issue
|
||||
- **Intelligence Cache**: Shared with IntelligenceEngine (~50KB)
|
||||
|
||||
### Quality Metrics
|
||||
- **Health Score Accuracy**: 95%+ correlation with actual system health
|
||||
- **Issue Detection Rate**: 90%+ detection of actual system problems
|
||||
- **False Positive Rate**: <5% for critical and high severity issues
|
||||
- **Auto-Fix Success Rate**: 98%+ for auto-fixable issues
|
||||
|
||||
## Error Handling Strategies
|
||||
|
||||
### Validation Failures
|
||||
- **Component Validation Errors**: Skip problematic components, log warnings, continue with others
|
||||
- **Pattern Matching Failures**: Use fallback scoring, proceed with available data
|
||||
- **Context Gathering Errors**: Use partial context, note missing information
|
||||
|
||||
### YAML Pattern Errors
|
||||
- **Malformed Intelligence Patterns**: Skip invalid patterns, use defaults where possible
|
||||
- **Missing Configuration**: Provide default component weights and thresholds
|
||||
- **Permission Issues**: Log errors, continue with available patterns
|
||||
|
||||
### Auto-Fix Failures
|
||||
- **Remediation Errors**: Log failures, provide manual remediation instructions
|
||||
- **Permission Denied**: Skip auto-fixes, recommend manual intervention
|
||||
- **Partial Fixes**: Apply successful fixes, report failures for manual resolution
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **intelligence_engine**: YAML pattern interpretation and hot-reload capability
|
||||
- **yaml_loader**: Configuration loading for validation intelligence patterns
|
||||
- **Standard Libraries**: os, json, time, statistics, sys, argparse, pathlib
|
||||
|
||||
### Framework Integration
|
||||
- **validation_intelligence.yaml**: Consumes validation patterns and health scoring rules
|
||||
- **System Health Monitoring**: Continuous validation with configurable thresholds
|
||||
- **Proactive Diagnostics**: Early warning system for predictive issue detection
|
||||
|
||||
### Hook Coordination
|
||||
- Provides system health validation for all hook operations
|
||||
- Enables proactive health monitoring with intelligent diagnostics
|
||||
- Supports automated remediation for common system issues
|
||||
|
||||
---
|
||||
|
||||
*This module provides comprehensive, intelligence-driven system validation that adapts to changing requirements through YAML configuration, enabling proactive health monitoring and automated remediation for the SuperClaude Framework-Hooks system.*
|
||||
@@ -1,622 +0,0 @@
|
||||
# yaml_loader.py - Unified Configuration Management System
|
||||
|
||||
## Overview
|
||||
|
||||
The `yaml_loader.py` module provides unified configuration loading with support for both JSON and YAML formats, featuring intelligent caching, hot-reload capabilities, and comprehensive error handling. It serves as the central configuration management system for all SuperClaude hooks, supporting Claude Code settings.json, SuperClaude superclaude-config.json, and YAML configuration files.
|
||||
|
||||
## Purpose and Responsibilities
|
||||
|
||||
### Primary Functions
|
||||
- **Dual-Format Support**: JSON (Claude Code + SuperClaude) and YAML configuration handling
|
||||
- **Intelligent Caching**: Sub-10ms configuration access with file modification detection
|
||||
- **Hot-Reload Capability**: Automatic detection and reload of configuration changes
|
||||
- **Environment Interpolation**: ${VAR} and ${VAR:default} syntax support for dynamic configuration
|
||||
- **Modular Configuration**: Include/merge support for complex deployment scenarios
|
||||
|
||||
### Performance Characteristics
|
||||
- **Sub-10ms Access**: Cached configuration retrieval for optimal hook performance
|
||||
- **<50ms Reload**: Configuration file reload when changes detected
|
||||
- **1-Second Check Interval**: Rate-limited file modification checks for efficiency
|
||||
- **Comprehensive Error Handling**: Graceful degradation with fallback configurations
|
||||
|
||||
## Core Architecture
|
||||
|
||||
### UnifiedConfigLoader Class
|
||||
```python
|
||||
class UnifiedConfigLoader:
|
||||
"""
|
||||
Intelligent configuration loader with support for JSON and YAML formats.
|
||||
|
||||
Features:
|
||||
- Dual-configuration support (Claude Code + SuperClaude)
|
||||
- File modification detection for hot-reload
|
||||
- In-memory caching for performance (<10ms access)
|
||||
- Comprehensive error handling and validation
|
||||
- Environment variable interpolation
|
||||
- Include/merge support for modular configs
|
||||
- Unified configuration interface
|
||||
"""
|
||||
```
|
||||
|
||||
### Configuration Source Registry
|
||||
```python
|
||||
def __init__(self, project_root: Union[str, Path]):
|
||||
self.project_root = Path(project_root)
|
||||
self.config_dir = self.project_root / "config"
|
||||
|
||||
# Configuration file paths
|
||||
self.claude_settings_path = self.project_root / "settings.json"
|
||||
self.superclaude_config_path = self.project_root / "superclaude-config.json"
|
||||
|
||||
# Configuration source registry
|
||||
self._config_sources = {
|
||||
'claude_settings': self.claude_settings_path,
|
||||
'superclaude_config': self.superclaude_config_path
|
||||
}
|
||||
```
|
||||
|
||||
**Supported Configuration Sources**:
|
||||
- **claude_settings**: Claude Code settings.json file
|
||||
- **superclaude_config**: SuperClaude superclaude-config.json file
|
||||
- **YAML Files**: config/*.yaml files for modular configuration
|
||||
|
||||
## Intelligent Caching System
|
||||
|
||||
### Cache Structure
|
||||
```python
|
||||
# Cache for all configuration sources
|
||||
self._cache: Dict[str, Dict[str, Any]] = {}
|
||||
self._file_hashes: Dict[str, str] = {}
|
||||
self._last_check: Dict[str, float] = {}
|
||||
self.check_interval = 1.0 # Check files every 1 second max
|
||||
```
|
||||
|
||||
### Cache Validation
|
||||
```python
|
||||
def _should_use_cache(self, config_name: str, config_path: Path) -> bool:
|
||||
if config_name not in self._cache:
|
||||
return False
|
||||
|
||||
# Rate limit file checks
|
||||
now = time.time()
|
||||
if now - self._last_check.get(config_name, 0) < self.check_interval:
|
||||
return True
|
||||
|
||||
# Check if file changed
|
||||
current_hash = self._compute_hash(config_path)
|
||||
return current_hash == self._file_hashes.get(config_name)
|
||||
```
|
||||
|
||||
**Cache Invalidation Strategy**:
|
||||
1. **Rate Limiting**: File checks limited to once per second per configuration
|
||||
2. **Hash-Based Detection**: File modification detection using mtime and size hash
|
||||
3. **Automatic Reload**: Cache invalidation triggers automatic configuration reload
|
||||
4. **Memory Optimization**: Only cache active configurations to minimize memory usage
|
||||
|
||||
### File Change Detection
|
||||
```python
|
||||
def _compute_hash(self, file_path: Path) -> str:
|
||||
"""Compute file hash for change detection."""
|
||||
stat = file_path.stat()
|
||||
return hashlib.md5(f"{stat.st_mtime}:{stat.st_size}".encode()).hexdigest()
|
||||
```
|
||||
|
||||
**Hash Components**:
|
||||
- **Modification Time**: File system mtime for change detection
|
||||
- **File Size**: Content size changes for additional validation
|
||||
- **MD5 Hash**: Combined hash for efficient comparison
|
||||
|
||||
## Configuration Loading Interface
|
||||
|
||||
### Primary Loading Method
|
||||
```python
|
||||
def load_config(self, config_name: str, force_reload: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Load configuration with intelligent caching (supports JSON and YAML).
|
||||
|
||||
Args:
|
||||
config_name: Name of config file or special config identifier
|
||||
- For YAML: config file name without .yaml extension
|
||||
- For JSON: 'claude_settings' or 'superclaude_config'
|
||||
force_reload: Force reload even if cached
|
||||
|
||||
Returns:
|
||||
Parsed configuration dictionary
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If config file doesn't exist
|
||||
ValueError: If config parsing fails
|
||||
"""
|
||||
```
|
||||
|
||||
**Loading Logic**:
|
||||
1. **Source Identification**: Determine if request is for JSON or YAML configuration
|
||||
2. **Cache Validation**: Check if cached version is still valid
|
||||
3. **File Loading**: Read and parse configuration file if reload needed
|
||||
4. **Environment Interpolation**: Process ${VAR} and ${VAR:default} syntax
|
||||
5. **Include Processing**: Handle __include__ directives for modular configuration
|
||||
6. **Cache Update**: Store parsed configuration with metadata
|
||||
|
||||
### Specialized Access Methods
|
||||
|
||||
#### Section Access with Dot Notation
|
||||
```python
|
||||
def get_section(self, config_name: str, section_path: str, default: Any = None) -> Any:
|
||||
"""
|
||||
Get specific section from configuration using dot notation.
|
||||
|
||||
Args:
|
||||
config_name: Configuration file name or identifier
|
||||
section_path: Dot-separated path (e.g., 'routing.ui_components')
|
||||
default: Default value if section not found
|
||||
|
||||
Returns:
|
||||
Configuration section value or default
|
||||
"""
|
||||
config = self.load_config(config_name)
|
||||
|
||||
try:
|
||||
result = config
|
||||
for key in section_path.split('.'):
|
||||
result = result[key]
|
||||
return result
|
||||
except (KeyError, TypeError):
|
||||
return default
|
||||
```
|
||||
|
||||
#### Hook-Specific Configuration
|
||||
```python
|
||||
def get_hook_config(self, hook_name: str, section_path: str = None, default: Any = None) -> Any:
|
||||
"""
|
||||
Get hook-specific configuration from SuperClaude config.
|
||||
|
||||
Args:
|
||||
hook_name: Hook name (e.g., 'session_start', 'pre_tool_use')
|
||||
section_path: Optional dot-separated path within hook config
|
||||
default: Default value if not found
|
||||
|
||||
Returns:
|
||||
Hook configuration or specific section
|
||||
"""
|
||||
base_path = f"hook_configurations.{hook_name}"
|
||||
if section_path:
|
||||
full_path = f"{base_path}.{section_path}"
|
||||
else:
|
||||
full_path = base_path
|
||||
|
||||
return self.get_section('superclaude_config', full_path, default)
|
||||
```
|
||||
|
||||
#### Claude Code Integration
|
||||
```python
|
||||
def get_claude_hooks(self) -> Dict[str, Any]:
|
||||
"""Get Claude Code hook definitions from settings.json."""
|
||||
return self.get_section('claude_settings', 'hooks', {})
|
||||
|
||||
def get_superclaude_config(self, section_path: str = None, default: Any = None) -> Any:
|
||||
"""Get SuperClaude framework configuration."""
|
||||
if section_path:
|
||||
return self.get_section('superclaude_config', section_path, default)
|
||||
else:
|
||||
return self.load_config('superclaude_config')
|
||||
```
|
||||
|
||||
#### MCP Server Configuration
|
||||
```python
|
||||
def get_mcp_server_config(self, server_name: str = None) -> Dict[str, Any]:
|
||||
"""Get MCP server configuration."""
|
||||
if server_name:
|
||||
return self.get_section('superclaude_config', f'mcp_server_integration.servers.{server_name}', {})
|
||||
else:
|
||||
return self.get_section('superclaude_config', 'mcp_server_integration', {})
|
||||
|
||||
def get_performance_targets(self) -> Dict[str, Any]:
|
||||
"""Get performance targets for all components."""
|
||||
return self.get_section('superclaude_config', 'global_configuration.performance_monitoring', {})
|
||||
```
|
||||
|
||||
## Environment Variable Interpolation
|
||||
|
||||
### Interpolation Processing
|
||||
```python
|
||||
def _interpolate_env_vars(self, content: str) -> str:
|
||||
"""Replace environment variables in YAML content."""
|
||||
import re
|
||||
|
||||
def replace_env_var(match):
|
||||
var_name = match.group(1)
|
||||
default_value = match.group(2) if match.group(2) else ""
|
||||
return os.getenv(var_name, default_value)
|
||||
|
||||
# Support ${VAR} and ${VAR:default} syntax
|
||||
pattern = r'\$\{([^}:]+)(?::([^}]*))?\}'
|
||||
return re.sub(pattern, replace_env_var, content)
|
||||
```
|
||||
|
||||
**Supported Syntax**:
|
||||
- **${VAR_NAME}**: Replace with environment variable value or empty string
|
||||
- **${VAR_NAME:default_value}**: Replace with environment variable or default value
|
||||
- **Nested Variables**: Support for complex environment variable combinations
|
||||
|
||||
### Usage Examples
|
||||
```yaml
|
||||
# Configuration with environment interpolation
|
||||
database:
|
||||
host: ${DB_HOST:localhost}
|
||||
port: ${DB_PORT:5432}
|
||||
username: ${DB_USER}
|
||||
password: ${DB_PASS:}
|
||||
|
||||
logging:
|
||||
level: ${LOG_LEVEL:INFO}
|
||||
directory: ${LOG_DIR:./logs}
|
||||
```
|
||||
|
||||
## Modular Configuration Support
|
||||
|
||||
### Include Directive Processing
|
||||
```python
|
||||
def _process_includes(self, config: Dict[str, Any], base_dir: Path) -> Dict[str, Any]:
|
||||
"""Process include directives in configuration."""
|
||||
if not isinstance(config, dict):
|
||||
return config
|
||||
|
||||
# Handle special include key
|
||||
if '__include__' in config:
|
||||
includes = config.pop('__include__')
|
||||
if isinstance(includes, str):
|
||||
includes = [includes]
|
||||
|
||||
for include_file in includes:
|
||||
include_path = base_dir / include_file
|
||||
if include_path.exists():
|
||||
with open(include_path, 'r', encoding='utf-8') as f:
|
||||
included_config = yaml.safe_load(f.read())
|
||||
if isinstance(included_config, dict):
|
||||
# Merge included config (current config takes precedence)
|
||||
included_config.update(config)
|
||||
config = included_config
|
||||
|
||||
return config
|
||||
```
|
||||
|
||||
### Modular Configuration Example
|
||||
```yaml
|
||||
# main.yaml
|
||||
__include__:
|
||||
- "common/logging.yaml"
|
||||
- "environments/production.yaml"
|
||||
|
||||
application:
|
||||
name: "SuperClaude Hooks"
|
||||
version: "1.0.0"
|
||||
|
||||
# Override included values
|
||||
logging:
|
||||
level: "DEBUG" # Overrides value from logging.yaml
|
||||
```
|
||||
|
||||
## JSON Configuration Support
|
||||
|
||||
### JSON Loading with Error Handling
|
||||
```python
|
||||
def _load_json_config(self, config_name: str, force_reload: bool = False) -> Dict[str, Any]:
|
||||
"""Load JSON configuration file."""
|
||||
config_path = self._config_sources[config_name]
|
||||
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Configuration file not found: {config_path}")
|
||||
|
||||
# Check if we need to reload
|
||||
if not force_reload and self._should_use_cache(config_name, config_path):
|
||||
return self._cache[config_name]
|
||||
|
||||
# Load and parse the JSON configuration
|
||||
try:
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
# Environment variable interpolation
|
||||
content = self._interpolate_env_vars(content)
|
||||
|
||||
# Parse JSON
|
||||
config = json.loads(content)
|
||||
|
||||
# Update cache
|
||||
self._cache[config_name] = config
|
||||
self._file_hashes[config_name] = self._compute_hash(config_path)
|
||||
self._last_check[config_name] = time.time()
|
||||
|
||||
return config
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
raise ValueError(f"JSON parsing error in {config_path}: {e}")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Error loading JSON config {config_name}: {e}")
|
||||
```
|
||||
|
||||
**JSON Support Features**:
|
||||
- **Environment Interpolation**: ${VAR} syntax support in JSON files
|
||||
- **Error Handling**: Comprehensive JSON parsing error messages
|
||||
- **Cache Integration**: Same caching behavior as YAML configurations
|
||||
- **Encoding Support**: UTF-8 encoding for international character support
|
||||
|
||||
## Configuration Validation and Error Handling
|
||||
|
||||
### Error Handling Strategy
|
||||
```python
|
||||
def load_config(self, config_name: str, force_reload: bool = False) -> Dict[str, Any]:
|
||||
try:
|
||||
# Configuration loading logic
|
||||
pass
|
||||
except yaml.YAMLError as e:
|
||||
raise ValueError(f"YAML parsing error in {config_path}: {e}")
|
||||
except json.JSONDecodeError as e:
|
||||
raise ValueError(f"JSON parsing error in {config_path}: {e}")
|
||||
except FileNotFoundError as e:
|
||||
raise FileNotFoundError(f"Configuration file not found: {config_path}")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Error loading config {config_name}: {e}")
|
||||
```
|
||||
|
||||
**Error Categories**:
|
||||
- **File Not Found**: Configuration file missing or inaccessible
|
||||
- **Parsing Errors**: YAML or JSON syntax errors with detailed messages
|
||||
- **Permission Errors**: File system permission issues
|
||||
- **General Errors**: Unexpected errors with full context
|
||||
|
||||
### Graceful Degradation
|
||||
```python
|
||||
def get_section(self, config_name: str, section_path: str, default: Any = None) -> Any:
|
||||
try:
|
||||
result = config
|
||||
for key in section_path.split('.'):
|
||||
result = result[key]
|
||||
return result
|
||||
except (KeyError, TypeError):
|
||||
return default # Graceful fallback to default value
|
||||
```
|
||||
|
||||
## Performance Optimization
|
||||
|
||||
### Cache Reload Management
|
||||
```python
|
||||
def reload_all(self) -> None:
|
||||
"""Force reload of all cached configurations."""
|
||||
for config_name in list(self._cache.keys()):
|
||||
self.load_config(config_name, force_reload=True)
|
||||
```
|
||||
|
||||
### Hook Status Checking
|
||||
```python
|
||||
def is_hook_enabled(self, hook_name: str) -> bool:
|
||||
"""Check if a specific hook is enabled."""
|
||||
return self.get_hook_config(hook_name, 'enabled', False)
|
||||
```
|
||||
|
||||
**Performance Optimizations**:
|
||||
- **Selective Reloading**: Only reload changed configurations
|
||||
- **Rate-Limited Checks**: File modification checks limited to once per second
|
||||
- **Memory Efficient**: Cache only active configurations
|
||||
- **Batch Operations**: Multiple configuration accesses use cached versions
|
||||
|
||||
## Integration with Hooks
|
||||
|
||||
### Global Instance
|
||||
```python
|
||||
# Global instance for shared use across hooks
|
||||
config_loader = UnifiedConfigLoader(".")
|
||||
```
|
||||
|
||||
### Hook Usage Pattern
|
||||
```python
|
||||
from shared.yaml_loader import config_loader
|
||||
|
||||
# Load hook-specific configuration
|
||||
hook_config = config_loader.get_hook_config('pre_tool_use')
|
||||
performance_target = config_loader.get_hook_config('pre_tool_use', 'performance_target_ms', 200)
|
||||
|
||||
# Load MCP server configuration
|
||||
mcp_config = config_loader.get_mcp_server_config('sequential')
|
||||
all_mcp_servers = config_loader.get_mcp_server_config()
|
||||
|
||||
# Load global performance targets
|
||||
performance_targets = config_loader.get_performance_targets()
|
||||
|
||||
# Check if hook is enabled
|
||||
if config_loader.is_hook_enabled('pre_tool_use'):
|
||||
# Execute hook logic
|
||||
pass
|
||||
```
|
||||
|
||||
### Configuration Structure Examples
|
||||
|
||||
#### SuperClaude Configuration (superclaude-config.json)
|
||||
```json
|
||||
{
|
||||
"hook_configurations": {
|
||||
"session_start": {
|
||||
"enabled": true,
|
||||
"performance_target_ms": 50,
|
||||
"initialization_timeout_ms": 1000
|
||||
},
|
||||
"pre_tool_use": {
|
||||
"enabled": true,
|
||||
"performance_target_ms": 200,
|
||||
"pattern_detection_enabled": true,
|
||||
"mcp_intelligence_enabled": true
|
||||
}
|
||||
},
|
||||
"mcp_server_integration": {
|
||||
"servers": {
|
||||
"sequential": {
|
||||
"enabled": true,
|
||||
"activation_cost_ms": 200,
|
||||
"performance_profile": "intensive"
|
||||
},
|
||||
"context7": {
|
||||
"enabled": true,
|
||||
"activation_cost_ms": 150,
|
||||
"performance_profile": "standard"
|
||||
}
|
||||
}
|
||||
},
|
||||
"global_configuration": {
|
||||
"performance_monitoring": {
|
||||
"enabled": true,
|
||||
"target_percentile": 95,
|
||||
"alert_threshold_ms": 500
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### YAML Configuration (config/logging.yaml)
|
||||
```yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: ${LOG_LEVEL:INFO}
|
||||
|
||||
file_settings:
|
||||
log_directory: ${LOG_DIR:cache/logs}
|
||||
retention_days: ${LOG_RETENTION:30}
|
||||
max_file_size_mb: 10
|
||||
|
||||
hook_logging:
|
||||
log_lifecycle: true
|
||||
log_decisions: true
|
||||
log_errors: true
|
||||
log_performance: true
|
||||
|
||||
# Include common configuration
|
||||
__include__:
|
||||
- "common/base.yaml"
|
||||
```
|
||||
|
||||
## Performance Characteristics
|
||||
|
||||
### Access Performance
|
||||
- **Cached Access**: <10ms average for configuration retrieval
|
||||
- **Initial Load**: <50ms for typical configuration files
|
||||
- **Hot Reload**: <75ms for configuration file changes
|
||||
- **Bulk Access**: <5ms per additional section access from cached config
|
||||
|
||||
### Memory Efficiency
|
||||
- **Configuration Cache**: ~1-5KB per cached configuration file
|
||||
- **File Hash Cache**: ~50B per tracked configuration file
|
||||
- **Include Processing**: Dynamic memory usage based on included file sizes
|
||||
- **Memory Cleanup**: Automatic cleanup of unused cached configurations
|
||||
|
||||
### File System Optimization
|
||||
- **Rate-Limited Checks**: Maximum one file system check per second per configuration
|
||||
- **Efficient Hashing**: mtime + size based change detection
|
||||
- **Batch Processing**: Multiple configuration accesses use single file check
|
||||
- **Error Caching**: Failed configuration loads cached to prevent repeated failures
|
||||
|
||||
## Error Handling and Recovery
|
||||
|
||||
### Configuration Loading Failures
|
||||
```python
|
||||
# Graceful degradation for missing configurations
|
||||
try:
|
||||
config = config_loader.load_config('optional_config')
|
||||
except FileNotFoundError:
|
||||
config = {} # Use empty configuration
|
||||
except ValueError as e:
|
||||
logger.log_error("config_loader", f"Configuration parsing failed: {e}")
|
||||
config = {} # Use empty configuration with error logging
|
||||
```
|
||||
|
||||
### Cache Corruption Recovery
|
||||
- **Hash Mismatch**: Automatic cache invalidation and reload
|
||||
- **Memory Corruption**: Cache clearing and fresh reload
|
||||
- **File Permission Changes**: Graceful fallback to default values
|
||||
- **Network File System Issues**: Retry logic with exponential backoff
|
||||
|
||||
### Environment Variable Issues
|
||||
- **Missing Variables**: Use default values or empty strings as specified
|
||||
- **Invalid Syntax**: Log warning and use literal value
|
||||
- **Circular References**: Detection and prevention of infinite loops
|
||||
|
||||
## Configuration Best Practices
|
||||
|
||||
### File Organization
|
||||
```
|
||||
project_root/
|
||||
├── settings.json # Claude Code settings
|
||||
├── superclaude-config.json # SuperClaude framework config
|
||||
└── config/
|
||||
├── logging.yaml # Logging configuration
|
||||
├── orchestrator.yaml # MCP server routing
|
||||
├── modes.yaml # Mode detection patterns
|
||||
└── common/
|
||||
└── base.yaml # Shared configuration elements
|
||||
```
|
||||
|
||||
### Configuration Conventions
|
||||
- **JSON for Integration**: Use JSON for Claude Code and SuperClaude integration configs
|
||||
- **YAML for Modularity**: Use YAML for complex, hierarchical configurations
|
||||
- **Environment Variables**: Use ${VAR} syntax for deployment-specific values
|
||||
- **Include Files**: Use __include__ for shared configuration elements
|
||||
|
||||
## Usage Examples
|
||||
|
||||
### Basic Configuration Loading
|
||||
```python
|
||||
from shared.yaml_loader import config_loader
|
||||
|
||||
# Load hook configuration
|
||||
hook_config = config_loader.get_hook_config('pre_tool_use')
|
||||
print(f"Hook enabled: {hook_config.get('enabled', False)}")
|
||||
print(f"Performance target: {hook_config.get('performance_target_ms', 200)}ms")
|
||||
|
||||
# Load MCP server configuration
|
||||
sequential_config = config_loader.get_mcp_server_config('sequential')
|
||||
print(f"Sequential activation cost: {sequential_config.get('activation_cost_ms', 200)}ms")
|
||||
```
|
||||
|
||||
### Advanced Configuration Access
|
||||
```python
|
||||
# Get nested configuration with dot notation
|
||||
logging_level = config_loader.get_section('logging', 'file_settings.log_level', 'INFO')
|
||||
performance_target = config_loader.get_section('superclaude_config', 'hook_configurations.pre_tool_use.performance_target_ms', 200)
|
||||
|
||||
# Check hook status
|
||||
if config_loader.is_hook_enabled('mcp_intelligence'):
|
||||
# Initialize MCP intelligence
|
||||
pass
|
||||
|
||||
# Force reload all configurations
|
||||
config_loader.reload_all()
|
||||
```
|
||||
|
||||
### Environment Variable Integration
|
||||
```python
|
||||
# Configuration automatically processes environment variables
|
||||
# In config/database.yaml:
|
||||
# database:
|
||||
# host: ${DB_HOST:localhost}
|
||||
# port: ${DB_PORT:5432}
|
||||
|
||||
db_config = config_loader.get_section('database', 'host') # Uses DB_HOST env var or 'localhost'
|
||||
```
|
||||
|
||||
## Dependencies and Relationships
|
||||
|
||||
### Internal Dependencies
|
||||
- **Standard Libraries**: os, json, yaml, time, hashlib, pathlib, re
|
||||
- **No External Dependencies**: Self-contained configuration management system
|
||||
|
||||
### Framework Integration
|
||||
- **Hook Configuration**: Centralized configuration for all 7 SuperClaude hooks
|
||||
- **MCP Server Integration**: Configuration management for MCP server coordination
|
||||
- **Performance Monitoring**: Configuration-driven performance target management
|
||||
|
||||
### Global Availability
|
||||
- **Shared Instance**: config_loader global instance available to all hooks
|
||||
- **Consistent Interface**: Standardized configuration access across all modules
|
||||
- **Hot-Reload Support**: Dynamic configuration updates without hook restart
|
||||
|
||||
---
|
||||
|
||||
*This module serves as the foundational configuration management system for the entire SuperClaude framework, providing high-performance, flexible, and reliable configuration loading with comprehensive error handling and hot-reload capabilities.*
|
||||
@@ -1,246 +0,0 @@
|
||||
# Framework-Hooks System Overview
|
||||
|
||||
## System Architecture
|
||||
|
||||
The Framework-Hooks system provides lifecycle hooks for Claude Code that implement SuperClaude framework patterns. The system consists of:
|
||||
|
||||
### Core Components
|
||||
|
||||
1. **Lifecycle Hooks** - 7 Python modules (session_start.py, pre_tool_use.py, post_tool_use.py, pre_compact.py, notification.py, stop.py, subagent_stop.py)
|
||||
2. **Shared Modules** - 9 Python modules providing shared functionality (framework_logic.py, pattern_detection.py, mcp_intelligence.py, learning_engine.py, compression_engine.py, intelligence_engine.py, validate_system.py, yaml_loader.py, logger.py)
|
||||
3. **Configuration System** - 19 YAML files defining behavior and settings
|
||||
4. **Pattern System** - YAML pattern files in minimal/, dynamic/, and learned/ directories
|
||||
|
||||
### Architecture Layers
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────┐
|
||||
│ Claude Code Interface │
|
||||
├─────────────────────────────────────────┤
|
||||
│ Lifecycle Hooks │
|
||||
│ ┌─────┬─────┬─────┬─────┬─────┬─────┐ │
|
||||
│ │Start│Pre │Post │Pre │Notif│Stop │ │
|
||||
│ │ │Tool │Tool │Comp │ │ │ │
|
||||
│ └─────┴─────┴─────┴─────┴─────┴─────┘ │
|
||||
├─────────────────────────────────────────┤
|
||||
│ Shared Intelligence │
|
||||
│ ┌────────────┬─────────────┬─────────┐ │
|
||||
│ │ Framework │ Pattern │Learning │ │
|
||||
│ │ Logic │ Detection │Engine │ │
|
||||
│ └────────────┴─────────────┴─────────┘ │
|
||||
├─────────────────────────────────────────┤
|
||||
│ YAML Configuration │
|
||||
│ ┌─────────────┬────────────┬─────────┐ │
|
||||
│ │Performance │Modes │Logging │ │
|
||||
│ │Targets │Config │Config │ │
|
||||
│ └─────────────┴────────────┴─────────┘ │
|
||||
└─────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Purpose
|
||||
|
||||
The Framework-Hooks system implements the SuperClaude framework through lifecycle hooks that run during Claude Code execution.
|
||||
|
||||
### Implementation Features
|
||||
|
||||
1. **Session Management** - Implements session lifecycle patterns from SESSION_LIFECYCLE.md
|
||||
2. **Mode Detection** - Activates SuperClaude modes (brainstorming, task management, token efficiency, introspection) based on user input patterns
|
||||
3. **MCP Server Routing** - Routes operations to appropriate MCP servers (Context7, Sequential, Magic, Playwright, Morphllm, Serena)
|
||||
4. **Configuration Management** - Loads settings from YAML files to customize behavior
|
||||
5. **Pattern Recognition** - Detects project types and operation patterns to apply appropriate configurations
|
||||
|
||||
### Design Goals
|
||||
|
||||
- **Framework Compliance**: Implement SuperClaude patterns and principles
|
||||
- **Configuration Flexibility**: YAML-driven behavior customization
|
||||
- **Performance Targets**: 50ms session_start, 200ms pre_tool_use, etc. (as defined in performance.yaml)
|
||||
- **Pattern-Based Operation**: Use project type and operation detection for intelligent behavior
|
||||
|
||||
## Pattern-Based Operation
|
||||
|
||||
The system uses pattern files to configure behavior based on detected project characteristics:
|
||||
|
||||
### Pattern Detection
|
||||
```
|
||||
User Request → Project Type Detection → Load Pattern Files → Apply Configuration → Execute
|
||||
```
|
||||
|
||||
### Pattern System Components
|
||||
|
||||
1. **Minimal Patterns** - Essential patterns loaded during session initialization (e.g., python_project.yaml, react_project.yaml)
|
||||
2. **Dynamic Patterns** - Runtime patterns for mode detection and MCP activation
|
||||
3. **Learned Patterns** - User preference and project-specific optimizations (stored in learned/ directory)
|
||||
|
||||
### Core Modules
|
||||
|
||||
1. **Pattern Detection (pattern_detection.py)**
|
||||
- 45KB module detecting project types and operation patterns
|
||||
- Analyzes file structures, dependencies, and user input
|
||||
|
||||
2. **Learning Engine (learning_engine.py)**
|
||||
- 40KB module for user preference tracking
|
||||
- Records effectiveness of different configurations
|
||||
|
||||
3. **MCP Intelligence (mcp_intelligence.py)**
|
||||
- 31KB module for MCP server routing decisions
|
||||
- Maps operations to appropriate servers based on capabilities
|
||||
|
||||
4. **Framework Logic (framework_logic.py)**
|
||||
- 12KB module implementing SuperClaude principles
|
||||
- Handles complexity scoring and risk assessment
|
||||
|
||||
## Configuration System
|
||||
|
||||
The system is configured through YAML files and settings:
|
||||
|
||||
### Configuration Files (19 total)
|
||||
|
||||
Configuration is defined in /config/ directory:
|
||||
- **performance.yaml** (345 lines) - Performance targets and thresholds
|
||||
- **modes.yaml** - Mode detection patterns and settings
|
||||
- **session.yaml** - Session lifecycle configuration
|
||||
- **logging.yaml** - Logging configuration and levels
|
||||
- **compression.yaml** - Token efficiency settings
|
||||
- Other specialized configuration files
|
||||
|
||||
### Settings Integration
|
||||
|
||||
Claude Code hooks are configured through settings.json:
|
||||
- **Hook Timeouts**: session_start (10s), pre_tool_use (15s), etc.
|
||||
- **Hook Commands**: Python execution paths for each lifecycle hook
|
||||
- **Hook Matching**: All hooks configured with "*" matcher (apply to all sessions)
|
||||
|
||||
### Performance Targets (from performance.yaml)
|
||||
|
||||
| Component | Target | Warning | Critical |
|
||||
|---------------|--------|---------|----------|
|
||||
| session_start | 50ms | 75ms | 100ms |
|
||||
| pre_tool_use | 200ms | 300ms | 500ms |
|
||||
| post_tool_use | 100ms | 150ms | 250ms |
|
||||
| pre_compact | 150ms | 200ms | 300ms |
|
||||
| notification | 100ms | 150ms | 200ms |
|
||||
| stop | 200ms | 300ms | 500ms |
|
||||
| subagent_stop | 150ms | 200ms | 300ms |
|
||||
|
||||
## Directory Structure
|
||||
|
||||
```
|
||||
Framework-Hooks/
|
||||
├── hooks/ # Lifecycle hook implementations (7 Python files)
|
||||
│ ├── session_start.py # 703 lines - Session initialization
|
||||
│ ├── pre_tool_use.py # MCP server selection and optimization
|
||||
│ ├── post_tool_use.py # Validation and learning integration
|
||||
│ ├── pre_compact.py # Token efficiency and compression
|
||||
│ ├── notification.py # Pattern updates and notifications
|
||||
│ ├── stop.py # Session analytics and persistence
|
||||
│ ├── subagent_stop.py # Task management coordination
|
||||
│ └── shared/ # Shared modules (9 Python files)
|
||||
│ ├── framework_logic.py # 12KB - SuperClaude principles
|
||||
│ ├── pattern_detection.py # 45KB - Pattern recognition
|
||||
│ ├── mcp_intelligence.py # 31KB - MCP server routing
|
||||
│ ├── learning_engine.py # 40KB - User preference learning
|
||||
│ ├── compression_engine.py # 27KB - Token optimization
|
||||
│ ├── intelligence_engine.py # 18KB - Core intelligence
|
||||
│ ├── validate_system.py # 32KB - System validation
|
||||
│ ├── yaml_loader.py # 16KB - Configuration loading
|
||||
│ └── logger.py # 11KB - Logging utilities
|
||||
├── config/ # Configuration files (19 YAML files)
|
||||
│ ├── performance.yaml # 345 lines - Performance targets
|
||||
│ ├── modes.yaml # Mode detection patterns
|
||||
│ ├── session.yaml # Session management settings
|
||||
│ ├── logging.yaml # Logging configuration
|
||||
│ ├── compression.yaml # Token efficiency settings
|
||||
│ └── ... # Additional configuration files
|
||||
├── patterns/ # Pattern storage
|
||||
│ ├── dynamic/ # Runtime pattern detection (mode_detection.yaml, mcp_activation.yaml)
|
||||
│ ├── learned/ # User preferences (user_preferences.yaml, project_optimizations.yaml)
|
||||
│ └── minimal/ # Project patterns (python_project.yaml, react_project.yaml)
|
||||
├── docs/ # Documentation
|
||||
└── settings.json # Claude Code hook configuration
|
||||
```
|
||||
|
||||
## Key Components
|
||||
|
||||
### Lifecycle Hooks
|
||||
|
||||
1. **session_start.py** (703 lines)
|
||||
- Runs at session start with 10-second timeout
|
||||
- Detects project type and loads appropriate patterns
|
||||
- Activates modes based on user input (brainstorming, task management, etc.)
|
||||
- Routes to appropriate MCP servers
|
||||
|
||||
2. **pre_tool_use.py**
|
||||
- Runs before each tool use with 15-second timeout
|
||||
- Selects MCP servers based on operation type
|
||||
- Applies performance optimizations
|
||||
|
||||
3. **post_tool_use.py**
|
||||
- Runs after tool execution with 10-second timeout
|
||||
- Validates results and logs learning data
|
||||
- Updates effectiveness tracking
|
||||
|
||||
4. **pre_compact.py**
|
||||
- Runs before token compression with 15-second timeout
|
||||
- Applies compression strategies based on content type
|
||||
- Preserves important content while optimizing tokens
|
||||
|
||||
5. **notification.py**
|
||||
- Handles notifications with 10-second timeout
|
||||
- Updates pattern caches and configurations
|
||||
|
||||
6. **stop.py**
|
||||
- Runs at session end with 15-second timeout
|
||||
- Generates session analytics and saves learning data
|
||||
|
||||
7. **subagent_stop.py**
|
||||
- Handles subagent coordination with 15-second timeout
|
||||
- Tracks delegation performance
|
||||
|
||||
### Shared Modules
|
||||
|
||||
Core functionality shared across hooks:
|
||||
|
||||
- **pattern_detection.py** (45KB) - Project and operation pattern recognition
|
||||
- **learning_engine.py** (40KB) - User preference and effectiveness tracking
|
||||
- **validate_system.py** (32KB) - System validation and health checks
|
||||
- **mcp_intelligence.py** (31KB) - MCP server routing logic
|
||||
- **compression_engine.py** (27KB) - Token optimization algorithms
|
||||
- **intelligence_engine.py** (18KB) - Core intelligence coordination
|
||||
- **yaml_loader.py** (16KB) - Configuration file loading
|
||||
- **framework_logic.py** (12KB) - SuperClaude framework implementation
|
||||
- **logger.py** (11KB) - Logging and debugging utilities
|
||||
|
||||
## Integration with SuperClaude
|
||||
|
||||
The Framework-Hooks system implements SuperClaude framework patterns through lifecycle hooks:
|
||||
|
||||
### Mode Detection and Activation
|
||||
|
||||
The session_start hook detects user intent and activates appropriate SuperClaude modes:
|
||||
|
||||
1. **Brainstorming Mode** - Activated for ambiguous requests ("not sure", "thinking about")
|
||||
2. **Task Management Mode** - Activated for multi-step operations and complex builds
|
||||
3. **Token Efficiency Mode** - Activated during resource constraints or when brevity requested
|
||||
4. **Introspection Mode** - Activated for meta-analysis requests
|
||||
|
||||
### MCP Server Routing
|
||||
|
||||
The hooks route operations to appropriate MCP servers based on detected patterns:
|
||||
|
||||
- **Context7** - Library documentation and framework patterns
|
||||
- **Sequential** - Multi-step reasoning and complex analysis
|
||||
- **Magic** - UI component generation and design systems
|
||||
- **Playwright** - Browser automation and testing
|
||||
- **Morphllm** - File editing with pattern optimization
|
||||
- **Serena** - Semantic analysis and memory management
|
||||
|
||||
### Framework Implementation
|
||||
|
||||
The hooks implement core SuperClaude concepts:
|
||||
|
||||
- **Rules Compliance** - File operation validation and security protocols
|
||||
- **Principles Enforcement** - Evidence-based decisions and code quality standards
|
||||
- **Performance Targets** - Sub-200ms operation targets with monitoring
|
||||
- **Configuration Management** - YAML-driven behavior customization
|
||||
|
||||
The system provides SuperClaude framework functionality through Python hooks that run during Claude Code execution, enabling intelligent behavior based on project patterns and user preferences while maintaining performance targets defined in the configuration files.
|
||||
@@ -1,218 +0,0 @@
|
||||
# Creating Patterns: Developer Guide
|
||||
|
||||
## Overview
|
||||
|
||||
This guide explains how to create new patterns for the Framework-Hooks pattern system. Patterns are YAML files that define automatic behavior, MCP server activation, and optimization rules for different contexts.
|
||||
|
||||
## Pattern Development Process
|
||||
|
||||
1. **Identify the Need**: Determine what behavior should be automated
|
||||
2. **Choose Pattern Type**: Select minimal, dynamic, or learned pattern
|
||||
3. **Define Structure**: Create YAML structure following the schema
|
||||
4. **Test Pattern**: Verify the pattern works correctly
|
||||
5. **Document Pattern**: Add appropriate documentation
|
||||
|
||||
## Creating Minimal Patterns
|
||||
|
||||
Minimal patterns detect project types and configure initial MCP server activation.
|
||||
|
||||
### Minimal Pattern Template
|
||||
|
||||
```yaml
|
||||
# File: /patterns/minimal/{project_type}_project.yaml
|
||||
|
||||
project_type: "unique_identifier" # e.g., "python", "react", "vue"
|
||||
detection_patterns: # File/directory existence patterns
|
||||
- "*.{ext} files present" # File extension patterns
|
||||
- "{manifest_file} dependency" # Dependency manifest detection
|
||||
- "{directory}/ directories" # Directory structure patterns
|
||||
|
||||
auto_flags: # Automatic flag activation
|
||||
- "--{primary_server}" # Primary server flag
|
||||
- "--{secondary_server}" # Secondary server flag
|
||||
|
||||
mcp_servers:
|
||||
primary: "{server_name}" # Primary MCP server
|
||||
secondary: ["{server1}", "{server2}"] # Fallback servers
|
||||
|
||||
patterns:
|
||||
file_structure: # Expected project structure
|
||||
- "{directory}/" # Key directories
|
||||
- "{file_pattern}" # Important files
|
||||
common_tasks: # Typical operations
|
||||
- "{task_description}" # Task patterns
|
||||
|
||||
intelligence:
|
||||
mode_triggers: # Mode activation patterns
|
||||
- "{mode_name}: {trigger_condition}"
|
||||
validation_focus: # Quality validation priorities
|
||||
- "{validation_type}"
|
||||
|
||||
performance_targets:
|
||||
bootstrap_ms: {target_milliseconds} # Bootstrap time target
|
||||
context_size: "{size}KB" # Context footprint
|
||||
cache_duration: "{duration}min" # Cache retention time
|
||||
```
|
||||
|
||||
### Example: Vue.js Project Pattern
|
||||
|
||||
```yaml
|
||||
project_type: "vue"
|
||||
detection_patterns:
|
||||
- "package.json with vue dependency"
|
||||
- "src/ directory with .vue files"
|
||||
- "vue.config.js or vite.config.js"
|
||||
|
||||
auto_flags:
|
||||
- "--magic" # Vue component generation
|
||||
- "--context7" # Vue documentation
|
||||
|
||||
mcp_servers:
|
||||
primary: "magic"
|
||||
secondary: ["context7", "morphllm"]
|
||||
|
||||
patterns:
|
||||
file_structure:
|
||||
- "src/components/"
|
||||
- "src/views/"
|
||||
- "src/composables/"
|
||||
- "src/stores/"
|
||||
common_tasks:
|
||||
- "component development"
|
||||
- "composable creation"
|
||||
- "store management"
|
||||
- "routing configuration"
|
||||
|
||||
intelligence:
|
||||
mode_triggers:
|
||||
- "task_management: component|view|composable"
|
||||
- "token_efficiency: context >75%"
|
||||
validation_focus:
|
||||
- "vue_syntax"
|
||||
- "composition_api"
|
||||
- "reactivity_patterns"
|
||||
- "performance"
|
||||
|
||||
performance_targets:
|
||||
bootstrap_ms: 32
|
||||
context_size: "3.2KB"
|
||||
cache_duration: "55min"
|
||||
## Creating Dynamic Patterns
|
||||
|
||||
Dynamic patterns define runtime activation rules for modes and MCP servers.
|
||||
|
||||
### Dynamic Pattern Structure
|
||||
|
||||
```yaml
|
||||
# For mode detection patterns
|
||||
mode_detection:
|
||||
{mode_name}:
|
||||
triggers:
|
||||
- "trigger description"
|
||||
patterns:
|
||||
- "keyword or phrase"
|
||||
confidence_threshold: 0.7
|
||||
activation_hooks: ["session_start", "pre_tool_use"]
|
||||
coordination:
|
||||
command: "/sc:command"
|
||||
mcp_servers: ["server1", "server2"]
|
||||
|
||||
# For MCP activation patterns
|
||||
activation_patterns:
|
||||
{server_name}:
|
||||
triggers:
|
||||
- "activation trigger"
|
||||
context_keywords:
|
||||
- "keyword"
|
||||
activation_confidence: 0.8
|
||||
|
||||
coordination_patterns:
|
||||
hybrid_intelligence:
|
||||
{server1}_{server2}:
|
||||
condition: "coordination condition"
|
||||
strategy: "coordination strategy"
|
||||
confidence_threshold: 0.8
|
||||
|
||||
performance_optimization:
|
||||
cache_activation_decisions: true
|
||||
cache_duration_minutes: 15
|
||||
batch_similar_requests: true
|
||||
lazy_loading: true
|
||||
```
|
||||
|
||||
## Creating Learned Patterns
|
||||
|
||||
Learned patterns track usage data and adapt behavior over time.
|
||||
|
||||
### Learned Pattern Structure
|
||||
|
||||
```yaml
|
||||
# For user preferences
|
||||
user_profile:
|
||||
id: "user_identifier"
|
||||
created: "2025-01-31"
|
||||
last_updated: "2025-01-31"
|
||||
sessions_analyzed: 0
|
||||
|
||||
learned_preferences:
|
||||
communication_style:
|
||||
verbosity_preference: "balanced"
|
||||
technical_depth: "high"
|
||||
symbol_usage_comfort: "high"
|
||||
|
||||
workflow_patterns:
|
||||
preferred_thinking_mode: "--think-hard"
|
||||
mcp_server_preferences:
|
||||
- "serena"
|
||||
- "sequential"
|
||||
mode_activation_frequency:
|
||||
task_management: 0.8
|
||||
token_efficiency: 0.6
|
||||
|
||||
# For project optimizations
|
||||
project_profile:
|
||||
id: "project_identifier"
|
||||
type: "project_type"
|
||||
created: "2025-01-31"
|
||||
optimization_cycles: 0
|
||||
|
||||
learned_optimizations:
|
||||
workflow_optimizations:
|
||||
effective_sequences:
|
||||
- sequence: ["Read", "Edit", "Validate"]
|
||||
success_rate: 0.95
|
||||
context: "documentation updates"
|
||||
|
||||
mcp_server_effectiveness:
|
||||
serena:
|
||||
effectiveness: 0.9
|
||||
optimal_contexts:
|
||||
- "framework analysis"
|
||||
performance_notes: "excellent for project context"
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Pattern Design Guidelines
|
||||
|
||||
1. **Be Specific**: Use unique identifiers and clear detection criteria
|
||||
2. **Set Appropriate Thresholds**: Match confidence thresholds to resource impact
|
||||
3. **Include Fallbacks**: Define secondary servers and graceful degradation
|
||||
4. **Document Rationale**: Explain why specific servers or settings were chosen
|
||||
5. **Test Thoroughly**: Verify patterns work in different scenarios
|
||||
|
||||
### Performance Considerations
|
||||
|
||||
1. **Keep Minimal Patterns Small**: Aim for fast loading and low memory usage
|
||||
2. **Set Realistic Targets**: Bootstrap times should be achievable
|
||||
3. **Cache Wisely**: Set appropriate cache durations for stability
|
||||
4. **Monitor Effectiveness**: Track pattern performance over time
|
||||
|
||||
### Integration Testing
|
||||
|
||||
1. **Hook Integration**: Verify patterns work with Framework-Hooks lifecycle
|
||||
2. **MCP Coordination**: Test server activation and coordination rules
|
||||
3. **Mode Transitions**: Ensure smooth transitions between modes
|
||||
4. **Error Handling**: Test graceful degradation when servers unavailable
|
||||
|
||||
Pattern creation requires understanding the specific use case, careful design of activation rules, and thorough testing to ensure reliable operation within the Framework-Hooks system.
|
||||
@@ -1,191 +0,0 @@
|
||||
# Dynamic Patterns: Runtime Mode Detection and MCP Activation
|
||||
|
||||
## Overview
|
||||
|
||||
Dynamic patterns provide runtime mode detection and MCP server activation based on user context and requests. These patterns are stored in `/patterns/dynamic/` and use confidence thresholds to determine when to activate specific modes or MCP servers during operation.
|
||||
|
||||
## Purpose
|
||||
|
||||
Dynamic patterns handle:
|
||||
|
||||
- **Mode Detection**: Detect when to activate behavioral modes (brainstorming, task management, etc.)
|
||||
- **MCP Server Activation**: Determine which MCP servers to activate based on context
|
||||
- **Confidence Thresholds**: Use probability scores to make activation decisions
|
||||
- **Coordination Rules**: Define how multiple servers or modes work together
|
||||
|
||||
## Pattern Structure
|
||||
|
||||
Dynamic patterns use confidence-based activation with trigger patterns and context analysis.
|
||||
|
||||
## Current Dynamic Patterns
|
||||
|
||||
### Mode Detection Pattern (`mode_detection.yaml`)
|
||||
|
||||
This pattern defines how different behavioral modes are detected and activated:
|
||||
|
||||
```yaml
|
||||
mode_detection:
|
||||
brainstorming:
|
||||
triggers:
|
||||
- "vague project requests"
|
||||
- "exploration keywords"
|
||||
- "uncertainty indicators"
|
||||
- "new project discussions"
|
||||
patterns:
|
||||
- "I want to build"
|
||||
- "thinking about"
|
||||
- "not sure"
|
||||
- "explore"
|
||||
- "brainstorm"
|
||||
- "figure out"
|
||||
confidence_threshold: 0.7
|
||||
activation_hooks: ["session_start", "pre_tool_use"]
|
||||
coordination:
|
||||
command: "/sc:brainstorm"
|
||||
mcp_servers: ["sequential", "context7"]
|
||||
|
||||
task_management:
|
||||
triggers:
|
||||
- "multi-step operations"
|
||||
- "build/implement keywords"
|
||||
- "system-wide scope"
|
||||
- "delegation indicators"
|
||||
patterns:
|
||||
- "build"
|
||||
- "implement"
|
||||
- "create"
|
||||
- "system"
|
||||
- "comprehensive"
|
||||
- "multiple files"
|
||||
confidence_threshold: 0.8
|
||||
activation_hooks: ["pre_tool_use", "subagent_stop"]
|
||||
coordination:
|
||||
wave_orchestration: true
|
||||
delegation_patterns: true
|
||||
```
|
||||
|
||||
The pattern includes similar configurations for `token_efficiency` (threshold 0.75) and `introspection` (threshold 0.6) modes.
|
||||
|
||||
### MCP Activation Pattern (`mcp_activation.yaml`)
|
||||
|
||||
This pattern defines how MCP servers are activated based on context and user requests:
|
||||
|
||||
```yaml
|
||||
activation_patterns:
|
||||
context7:
|
||||
triggers:
|
||||
- "import statements from external libraries"
|
||||
- "framework-specific questions"
|
||||
- "documentation requests"
|
||||
- "best practices queries"
|
||||
context_keywords:
|
||||
- "how to use"
|
||||
- "documentation"
|
||||
- "examples"
|
||||
- "patterns"
|
||||
activation_confidence: 0.8
|
||||
|
||||
sequential:
|
||||
triggers:
|
||||
- "complex debugging scenarios"
|
||||
- "multi-step analysis requests"
|
||||
- "--think flags detected"
|
||||
- "system design questions"
|
||||
context_keywords:
|
||||
- "analyze"
|
||||
- "debug"
|
||||
- "complex"
|
||||
- "system"
|
||||
- "architecture"
|
||||
activation_confidence: 0.85
|
||||
|
||||
magic:
|
||||
triggers:
|
||||
- "UI component requests"
|
||||
- "design system queries"
|
||||
- "frontend development"
|
||||
- "component keywords"
|
||||
context_keywords:
|
||||
- "component"
|
||||
- "UI"
|
||||
- "frontend"
|
||||
- "design"
|
||||
- "interface"
|
||||
activation_confidence: 0.9
|
||||
|
||||
serena:
|
||||
triggers:
|
||||
- "semantic analysis"
|
||||
- "project-wide operations"
|
||||
- "symbol navigation"
|
||||
- "memory management"
|
||||
context_keywords:
|
||||
- "analyze"
|
||||
- "project"
|
||||
- "semantic"
|
||||
- "memory"
|
||||
- "context"
|
||||
activation_confidence: 0.75
|
||||
|
||||
coordination_patterns:
|
||||
hybrid_intelligence:
|
||||
serena_morphllm:
|
||||
condition: "complex editing with semantic understanding"
|
||||
strategy: "serena analyzes, morphllm executes"
|
||||
confidence_threshold: 0.8
|
||||
|
||||
multi_server_activation:
|
||||
max_concurrent: 3
|
||||
priority_order:
|
||||
- "serena"
|
||||
- "sequential"
|
||||
- "context7"
|
||||
- "magic"
|
||||
- "morphllm"
|
||||
- "playwright"
|
||||
|
||||
performance_optimization:
|
||||
cache_activation_decisions: true
|
||||
cache_duration_minutes: 15
|
||||
batch_similar_requests: true
|
||||
lazy_loading: true
|
||||
```
|
||||
|
||||
## Confidence Thresholds
|
||||
|
||||
Dynamic patterns use confidence scores to determine activation:
|
||||
|
||||
- **Higher Thresholds (0.8-0.9)**: Used for resource-intensive operations (task management, magic)
|
||||
- **Medium Thresholds (0.7-0.8)**: Used for standard operations (brainstorming, context7)
|
||||
- **Lower Thresholds (0.6-0.75)**: Used for lightweight operations (introspection, serena)
|
||||
|
||||
## Coordination Patterns
|
||||
|
||||
The `mcp_activation.yaml` pattern includes coordination rules for:
|
||||
|
||||
- **Hybrid Intelligence**: Coordinated server usage (e.g., serena analyzes, morphllm executes)
|
||||
- **Multi-Server Limits**: Maximum 3 concurrent servers to manage resources
|
||||
- **Priority Ordering**: Server activation priority when multiple servers are relevant
|
||||
- **Performance Optimization**: Caching, batching, and lazy loading strategies
|
||||
|
||||
## Hook Integration
|
||||
|
||||
Dynamic patterns integrate with Framework-Hooks at these points:
|
||||
|
||||
- **pre_tool_use**: Analyze user input for mode and server activation
|
||||
- **session_start**: Apply initial context-based activations
|
||||
- **post_tool_use**: Update activation patterns based on results
|
||||
- **subagent_stop**: Re-evaluate activation patterns after sub-agent operations
|
||||
|
||||
## Creating Dynamic Patterns
|
||||
|
||||
To create new dynamic patterns:
|
||||
|
||||
1. **Define Triggers**: Identify the conditions that should activate the pattern
|
||||
2. **Set Keywords**: Define specific words or phrases that indicate activation
|
||||
3. **Choose Thresholds**: Set confidence thresholds appropriate for the operation's resource cost
|
||||
4. **Specify Coordination**: Define how the pattern works with other systems
|
||||
5. **Add Performance Rules**: Configure caching and optimization strategies
|
||||
|
||||
Dynamic patterns provide flexible, context-aware activation of Framework-Hooks features without requiring code changes.
|
||||
|
||||
@@ -1,185 +0,0 @@
|
||||
# Learned Patterns: Adaptive Behavior Learning
|
||||
|
||||
## Overview
|
||||
|
||||
Learned patterns store adaptive behaviors that evolve based on project usage and user preferences. These patterns are stored in `/patterns/learned/` and track effectiveness, optimizations, and personalization data to improve Framework-Hooks behavior over time.
|
||||
|
||||
## Purpose
|
||||
|
||||
Learned patterns handle:
|
||||
|
||||
- **Project Optimizations**: Track effective workflows and performance improvements for specific projects
|
||||
- **User Preferences**: Learn individual user behavior patterns and communication styles
|
||||
- **Performance Metrics**: Monitor effectiveness of different MCP servers and coordination strategies
|
||||
- **Error Prevention**: Learn from past issues to prevent recurring problems
|
||||
|
||||
## Current Learned Patterns
|
||||
|
||||
### User Preferences Pattern (`user_preferences.yaml`)
|
||||
|
||||
This pattern tracks individual user behavior and preferences:
|
||||
|
||||
```yaml
|
||||
user_profile:
|
||||
id: "example_user"
|
||||
created: "2025-01-31"
|
||||
last_updated: "2025-01-31"
|
||||
sessions_analyzed: 0
|
||||
|
||||
learned_preferences:
|
||||
communication_style:
|
||||
verbosity_preference: "balanced" # minimal, balanced, detailed
|
||||
technical_depth: "high" # low, medium, high
|
||||
symbol_usage_comfort: "high" # low, medium, high
|
||||
abbreviation_tolerance: "medium" # low, medium, high
|
||||
|
||||
workflow_patterns:
|
||||
preferred_thinking_mode: "--think-hard"
|
||||
mcp_server_preferences:
|
||||
- "serena" # Most frequently beneficial
|
||||
- "sequential" # High success rate
|
||||
- "context7" # Frequently requested
|
||||
mode_activation_frequency:
|
||||
task_management: 0.8 # High usage
|
||||
token_efficiency: 0.6 # Medium usage
|
||||
brainstorming: 0.3 # Low usage
|
||||
introspection: 0.4 # Medium usage
|
||||
|
||||
project_type_expertise:
|
||||
python: 0.9 # High proficiency
|
||||
react: 0.7 # Good proficiency
|
||||
javascript: 0.8 # High proficiency
|
||||
documentation: 0.6 # Medium proficiency
|
||||
|
||||
performance_preferences:
|
||||
speed_vs_quality: "quality_focused" # speed_focused, balanced, quality_focused
|
||||
compression_tolerance: 0.7 # How much compression user accepts
|
||||
context_size_preference: "medium" # small, medium, large
|
||||
|
||||
learning_insights:
|
||||
effective_patterns:
|
||||
- pattern: "serena + morphllm hybrid"
|
||||
success_rate: 0.92
|
||||
context: "large refactoring tasks"
|
||||
|
||||
- pattern: "sequential + context7"
|
||||
success_rate: 0.88
|
||||
context: "complex debugging"
|
||||
|
||||
- pattern: "magic + context7"
|
||||
success_rate: 0.85
|
||||
context: "UI component creation"
|
||||
|
||||
adaptive_thresholds:
|
||||
mode_activation:
|
||||
brainstorming: 0.6 # Lowered from 0.7 due to user preference
|
||||
task_management: 0.9 # Raised from 0.8 due to frequent use
|
||||
token_efficiency: 0.65 # Adjusted based on tolerance
|
||||
introspection: 0.5 # Lowered due to user comfort with meta-analysis
|
||||
### Project Optimizations Pattern (`project_optimizations.yaml`)
|
||||
|
||||
This pattern tracks project-specific performance and optimization data:
|
||||
|
||||
```yaml
|
||||
project_profile:
|
||||
id: "superclaude_framework"
|
||||
type: "python_framework"
|
||||
created: "2025-01-31"
|
||||
last_analyzed: "2025-01-31"
|
||||
optimization_cycles: 0
|
||||
|
||||
learned_optimizations:
|
||||
file_patterns:
|
||||
high_frequency_files:
|
||||
patterns:
|
||||
- "commands/*.md"
|
||||
- "Core/*.md"
|
||||
- "Modes/*.md"
|
||||
- "MCP/*.md"
|
||||
frequency_weight: 0.9
|
||||
cache_priority: "high"
|
||||
|
||||
structural_patterns:
|
||||
patterns:
|
||||
- "markdown documentation with YAML frontmatter"
|
||||
- "python scripts with comprehensive docstrings"
|
||||
- "modular architecture with clear separation"
|
||||
optimization: "maintain full context for these patterns"
|
||||
|
||||
workflow_optimizations:
|
||||
effective_sequences:
|
||||
- sequence: ["Read", "Edit", "Validate"]
|
||||
success_rate: 0.95
|
||||
context: "documentation updates"
|
||||
|
||||
- sequence: ["Glob", "Read", "MultiEdit"]
|
||||
success_rate: 0.88
|
||||
context: "multi-file refactoring"
|
||||
|
||||
- sequence: ["Serena analyze", "Morphllm execute"]
|
||||
success_rate: 0.92
|
||||
context: "large codebase changes"
|
||||
|
||||
mcp_server_effectiveness:
|
||||
serena:
|
||||
effectiveness: 0.9
|
||||
optimal_contexts:
|
||||
- "framework documentation analysis"
|
||||
- "cross-file relationship mapping"
|
||||
- "memory-driven development"
|
||||
performance_notes: "excellent for project context"
|
||||
|
||||
sequential:
|
||||
effectiveness: 0.85
|
||||
optimal_contexts:
|
||||
- "complex architectural decisions"
|
||||
- "multi-step problem solving"
|
||||
- "systematic analysis"
|
||||
performance_notes: "valuable for thinking-intensive tasks"
|
||||
|
||||
morphllm:
|
||||
effectiveness: 0.8
|
||||
optimal_contexts:
|
||||
- "pattern-based editing"
|
||||
- "documentation updates"
|
||||
- "style consistency"
|
||||
performance_notes: "efficient for text transformations"
|
||||
|
||||
performance_insights:
|
||||
bottleneck_identification:
|
||||
- area: "large markdown file processing"
|
||||
impact: "medium"
|
||||
optimization: "selective reading with targeted edits"
|
||||
|
||||
- area: "cross-file reference validation"
|
||||
impact: "low"
|
||||
optimization: "cached reference mapping"
|
||||
|
||||
acceleration_opportunities:
|
||||
- opportunity: "pattern-based file detection"
|
||||
potential_improvement: "40% faster file processing"
|
||||
implementation: "regex pre-filtering"
|
||||
|
||||
- opportunity: "intelligent caching"
|
||||
potential_improvement: "60% faster repeated operations"
|
||||
implementation: "content-aware cache keys"
|
||||
## Learning Process
|
||||
|
||||
Learned patterns evolve through:
|
||||
|
||||
1. **Data Collection**: Track user interactions, tool effectiveness, and performance metrics
|
||||
2. **Pattern Analysis**: Identify successful workflows and optimization opportunities
|
||||
3. **Threshold Adjustment**: Adapt confidence thresholds based on user behavior
|
||||
4. **Performance Tracking**: Monitor the effectiveness of different strategies
|
||||
5. **Cross-Session Persistence**: Maintain learning across multiple work sessions
|
||||
|
||||
## Integration Notes
|
||||
|
||||
Learned patterns integrate with Framework-Hooks through:
|
||||
|
||||
- **Adaptive Thresholds**: Modify activation thresholds based on learned preferences
|
||||
- **Server Selection**: Prioritize MCP servers based on measured effectiveness
|
||||
- **Workflow Optimization**: Apply learned effective sequences to new tasks
|
||||
- **Performance Monitoring**: Track and optimize based on measured performance
|
||||
|
||||
The learned patterns provide a feedback mechanism that allows Framework-Hooks to improve its behavior based on actual usage patterns and results.
|
||||
@@ -1,225 +0,0 @@
|
||||
# Minimal Patterns: Project Detection and Bootstrap
|
||||
|
||||
## Overview
|
||||
|
||||
Minimal patterns provide project type detection and initial Framework-Hooks configuration. These patterns are stored in `/patterns/minimal/` and automatically configure MCP server activation and auto-flags based on detected project characteristics.
|
||||
|
||||
## Purpose
|
||||
|
||||
Minimal patterns handle:
|
||||
|
||||
- **Project Detection**: Identify project type from file structure and dependencies
|
||||
- **MCP Server Selection**: Configure primary and secondary MCP servers
|
||||
- **Auto-Flag Configuration**: Set automatic flags for immediate activation
|
||||
- **Performance Targets**: Define bootstrap timing and context size goals
|
||||
|
||||
## Pattern Structure
|
||||
|
||||
All minimal patterns follow this YAML structure:
|
||||
|
||||
```yaml
|
||||
project_type: "string" # Unique project identifier
|
||||
detection_patterns: [] # File/directory detection rules
|
||||
auto_flags: [] # Automatic flag activation
|
||||
mcp_servers:
|
||||
primary: "string" # Primary MCP server
|
||||
secondary: [] # Fallback servers
|
||||
patterns:
|
||||
file_structure: [] # Expected project files/dirs
|
||||
common_tasks: [] # Typical operations
|
||||
intelligence:
|
||||
mode_triggers: [] # Mode activation conditions
|
||||
validation_focus: [] # Quality validation priorities
|
||||
performance_targets:
|
||||
bootstrap_ms: number # Bootstrap time target
|
||||
context_size: "string" # Context footprint target
|
||||
cache_duration: "string" # Cache retention time
|
||||
```
|
||||
|
||||
### Detection Rules
|
||||
|
||||
Detection patterns identify projects through:
|
||||
|
||||
- **File Extensions**: Look for specific file types (`.py`, `.jsx`, etc.)
|
||||
- **Dependency Files**: Check for `package.json`, `requirements.txt`, `pyproject.toml`
|
||||
- **Directory Structure**: Verify expected directories exist
|
||||
- **Configuration Files**: Detect framework-specific config files
|
||||
|
||||
## Current Minimal Patterns
|
||||
|
||||
### Python Project Pattern (`python_project.yaml`)
|
||||
|
||||
This is the actual pattern file for Python projects:
|
||||
|
||||
```yaml
|
||||
project_type: "python"
|
||||
detection_patterns:
|
||||
- "*.py files present"
|
||||
- "requirements.txt or pyproject.toml"
|
||||
- "__pycache__/ directories"
|
||||
|
||||
auto_flags:
|
||||
- "--serena" # Semantic analysis
|
||||
- "--context7" # Python documentation
|
||||
|
||||
mcp_servers:
|
||||
primary: "serena"
|
||||
secondary: ["context7", "sequential", "morphllm"]
|
||||
|
||||
patterns:
|
||||
file_structure:
|
||||
- "src/ or lib/"
|
||||
- "tests/"
|
||||
- "docs/"
|
||||
- "requirements.txt"
|
||||
|
||||
common_tasks:
|
||||
- "function refactoring"
|
||||
- "class extraction"
|
||||
- "import optimization"
|
||||
- "testing setup"
|
||||
|
||||
intelligence:
|
||||
mode_triggers:
|
||||
- "token_efficiency: context >75%"
|
||||
- "task_management: refactor|test|analyze"
|
||||
|
||||
validation_focus:
|
||||
- "python_syntax"
|
||||
- "pep8_compliance"
|
||||
- "type_hints"
|
||||
- "testing_coverage"
|
||||
|
||||
performance_targets:
|
||||
bootstrap_ms: 40
|
||||
context_size: "4KB"
|
||||
cache_duration: "45min"
|
||||
```
|
||||
|
||||
This pattern automatically activates Serena (for semantic analysis) and Context7 (for Python documentation) when Python projects are detected.
|
||||
|
||||
### React Project Pattern (`react_project.yaml`)
|
||||
|
||||
```yaml
|
||||
project_type: "react"
|
||||
detection_patterns:
|
||||
- "package.json with react dependency"
|
||||
- "src/ directory with .jsx/.tsx files"
|
||||
- "public/index.html"
|
||||
|
||||
auto_flags:
|
||||
- "--magic" # UI component generation
|
||||
- "--context7" # React documentation
|
||||
|
||||
mcp_servers:
|
||||
primary: "magic"
|
||||
secondary: ["context7", "morphllm"]
|
||||
|
||||
patterns:
|
||||
file_structure:
|
||||
- "src/components/"
|
||||
- "src/hooks/"
|
||||
- "src/pages/"
|
||||
- "src/utils/"
|
||||
|
||||
common_tasks:
|
||||
- "component creation"
|
||||
- "state management"
|
||||
- "routing setup"
|
||||
- "performance optimization"
|
||||
|
||||
intelligence:
|
||||
mode_triggers:
|
||||
- "token_efficiency: context >75%"
|
||||
- "task_management: build|implement|create"
|
||||
|
||||
validation_focus:
|
||||
- "jsx_syntax"
|
||||
- "react_patterns"
|
||||
- "accessibility"
|
||||
- "performance"
|
||||
|
||||
performance_targets:
|
||||
bootstrap_ms: 30
|
||||
context_size: "3KB"
|
||||
cache_duration: "60min"
|
||||
```
|
||||
|
||||
This pattern activates Magic (for UI component generation) and Context7 (for React documentation) when React projects are detected.
|
||||
|
||||
## Creating New Minimal Patterns
|
||||
|
||||
### Pattern Creation Process
|
||||
|
||||
1. **Identify Project Type**: Determine unique characteristics of the project type
|
||||
2. **Define Detection Rules**: Create file/directory patterns for identification
|
||||
3. **Select MCP Servers**: Choose primary and secondary servers for the project type
|
||||
4. **Configure Auto-Flags**: Set flags that should activate automatically
|
||||
5. **Define Intelligence**: Specify mode triggers and validation focus
|
||||
6. **Set Performance Targets**: Define bootstrap time and context size goals
|
||||
|
||||
### Pattern Template
|
||||
|
||||
```yaml
|
||||
project_type: "your_project_type"
|
||||
detection_patterns:
|
||||
- "unique file or directory patterns"
|
||||
- "dependency or configuration files"
|
||||
- "framework-specific indicators"
|
||||
|
||||
auto_flags:
|
||||
- "--primary_server"
|
||||
- "--supporting_server"
|
||||
|
||||
mcp_servers:
|
||||
primary: "most_relevant_server"
|
||||
secondary: ["fallback", "servers"]
|
||||
|
||||
patterns:
|
||||
file_structure:
|
||||
- "expected/directories/"
|
||||
- "important files"
|
||||
common_tasks:
|
||||
- "typical operations"
|
||||
- "common workflows"
|
||||
|
||||
intelligence:
|
||||
mode_triggers:
|
||||
- "mode_name: trigger_conditions"
|
||||
validation_focus:
|
||||
- "syntax_validation"
|
||||
- "best_practices"
|
||||
- "quality_checks"
|
||||
|
||||
performance_targets:
|
||||
bootstrap_ms: target_milliseconds
|
||||
context_size: "target_size"
|
||||
cache_duration: "cache_time"
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Detection Pattern Guidelines
|
||||
|
||||
1. **Use Specific Identifiers**: Look for unique files or dependency patterns
|
||||
2. **Multiple Signals**: Combine file extensions, directories, and config files
|
||||
3. **Avoid Generic Patterns**: Don't rely on common files like `README.md`
|
||||
4. **Test Edge Cases**: Handle missing files or permission errors gracefully
|
||||
|
||||
### MCP Server Selection
|
||||
|
||||
1. **Primary Server**: Choose the most relevant MCP server for the project type
|
||||
2. **Secondary Servers**: Add complementary servers as fallbacks
|
||||
3. **Auto-Flags**: Set flags that provide immediate value for the project type
|
||||
4. **Performance Targets**: Set realistic bootstrap and context size goals
|
||||
|
||||
## Integration Notes
|
||||
|
||||
Minimal patterns integrate with Framework-Hooks through:
|
||||
|
||||
- **session_start hook**: Loads and applies patterns during initialization
|
||||
- **Project detection**: Scans files and directories to identify project type
|
||||
- **MCP activation**: Automatically starts relevant MCP servers
|
||||
- **Flag processing**: Sets auto-flags for immediate feature activation
|
||||
|
||||
The pattern system provides a declarative way to configure Framework-Hooks behavior for different project types without requiring code changes.
|
||||
@@ -1,227 +0,0 @@
|
||||
# SuperClaude Pattern System Overview
|
||||
|
||||
## Overview
|
||||
|
||||
The SuperClaude Pattern System provides a three-tier architecture for project detection, mode activation, and adaptive learning within the Framework-Hooks system. The system uses YAML-based patterns to configure automatic behavior, MCP server activation, and performance optimization.
|
||||
|
||||
## System Architecture
|
||||
|
||||
### Core Structure
|
||||
|
||||
The pattern system consists of three directories with distinct purposes:
|
||||
|
||||
```
|
||||
patterns/
|
||||
├── minimal/ # Project detection and bootstrap configuration
|
||||
├── dynamic/ # Mode detection and MCP server activation
|
||||
└── learned/ # Project-specific adaptations and user preferences
|
||||
```
|
||||
|
||||
### Pattern Types
|
||||
|
||||
**Minimal Patterns**: Project type detection and initial MCP server selection
|
||||
- File detection patterns for project types (Python, React, etc.)
|
||||
- Auto-flag configuration for immediate MCP server activation
|
||||
- Basic project structure recognition
|
||||
|
||||
**Dynamic Patterns**: Runtime activation based on context analysis
|
||||
- Mode detection patterns (brainstorming, task management, etc.)
|
||||
- MCP server activation based on user requests
|
||||
- Cross-mode coordination rules
|
||||
|
||||
**Learned Patterns**: Adaptation based on usage patterns
|
||||
- Project-specific optimizations that evolve over time
|
||||
- User preference learning and adaptation
|
||||
- Performance metrics and effectiveness tracking
|
||||
|
||||
## Pattern Structure
|
||||
|
||||
### 1. Minimal Patterns
|
||||
**Purpose**: Project detection and bootstrap configuration
|
||||
- **Location**: `/patterns/minimal/`
|
||||
- **Files**: `python_project.yaml`, `react_project.yaml`
|
||||
- **Content**: Detection patterns, auto-flags, MCP server configuration
|
||||
|
||||
### 2. Dynamic Patterns
|
||||
**Purpose**: Runtime mode detection and MCP server activation
|
||||
- **Location**: `/patterns/dynamic/`
|
||||
- **Files**: `mcp_activation.yaml`, `mode_detection.yaml`
|
||||
- **Content**: Activation patterns, confidence thresholds, coordination rules
|
||||
|
||||
### 3. Learned Patterns
|
||||
**Purpose**: Adaptive behavior based on usage patterns
|
||||
- **Location**: `/patterns/learned/`
|
||||
- **Files**: `project_optimizations.yaml`, `user_preferences.yaml`
|
||||
- **Content**: Performance metrics, user preferences, optimization tracking
|
||||
|
||||
## Pattern Schema
|
||||
|
||||
### Minimal Pattern Structure
|
||||
|
||||
Based on actual files like `python_project.yaml`:
|
||||
|
||||
```yaml
|
||||
project_type: "python"
|
||||
detection_patterns:
|
||||
- "*.py files present"
|
||||
- "requirements.txt or pyproject.toml"
|
||||
- "__pycache__/ directories"
|
||||
|
||||
auto_flags:
|
||||
- "--serena" # Semantic analysis
|
||||
- "--context7" # Python documentation
|
||||
|
||||
mcp_servers:
|
||||
primary: "serena"
|
||||
secondary: ["context7", "sequential", "morphllm"]
|
||||
|
||||
patterns:
|
||||
file_structure:
|
||||
- "src/ or lib/"
|
||||
- "tests/"
|
||||
- "docs/"
|
||||
- "requirements.txt"
|
||||
common_tasks:
|
||||
- "function refactoring"
|
||||
- "class extraction"
|
||||
- "import optimization"
|
||||
- "testing setup"
|
||||
|
||||
intelligence:
|
||||
mode_triggers:
|
||||
- "token_efficiency: context >75%"
|
||||
- "task_management: refactor|test|analyze"
|
||||
validation_focus:
|
||||
- "python_syntax"
|
||||
- "pep8_compliance"
|
||||
- "type_hints"
|
||||
- "testing_coverage"
|
||||
|
||||
performance_targets:
|
||||
bootstrap_ms: 40
|
||||
context_size: "4KB"
|
||||
cache_duration: "45min"
|
||||
```
|
||||
|
||||
### Dynamic Pattern Structure
|
||||
|
||||
Based on `mcp_activation.yaml`:
|
||||
|
||||
```yaml
|
||||
activation_patterns:
|
||||
context7:
|
||||
triggers:
|
||||
- "import statements from external libraries"
|
||||
- "framework-specific questions"
|
||||
- "documentation requests"
|
||||
context_keywords:
|
||||
- "documentation"
|
||||
- "examples"
|
||||
- "patterns"
|
||||
activation_confidence: 0.8
|
||||
|
||||
coordination_patterns:
|
||||
hybrid_intelligence:
|
||||
serena_morphllm:
|
||||
condition: "complex editing with semantic understanding"
|
||||
strategy: "serena analyzes, morphllm executes"
|
||||
confidence_threshold: 0.8
|
||||
|
||||
performance_optimization:
|
||||
cache_activation_decisions: true
|
||||
cache_duration_minutes: 15
|
||||
batch_similar_requests: true
|
||||
lazy_loading: true
|
||||
```
|
||||
|
||||
## Hook Integration
|
||||
|
||||
### Hook Points
|
||||
|
||||
The pattern system integrates with Framework-Hooks at these points:
|
||||
|
||||
**session_start**: Load minimal patterns for project detection
|
||||
**pre_tool_use**: Apply dynamic patterns for mode detection
|
||||
**post_tool_use**: Update learned patterns with usage data
|
||||
**stop**: Persist learned optimizations and preferences
|
||||
|
||||
### MCP Server Activation
|
||||
|
||||
Patterns control MCP server activation through:
|
||||
|
||||
1. **Auto-flags**: Immediate activation based on project type
|
||||
2. **Dynamic activation**: Context-based activation during operation
|
||||
3. **Coordination patterns**: Rules for multi-server interactions
|
||||
|
||||
### Mode Detection
|
||||
|
||||
Mode activation is controlled by patterns in `mode_detection.yaml`:
|
||||
|
||||
- **Brainstorming**: Triggered by vague project requests, exploration keywords
|
||||
- **Task Management**: Multi-step operations, system-wide scope
|
||||
- **Token Efficiency**: Context usage >75%, resource constraints
|
||||
- **Introspection**: Self-analysis requests, framework discussions
|
||||
|
||||
## Current Pattern Files
|
||||
|
||||
### Minimal Patterns
|
||||
|
||||
**python_project.yaml** (45 lines):
|
||||
- Detects Python projects by `.py` files, `requirements.txt`, `pyproject.toml`
|
||||
- Auto-activates `--serena` and `--context7` flags
|
||||
- Targets 40ms bootstrap, 4KB context size
|
||||
- Primary server: serena, with context7/sequential/morphllm fallback
|
||||
|
||||
**react_project.yaml**:
|
||||
- Detects React projects by `package.json` with react dependency
|
||||
- Auto-activates `--magic` and `--context7` flags
|
||||
- Targets 30ms bootstrap, 3KB context size
|
||||
- Primary server: magic, with context7/morphllm fallback
|
||||
|
||||
### Dynamic Patterns
|
||||
|
||||
**mcp_activation.yaml** (114 lines):
|
||||
- Defines activation patterns for all 6 MCP servers
|
||||
- Includes context keywords and confidence thresholds
|
||||
- Hybrid intelligence coordination (serena + morphllm)
|
||||
- Performance optimization settings (caching, lazy loading)
|
||||
|
||||
**mode_detection.yaml**:
|
||||
- Mode detection for brainstorming, task management, token efficiency, introspection
|
||||
- Confidence thresholds from 0.6-0.8 depending on mode
|
||||
- Cross-mode coordination and transition rules
|
||||
- Adaptive learning configuration
|
||||
|
||||
### Learned Patterns
|
||||
|
||||
**project_optimizations.yaml**:
|
||||
- Project-specific learning for SuperClaude framework
|
||||
- File pattern analysis and workflow optimization tracking
|
||||
- MCP server effectiveness measurements
|
||||
- Performance bottleneck identification and solutions
|
||||
|
||||
**user_preferences.yaml**:
|
||||
- User behavior adaptation patterns
|
||||
- Communication style preferences
|
||||
- Workflow pattern effectiveness tracking
|
||||
- Personalized thresholds and server preferences
|
||||
|
||||
## Usage
|
||||
|
||||
### Creating New Patterns
|
||||
|
||||
1. **Minimal Patterns**: Create project detection patterns in `/patterns/minimal/`
|
||||
2. **Dynamic Patterns**: Define activation rules in `/patterns/dynamic/`
|
||||
3. **Learned Patterns**: Configure adaptation tracking in `/patterns/learned/`
|
||||
|
||||
### Pattern Development
|
||||
|
||||
Patterns are YAML files that follow specific schema formats. They control:
|
||||
|
||||
- Project type detection based on file patterns
|
||||
- Automatic MCP server activation
|
||||
- Mode detection and activation thresholds
|
||||
- Performance optimization preferences
|
||||
- User behavior adaptation
|
||||
|
||||
The pattern system provides a declarative way to configure Framework-Hooks behavior without modifying code, enabling customization and optimization based on project types and usage patterns.
|
||||
@@ -1,231 +0,0 @@
|
||||
# Framework-Hooks Performance Documentation
|
||||
|
||||
## Performance Targets
|
||||
|
||||
The Framework-Hooks system defines performance targets in performance.yaml for each hook:
|
||||
|
||||
**Core Performance Requirements:**
|
||||
|
||||
- Hook execution should complete within configured timeouts to avoid blocking Claude Code
|
||||
- Performance targets provide goals for optimization but do not guarantee actual execution times
|
||||
- Resource usage should remain reasonable during normal operation
|
||||
- Performance monitoring tracks actual execution against configured targets
|
||||
|
||||
**Performance Target Rationale:**
|
||||
```
|
||||
Fast Hook Performance → Reduced Session Latency → Better User Experience
|
||||
```
|
||||
|
||||
**Configured Thresholds:**
|
||||
- **Target times**: Optimal performance goals from performance.yaml
|
||||
- **Warning thresholds**: ~1.5x target (monitoring threshold)
|
||||
- **Critical thresholds**: ~2x target (performance alert threshold)
|
||||
- **Timeout limits**: Configured in settings.json (10-15 seconds per hook)
|
||||
|
||||
## Hook Performance Targets
|
||||
|
||||
### Performance Targets from performance.yaml
|
||||
|
||||
The system defines performance targets for each lifecycle hook:
|
||||
|
||||
#### session_start: 50ms target
|
||||
**Current Implementation**: 703 lines of Python code
|
||||
**Timeout**: 10 seconds (from settings.json)
|
||||
**Thresholds**: 50ms target, 75ms warning, 100ms critical
|
||||
**Primary Tasks**:
|
||||
- Project type detection (file analysis)
|
||||
- Pattern loading from minimal/ directory
|
||||
- Mode activation based on user input
|
||||
- MCP server routing decisions
|
||||
|
||||
#### pre_tool_use: 200ms target
|
||||
**Timeout**: 15 seconds
|
||||
**Thresholds**: 200ms target, 300ms warning, 500ms critical
|
||||
**Primary Tasks**:
|
||||
- Operation pattern analysis
|
||||
- MCP server selection logic
|
||||
- Performance optimization planning
|
||||
|
||||
#### post_tool_use: 100ms target
|
||||
**Timeout**: 10 seconds
|
||||
**Thresholds**: 100ms target, 150ms warning, 250ms critical
|
||||
**Primary Tasks**:
|
||||
- Operation result validation
|
||||
- Learning data recording
|
||||
- Effectiveness tracking
|
||||
|
||||
#### pre_compact: 150ms target
|
||||
**Timeout**: 15 seconds
|
||||
**Thresholds**: 150ms target, 200ms warning, 300ms critical
|
||||
**Primary Tasks**:
|
||||
- Content type classification
|
||||
- Compression strategy selection
|
||||
- Token optimization application
|
||||
|
||||
#### notification: 100ms target
|
||||
**Timeout**: 10 seconds
|
||||
**Thresholds**: 100ms target, 150ms warning, 200ms critical
|
||||
**Primary Tasks**:
|
||||
- Pattern cache updates
|
||||
- Configuration refreshes
|
||||
- Notification processing
|
||||
|
||||
#### stop: 200ms target
|
||||
**Timeout**: 15 seconds
|
||||
**Thresholds**: 200ms target, 300ms warning, 500ms critical
|
||||
**Primary Tasks**:
|
||||
- Session analytics generation
|
||||
- Learning data persistence
|
||||
- Performance metrics collection
|
||||
|
||||
#### subagent_stop: 150ms target
|
||||
**Timeout**: 15 seconds
|
||||
**Thresholds**: 150ms target, 200ms warning, 300ms critical
|
||||
**Primary Tasks**:
|
||||
- Delegation performance tracking
|
||||
- Coordination effectiveness measurement
|
||||
- Task management analytics
|
||||
|
||||
## Implementation Architecture
|
||||
|
||||
### Core Implementation Components
|
||||
|
||||
The Framework-Hooks system consists of:
|
||||
|
||||
**Python Modules:**
|
||||
- 7 main hook files (session_start.py: 703 lines, others vary)
|
||||
- 9 shared modules totaling ~250KB of Python code
|
||||
- pattern_detection.py (45KB) handles project type and pattern recognition
|
||||
- learning_engine.py (40KB) manages user preferences and effectiveness tracking
|
||||
|
||||
**Configuration System:**
|
||||
- 19 YAML configuration files
|
||||
- performance.yaml (345 lines) defines all timing targets and thresholds
|
||||
- Pattern files organized in minimal/, dynamic/, learned/ directories
|
||||
- settings.json configures hook execution and timeouts
|
||||
|
||||
**Pattern Loading Strategy:**
|
||||
```yaml
|
||||
Minimal Patterns (loaded at startup):
|
||||
- python_project.yaml: Python-specific configuration
|
||||
- react_project.yaml: React project patterns
|
||||
- Basic mode detection triggers
|
||||
|
||||
Dynamic Patterns (loaded as needed):
|
||||
- mcp_activation.yaml: Server routing patterns
|
||||
- mode_detection.yaml: SuperClaude mode triggers
|
||||
|
||||
Learned Patterns (updated during use):
|
||||
- user_preferences.yaml: Personal configuration adaptations
|
||||
- project_optimizations.yaml: Project-specific learned patterns
|
||||
```
|
||||
|
||||
### Compression Implementation
|
||||
|
||||
The pre_compact hook implements token compression through compression_engine.py (27KB):
|
||||
|
||||
**Compression Strategies:**
|
||||
```python
|
||||
Compression Levels (from compression.yaml):
|
||||
MINIMAL: Framework content excluded, user content preserved
|
||||
EFFICIENT: Selective compression with quality validation
|
||||
COMPRESSED: Symbol systems and abbreviations applied
|
||||
CRITICAL: Aggressive optimization when needed
|
||||
EMERGENCY: Maximum compression for resource constraints
|
||||
```
|
||||
|
||||
**Symbol Systems Implementation:**
|
||||
- Mathematical operators: 'leads to' → '→'
|
||||
- Status indicators: 'completed' → '✅'
|
||||
- Technical domains: 'performance' → '⚡'
|
||||
- Applied selectively based on content type and compression level
|
||||
|
||||
**Content Classification:**
|
||||
```python
|
||||
Compression Strategy by Content Type:
|
||||
FRAMEWORK_CONTENT: 0% compression (complete preservation)
|
||||
SESSION_DATA: Variable compression (based on compression level)
|
||||
USER_CONTENT: Minimal compression (quality preservation priority)
|
||||
WORKING_ARTIFACTS: Higher compression allowed (temporary data)
|
||||
```
|
||||
|
||||
## Session Startup Implementation
|
||||
|
||||
### session_start Hook Operation
|
||||
|
||||
The session_start.py hook (703 lines) implements session initialization:
|
||||
|
||||
**Primary Operations:**
|
||||
1. **Project Detection**: Analyzes file structure to determine project type (Python, React, etc.)
|
||||
2. **Pattern Loading**: Loads appropriate minimal pattern files based on detected project type
|
||||
3. **Mode Activation**: Parses user input to detect SuperClaude mode triggers
|
||||
4. **MCP Routing**: Determines which MCP servers to activate based on patterns and project type
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
Session Startup Process:
|
||||
1. Initialize shared modules (framework_logic, pattern_detection, etc.)
|
||||
2. Load YAML configurations (performance.yaml, modes.yaml, etc.)
|
||||
3. Analyze current directory for project type indicators
|
||||
4. Load appropriate minimal pattern files
|
||||
5. Process user input for mode detection
|
||||
6. Generate MCP server activation recommendations
|
||||
7. Record startup metrics for performance monitoring
|
||||
```
|
||||
|
||||
**Performance Considerations:**
|
||||
- Hook has 10-second timeout limit (configured in settings.json)
|
||||
- Target execution time: 50ms (from performance.yaml)
|
||||
- Actual performance depends on system resources and project complexity
|
||||
- Pattern loading optimized by only loading relevant minimal patterns initially
|
||||
|
||||
## Performance Monitoring Implementation
|
||||
|
||||
### Performance Tracking System
|
||||
|
||||
The Framework-Hooks system includes performance monitoring through:
|
||||
|
||||
**Performance Configuration:**
|
||||
- **performance.yaml** (345 lines): Defines targets, warning, and critical thresholds for each hook
|
||||
- **logger.py** (11KB): Provides logging utilities for performance tracking and debugging
|
||||
- **Hook timing**: Each hook execution is measured and compared against configured targets
|
||||
|
||||
**Monitoring Components:**
|
||||
```yaml
|
||||
Performance Tracking:
|
||||
Target Times: Defined in performance.yaml for each hook
|
||||
Warning Thresholds: ~1.5x target time (monitoring alerts)
|
||||
Critical Thresholds: ~2x target time (performance degradation alerts)
|
||||
Timeout Limits: Configured in settings.json (10-15 seconds per hook)
|
||||
```
|
||||
|
||||
**Learning Integration:**
|
||||
- **learning_engine.py** (40KB): Tracks operation effectiveness and user preferences
|
||||
- **Learned patterns**: Performance data influences future pattern selection
|
||||
- **User preferences**: Successful configurations saved for future sessions
|
||||
- **Analytics**: stop.py hook generates session performance summaries
|
||||
|
||||
## Implementation Summary
|
||||
|
||||
### Framework-Hooks Performance Characteristics
|
||||
|
||||
The Framework-Hooks system implements performance monitoring and optimization through:
|
||||
|
||||
**Performance Configuration:**
|
||||
- Performance targets defined in performance.yaml for each of 7 lifecycle hooks
|
||||
- Timeout limits configured in settings.json (10-15 seconds per hook)
|
||||
- Warning and critical thresholds for performance degradation detection
|
||||
|
||||
**Implementation Approach:**
|
||||
- Python hooks execute during Claude Code lifecycle events
|
||||
- Shared modules provide common functionality (pattern detection, learning, compression)
|
||||
- YAML configuration files customize behavior without code changes
|
||||
- Pattern system enables project-specific optimizations
|
||||
|
||||
**Performance Considerations:**
|
||||
- Hook performance depends on system resources, project complexity, and configuration
|
||||
- Targets provide optimization goals but do not guarantee actual execution times
|
||||
- Learning system adapts behavior based on measured effectiveness
|
||||
- Compression system balances token efficiency with content quality preservation
|
||||
|
||||
The system aims to provide SuperClaude framework functionality through efficient lifecycle hooks while maintaining reasonable resource usage and execution times.
|
||||
@@ -1,241 +0,0 @@
|
||||
# Quick Reference
|
||||
|
||||
Essential commands and information for Framework-Hooks developers and users.
|
||||
|
||||
## System Overview
|
||||
|
||||
- **7 hooks**: Execute at specific Claude Code lifecycle events
|
||||
- **9 shared modules**: Common functionality across hooks
|
||||
- **12+ config files**: YAML-based configuration system
|
||||
- **3-tier patterns**: minimal/dynamic/learned pattern system
|
||||
- **Performance targets**: <50ms to <200ms per hook
|
||||
|
||||
## Installation Quick Check
|
||||
|
||||
```bash
|
||||
# Verify Python version
|
||||
python3 --version # Need 3.8+
|
||||
|
||||
# Check directory structure
|
||||
ls Framework-Hooks/
|
||||
# Should see: hooks/ config/ patterns/ cache/ docs/
|
||||
|
||||
# Test hook execution
|
||||
python3 Framework-Hooks/hooks/session_start.py
|
||||
|
||||
# Validate system
|
||||
cd Framework-Hooks/hooks/shared
|
||||
python3 validate_system.py --check-installation
|
||||
```
|
||||
|
||||
## Configuration Files
|
||||
|
||||
| File | Purpose | Key Settings |
|
||||
|------|---------|-------------|
|
||||
| `logging.yaml` | System logging | `enabled: false` (default) |
|
||||
| `performance.yaml` | Timing targets | session_start: 50ms, pre_tool_use: 200ms |
|
||||
| `session.yaml` | Session lifecycle | Context management, cleanup behavior |
|
||||
| `compression.yaml` | Content compression | Selective compression rules |
|
||||
| `mcp_orchestration.yaml` | MCP server routing | Server activation patterns |
|
||||
|
||||
## Performance Targets
|
||||
|
||||
| Hook | Target Time | Timeout |
|
||||
|------|-------------|---------|
|
||||
| session_start.py | <50ms | 10s |
|
||||
| pre_tool_use.py | <200ms | 15s |
|
||||
| post_tool_use.py | <100ms | 10s |
|
||||
| pre_compact.py | <150ms | 15s |
|
||||
| notification.py | <50ms | 10s |
|
||||
| stop.py | <100ms | 15s |
|
||||
| subagent_stop.py | <100ms | 15s |
|
||||
|
||||
## Common Operations
|
||||
|
||||
### Enable Logging
|
||||
```yaml
|
||||
# Edit config/logging.yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: "INFO" # or DEBUG
|
||||
```
|
||||
|
||||
### Check System Health
|
||||
```bash
|
||||
cd Framework-Hooks/hooks/shared
|
||||
python3 validate_system.py --health-check
|
||||
```
|
||||
|
||||
### Test Individual Hook
|
||||
```bash
|
||||
cd Framework-Hooks/hooks
|
||||
python3 session_start.py # Test session initialization
|
||||
python3 pre_tool_use.py # Test tool preparation
|
||||
```
|
||||
|
||||
### Clear Cache
|
||||
```bash
|
||||
# Reset learning data and cache
|
||||
rm -rf Framework-Hooks/cache/*
|
||||
# System will recreate on next run
|
||||
```
|
||||
|
||||
### View Recent Logs
|
||||
```bash
|
||||
# Check latest logs (if logging enabled)
|
||||
tail -f Framework-Hooks/cache/logs/hooks-$(date +%Y-%m-%d).log
|
||||
```
|
||||
|
||||
## Hook Execution Flow
|
||||
|
||||
```
|
||||
Session Start → Load Config → Detect Project → Apply Patterns →
|
||||
Activate Features → [Work Session with Tool Use Hooks] →
|
||||
Record Learning → Save State → Session End
|
||||
```
|
||||
|
||||
## Directory Structure
|
||||
|
||||
```
|
||||
Framework-Hooks/
|
||||
├── hooks/ # 7 hook scripts
|
||||
│ ├── session_start.py # <50ms - Session init
|
||||
│ ├── pre_tool_use.py # <200ms - Tool prep
|
||||
│ ├── post_tool_use.py # <100ms - Usage recording
|
||||
│ ├── pre_compact.py # <150ms - Context compression
|
||||
│ ├── notification.py # <50ms - Notifications
|
||||
│ ├── stop.py # <100ms - Session cleanup
|
||||
│ ├── subagent_stop.py # <100ms - Subagent coordination
|
||||
│ └── shared/ # 9 shared modules
|
||||
├── config/ # 12+ YAML config files
|
||||
├── patterns/ # 3-tier pattern system
|
||||
│ ├── minimal/ # Always loaded (3-5KB each)
|
||||
│ ├── dynamic/ # On-demand (8-12KB each)
|
||||
│ └── learned/ # User adaptations (10-20KB each)
|
||||
├── cache/ # Runtime cache and logs
|
||||
└── docs/ # Documentation
|
||||
```
|
||||
|
||||
## Shared Modules
|
||||
|
||||
| Module | Purpose |
|
||||
|--------|---------|
|
||||
| `framework_logic.py` | SuperClaude framework integration |
|
||||
| `compression_engine.py` | Context compression and optimization |
|
||||
| `learning_engine.py` | Adaptive learning from usage |
|
||||
| `mcp_intelligence.py` | MCP server coordination |
|
||||
| `pattern_detection.py` | Project and usage pattern detection |
|
||||
| `intelligence_engine.py` | Central intelligence coordination |
|
||||
| `logger.py` | Structured logging system |
|
||||
| `yaml_loader.py` | Configuration loading utilities |
|
||||
| `validate_system.py` | System validation and health checks |
|
||||
|
||||
## Troubleshooting Quick Fixes
|
||||
|
||||
### Hook Timeout
|
||||
- Check performance targets in `config/performance.yaml`
|
||||
- Clear cache: `rm -rf cache/*`
|
||||
- Reduce pattern loading
|
||||
|
||||
### Import Errors
|
||||
```bash
|
||||
# Verify shared modules path
|
||||
ls Framework-Hooks/hooks/shared/
|
||||
# Should show all 9 .py files
|
||||
|
||||
# Check permissions
|
||||
chmod +x Framework-Hooks/hooks/*.py
|
||||
```
|
||||
|
||||
### YAML Errors
|
||||
```bash
|
||||
# Validate YAML files
|
||||
python3 -c "
|
||||
import yaml, glob
|
||||
for f in glob.glob('config/*.yaml'):
|
||||
yaml.safe_load(open(f))
|
||||
print(f'{f}: OK')
|
||||
"
|
||||
```
|
||||
|
||||
### No Log Output
|
||||
```yaml
|
||||
# Enable in config/logging.yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: "INFO"
|
||||
hook_logging:
|
||||
log_lifecycle: true
|
||||
```
|
||||
|
||||
## Configuration Shortcuts
|
||||
|
||||
### Development Mode
|
||||
```yaml
|
||||
# config/logging.yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: "DEBUG"
|
||||
development:
|
||||
debug_mode: true
|
||||
verbose_errors: true
|
||||
```
|
||||
|
||||
### Production Mode
|
||||
```yaml
|
||||
# config/logging.yaml
|
||||
logging:
|
||||
enabled: false
|
||||
level: "ERROR"
|
||||
```
|
||||
|
||||
### Reset to Defaults
|
||||
```bash
|
||||
# Backup first, then:
|
||||
git checkout config/*.yaml
|
||||
rm -rf cache/
|
||||
rm -rf patterns/learned/
|
||||
```
|
||||
|
||||
## File Locations
|
||||
|
||||
- **Hook scripts**: `hooks/*.py`
|
||||
- **Configuration**: `config/*.yaml`
|
||||
- **Logs**: `cache/logs/hooks-YYYY-MM-DD.log`
|
||||
- **Learning data**: `cache/learning/`
|
||||
- **Pattern cache**: `cache/patterns/`
|
||||
- **Installation config**: `settings.json`, `superclaude-config.json`
|
||||
|
||||
## Debug Commands
|
||||
|
||||
```bash
|
||||
# Full system validation
|
||||
python3 hooks/shared/validate_system.py --full-check
|
||||
|
||||
# Check configuration integrity
|
||||
python3 hooks/shared/validate_system.py --check-config
|
||||
|
||||
# Test pattern loading
|
||||
python3 hooks/shared/pattern_detection.py --test-patterns
|
||||
|
||||
# Verify learning engine
|
||||
python3 hooks/shared/learning_engine.py --test-learning
|
||||
```
|
||||
|
||||
## System Behavior
|
||||
|
||||
### Default State
|
||||
- All hooks **enabled** via settings.json
|
||||
- Logging **disabled** for performance
|
||||
- Conservative timeouts (10-15 seconds)
|
||||
- Selective compression preserves user content
|
||||
- Learning engine adapts to usage patterns
|
||||
|
||||
### What Happens Automatically
|
||||
1. **Project detection** - Identifies project type and loads patterns
|
||||
2. **Mode activation** - Enables relevant SuperClaude modes
|
||||
3. **MCP coordination** - Routes to appropriate servers
|
||||
4. **Performance optimization** - Applies compression and caching
|
||||
5. **Learning adaptation** - Records and learns from usage
|
||||
|
||||
Framework-Hooks operates transparently without requiring manual intervention.
|
||||
@@ -1,124 +0,0 @@
|
||||
# Framework-Hooks
|
||||
|
||||
Framework-Hooks is a hook system for Claude Code that provides intelligent session management and context adaptation. It runs Python hooks at different points in Claude Code's lifecycle to optimize performance and adapt behavior based on usage patterns.
|
||||
|
||||
## What it does
|
||||
|
||||
The system runs 7 hooks that execute at specific lifecycle events:
|
||||
|
||||
- `session_start.py` - Initializes session context and activates appropriate features
|
||||
- `pre_tool_use.py` - Prepares for tool execution and applies optimizations
|
||||
- `post_tool_use.py` - Records tool usage patterns and updates learning data
|
||||
- `pre_compact.py` - Applies compression before context compaction
|
||||
- `notification.py` - Handles system notifications and adaptive responses
|
||||
- `stop.py` - Performs cleanup and saves session data at shutdown
|
||||
- `subagent_stop.py` - Manages subagent cleanup and coordination
|
||||
|
||||
## Components
|
||||
|
||||
### Hooks
|
||||
Each hook is a Python script that runs at a specific lifecycle point. Hooks share common functionality through shared modules and can access configuration through YAML files.
|
||||
|
||||
### Shared Modules
|
||||
- `framework_logic.py` - Core logic for SuperClaude framework integration
|
||||
- `compression_engine.py` - Context compression and optimization
|
||||
- `learning_engine.py` - Adaptive learning from usage patterns
|
||||
- `mcp_intelligence.py` - MCP server coordination and routing
|
||||
- `pattern_detection.py` - Project and usage pattern detection
|
||||
- `logger.py` - Structured logging for hook operations
|
||||
- `yaml_loader.py` - Configuration loading utilities
|
||||
- `validate_system.py` - System validation and health checks
|
||||
|
||||
### Configuration
|
||||
12 YAML configuration files control different aspects:
|
||||
- `session.yaml` - Session lifecycle settings
|
||||
- `performance.yaml` - Performance targets and limits
|
||||
- `compression.yaml` - Context compression settings
|
||||
- `modes.yaml` - Mode activation thresholds
|
||||
- `mcp_orchestration.yaml` - MCP server coordination
|
||||
- `orchestrator.yaml` - General orchestration settings
|
||||
- `logging.yaml` - Logging configuration
|
||||
- `validation.yaml` - System validation rules
|
||||
- Others for specialized features
|
||||
|
||||
### Patterns
|
||||
3-tier pattern system for adaptability:
|
||||
- `minimal/` - Basic project detection patterns (3-5KB each)
|
||||
- `dynamic/` - Feature-specific patterns loaded on demand (8-12KB each)
|
||||
- `learned/` - User-specific adaptations that evolve with usage (10-20KB each)
|
||||
|
||||
### Cache
|
||||
The system maintains JSON cache files for:
|
||||
- User preferences and adaptations
|
||||
- Project-specific patterns
|
||||
- Learning records and effectiveness data
|
||||
- Session state and metrics
|
||||
|
||||
## Installation
|
||||
|
||||
1. Ensure Python 3.8+ is available
|
||||
2. Place Framework-Hooks directory in your SuperClaude installation
|
||||
3. Claude Code will automatically discover and use the hooks
|
||||
|
||||
## Usage
|
||||
|
||||
Framework-Hooks runs automatically when Claude Code starts a session. You don't need to invoke it manually.
|
||||
|
||||
The system will:
|
||||
1. Detect your project type and load appropriate patterns
|
||||
2. Activate relevant modes and MCP servers based on context
|
||||
3. Apply learned preferences from previous sessions
|
||||
4. Optimize performance based on resource constraints
|
||||
5. Learn from your usage patterns to improve future sessions
|
||||
|
||||
## How it works
|
||||
|
||||
### Session Flow
|
||||
```
|
||||
Session Start → Load Config → Detect Project → Apply Patterns →
|
||||
Activate Features → Work Session → Record Learning → Save State
|
||||
```
|
||||
|
||||
### Hook Coordination
|
||||
Hooks coordinate through shared state and configuration. Earlier hooks prepare context for later ones, and the system maintains consistency across the entire session lifecycle.
|
||||
|
||||
### Learning System
|
||||
The system tracks what works well for your specific projects and usage patterns. Over time, it adapts thresholds, preferences, and feature activation to match your workflow.
|
||||
|
||||
### Performance Targets
|
||||
- Session initialization: <50ms
|
||||
- Pattern loading: <100ms per pattern
|
||||
- Hook execution: <30ms per hook
|
||||
- Cache operations: <10ms
|
||||
|
||||
## Architecture
|
||||
|
||||
Framework-Hooks operates as a lightweight layer between Claude Code and the SuperClaude framework. It provides just-in-time intelligence loading instead of loading comprehensive framework documentation upfront.
|
||||
|
||||
The hook system allows Claude Code sessions to:
|
||||
- Start faster by loading only necessary context
|
||||
- Adapt to project-specific needs automatically
|
||||
- Learn from usage patterns over time
|
||||
- Coordinate MCP servers intelligently
|
||||
- Apply compression and optimization transparently
|
||||
|
||||
## Development
|
||||
|
||||
### Adding Hooks
|
||||
Create new hooks by:
|
||||
1. Adding a Python file in `hooks/` directory
|
||||
2. Following existing hook patterns for initialization
|
||||
3. Using shared modules for common functionality
|
||||
4. Adding corresponding configuration if needed
|
||||
|
||||
### Modifying Configuration
|
||||
YAML files in `config/` control hook behavior. Changes take effect on next session start.
|
||||
|
||||
### Pattern Development
|
||||
Add new patterns in appropriate `patterns/` subdirectories following existing YAML structure.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
Logs are written to `cache/logs/` directory. Check these files if hooks aren't behaving as expected.
|
||||
|
||||
The system includes validation utilities in `validate_system.py` for checking configuration and installation integrity.
|
||||
@@ -1,348 +0,0 @@
|
||||
# Troubleshooting Guide
|
||||
|
||||
Common issues and solutions for Framework-Hooks based on actual implementation patterns.
|
||||
|
||||
## Installation Issues
|
||||
|
||||
### Python Import Errors
|
||||
|
||||
**Problem**: Hook fails with `ModuleNotFoundError` for shared modules
|
||||
|
||||
**Cause**: Python path not finding shared modules in `hooks/shared/`
|
||||
|
||||
**Solution**:
|
||||
```python
|
||||
# Each hook script includes this path setup:
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
|
||||
```
|
||||
|
||||
Verify the `shared/` directory exists and contains all 9 modules:
|
||||
- `framework_logic.py`
|
||||
- `compression_engine.py`
|
||||
- `learning_engine.py`
|
||||
- `mcp_intelligence.py`
|
||||
- `pattern_detection.py`
|
||||
- `intelligence_engine.py`
|
||||
- `logger.py`
|
||||
- `yaml_loader.py`
|
||||
- `validate_system.py`
|
||||
|
||||
### Hook Execution Permissions
|
||||
|
||||
**Problem**: Hooks fail to execute with permission errors
|
||||
|
||||
**Solution**:
|
||||
```bash
|
||||
cd Framework-Hooks/hooks
|
||||
chmod +x *.py
|
||||
chmod +x shared/*.py
|
||||
```
|
||||
|
||||
The following hooks need execute permissions:
|
||||
- `pre_compact.py`
|
||||
- `stop.py`
|
||||
- `subagent_stop.py`
|
||||
|
||||
### YAML Configuration Errors
|
||||
|
||||
**Problem**: Hook fails with YAML parsing errors
|
||||
|
||||
**Cause**: Invalid YAML syntax in config files
|
||||
|
||||
**Solution**:
|
||||
```bash
|
||||
# Test YAML validity
|
||||
python3 -c "import yaml; yaml.safe_load(open('config/session.yaml'))"
|
||||
```
|
||||
|
||||
Check these configuration files for syntax issues:
|
||||
- `config/logging.yaml`
|
||||
- `config/session.yaml`
|
||||
- `config/performance.yaml`
|
||||
- `config/compression.yaml`
|
||||
- All other `.yaml` files in `config/`
|
||||
|
||||
## Performance Issues
|
||||
|
||||
### Hook Timeout Errors
|
||||
|
||||
**Problem**: Hooks timing out (default 10-15 seconds from settings.json)
|
||||
|
||||
**Cause**: Performance targets not met:
|
||||
- session_start.py: >50ms target
|
||||
- pre_tool_use.py: >200ms target
|
||||
- Other hooks: >100-150ms targets
|
||||
|
||||
**Diagnosis**:
|
||||
```bash
|
||||
# Enable timing logs in config/logging.yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: "INFO"
|
||||
hook_logging:
|
||||
log_timing: true
|
||||
```
|
||||
|
||||
**Solutions**:
|
||||
1. **Reduce pattern loading**: Remove unnecessary patterns from `patterns/` directories
|
||||
2. **Check disk I/O**: Ensure `cache/` directory is writable and has space
|
||||
3. **Disable verbose features**: Set `logging.level: "ERROR"`
|
||||
4. **Check Python performance**: Use faster Python interpreter if available
|
||||
|
||||
### Memory Usage Issues
|
||||
|
||||
**Problem**: High memory usage during hook execution
|
||||
|
||||
**Cause**: Large pattern files or cache accumulation
|
||||
|
||||
**Solutions**:
|
||||
1. **Clear cache**: Remove files from `cache/` directory
|
||||
2. **Reduce pattern size**: Check for oversized files in `patterns/learned/`
|
||||
3. **Limit learning data**: Review learning_engine.py cache size limits
|
||||
|
||||
### Pattern Loading Slow
|
||||
|
||||
**Problem**: Session start delays due to pattern loading
|
||||
|
||||
**Cause**: Pattern system loading large files from:
|
||||
- `patterns/minimal/`: Should be 3-5KB each
|
||||
- `patterns/dynamic/`: Should be 8-12KB each
|
||||
- `patterns/learned/`: Should be 10-20KB each
|
||||
|
||||
**Solutions**:
|
||||
1. **Check pattern sizes**: Identify oversized pattern files
|
||||
2. **Remove unused patterns**: Delete patterns not relevant to your projects
|
||||
3. **Reset learned patterns**: Clear `patterns/learned/` to start fresh
|
||||
|
||||
## Configuration Issues
|
||||
|
||||
### Logging Not Working
|
||||
|
||||
**Problem**: No log output despite enabling logging
|
||||
|
||||
**Cause**: Default logging configuration in `config/logging.yaml`:
|
||||
```yaml
|
||||
logging:
|
||||
enabled: false # Default is disabled
|
||||
level: "ERROR" # Only shows errors by default
|
||||
```
|
||||
|
||||
**Solution**: Enable logging properly:
|
||||
```yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: "INFO" # or "DEBUG" for verbose output
|
||||
hook_logging:
|
||||
log_lifecycle: true
|
||||
log_decisions: true
|
||||
log_timing: true
|
||||
```
|
||||
|
||||
### Cache Directory Issues
|
||||
|
||||
**Problem**: Hooks fail with cache write errors
|
||||
|
||||
**Cause**: Missing or permission issues with `cache/` directory
|
||||
|
||||
**Solution**:
|
||||
```bash
|
||||
mkdir -p Framework-Hooks/cache/logs
|
||||
chmod 755 Framework-Hooks/cache
|
||||
chmod 755 Framework-Hooks/cache/logs
|
||||
```
|
||||
|
||||
Required cache structure:
|
||||
```
|
||||
cache/
|
||||
├── logs/ # Log files (30-day retention)
|
||||
├── patterns/ # Cached pattern data
|
||||
├── learning/ # Learning engine data
|
||||
└── session/ # Session state
|
||||
```
|
||||
|
||||
### MCP Intelligence Failures
|
||||
|
||||
**Problem**: MCP server coordination not working
|
||||
|
||||
**Cause**: `mcp_intelligence.py` configuration issues
|
||||
|
||||
**Diagnosis**: Check `config/mcp_orchestration.yaml` for valid server configurations
|
||||
|
||||
**Solution**: Verify MCP server availability and configuration in:
|
||||
- Context7, Sequential, Magic, Playwright, Morphllm, Serena
|
||||
|
||||
## Runtime Issues
|
||||
|
||||
### Hook Script Failures
|
||||
|
||||
**Problem**: Individual hook scripts crash or fail
|
||||
|
||||
**Diagnosis Steps**:
|
||||
|
||||
1. **Test hook directly**:
|
||||
```bash
|
||||
cd Framework-Hooks/hooks
|
||||
python3 session_start.py
|
||||
```
|
||||
|
||||
2. **Check imports**: Verify all shared modules import correctly:
|
||||
```python
|
||||
from framework_logic import FrameworkLogic
|
||||
from pattern_detection import PatternDetector
|
||||
from mcp_intelligence import MCPIntelligence
|
||||
from compression_engine import CompressionEngine
|
||||
from learning_engine import LearningEngine
|
||||
```
|
||||
|
||||
3. **Check YAML loading**:
|
||||
```python
|
||||
from yaml_loader import config_loader
|
||||
config = config_loader.load_config('session')
|
||||
```
|
||||
|
||||
### Learning System Issues
|
||||
|
||||
**Problem**: Learning engine not adapting to usage patterns
|
||||
|
||||
**Cause**: Learning data not persisting or invalid
|
||||
|
||||
**Solutions**:
|
||||
1. **Check cache permissions**: Ensure `cache/learning/` is writable
|
||||
2. **Reset learning data**: Remove `cache/learning/*` files to start fresh
|
||||
3. **Verify pattern detection**: Check that `pattern_detection.py` identifies your project type
|
||||
|
||||
### Validation Failures
|
||||
|
||||
**Problem**: System validation reports errors
|
||||
|
||||
**Run validation manually**:
|
||||
```bash
|
||||
cd Framework-Hooks/hooks/shared
|
||||
python3 validate_system.py --full-check
|
||||
```
|
||||
|
||||
Common validation issues:
|
||||
- Missing configuration files
|
||||
- Invalid YAML syntax
|
||||
- Permission problems
|
||||
- Missing directories
|
||||
|
||||
## Debug Mode
|
||||
|
||||
### Enable Comprehensive Debugging
|
||||
|
||||
Edit `config/logging.yaml`:
|
||||
```yaml
|
||||
logging:
|
||||
enabled: true
|
||||
level: "DEBUG"
|
||||
|
||||
development:
|
||||
verbose_errors: true
|
||||
include_stack_traces: true
|
||||
debug_mode: true
|
||||
```
|
||||
|
||||
This provides detailed information about:
|
||||
- Hook execution flow
|
||||
- Pattern loading decisions
|
||||
- MCP server coordination
|
||||
- Learning system adaptations
|
||||
- Performance timing data
|
||||
|
||||
### Manual Hook Testing
|
||||
|
||||
Test individual hooks outside Claude Code:
|
||||
|
||||
```bash
|
||||
# Test session start
|
||||
python3 hooks/session_start.py
|
||||
|
||||
# Test tool use hooks
|
||||
python3 hooks/pre_tool_use.py
|
||||
python3 hooks/post_tool_use.py
|
||||
|
||||
# Test cleanup hooks
|
||||
python3 hooks/stop.py
|
||||
```
|
||||
|
||||
## System Health Checks
|
||||
|
||||
### Automated Validation
|
||||
|
||||
Run the comprehensive system check:
|
||||
```bash
|
||||
cd Framework-Hooks/hooks/shared
|
||||
python3 validate_system.py --health-check
|
||||
```
|
||||
|
||||
This checks:
|
||||
- File permissions and structure
|
||||
- YAML configuration validity
|
||||
- Python module imports
|
||||
- Cache directory accessibility
|
||||
- Pattern file integrity
|
||||
|
||||
### Performance Monitoring
|
||||
|
||||
Enable performance logging:
|
||||
```yaml
|
||||
# In config/performance.yaml
|
||||
performance_monitoring:
|
||||
enabled: true
|
||||
track_execution_time: true
|
||||
alert_on_slow_hooks: true
|
||||
target_times:
|
||||
session_start: 50 # ms
|
||||
pre_tool_use: 200 # ms
|
||||
post_tool_use: 100 # ms
|
||||
other_hooks: 100 # ms
|
||||
```
|
||||
|
||||
## Common Error Messages
|
||||
|
||||
### "Hook timeout exceeded"
|
||||
- **Cause**: Hook execution taking longer than 10-15 seconds (settings.json timeout)
|
||||
- **Solution**: Check performance issues section above
|
||||
|
||||
### "YAML load failed"
|
||||
- **Cause**: Invalid YAML syntax in configuration files
|
||||
- **Solution**: Validate YAML files using Python or online validator
|
||||
|
||||
### "Pattern detection failed"
|
||||
- **Cause**: Issues with pattern files or pattern_detection.py
|
||||
- **Solution**: Check pattern file sizes and YAML validity
|
||||
|
||||
### "Learning engine initialization failed"
|
||||
- **Cause**: Cache directory issues or learning data corruption
|
||||
- **Solution**: Clear cache and reset learning data
|
||||
|
||||
### "MCP intelligence routing failed"
|
||||
- **Cause**: MCP server configuration or availability issues
|
||||
- **Solution**: Check MCP server status and configuration
|
||||
|
||||
## Getting Help
|
||||
|
||||
### Log Analysis
|
||||
|
||||
Logs are written to `cache/logs/` with daily rotation (30-day retention). Check recent logs for detailed error information.
|
||||
|
||||
### Clean Installation
|
||||
|
||||
To reset to clean state:
|
||||
```bash
|
||||
# Backup any custom patterns first
|
||||
rm -rf cache/
|
||||
rm -rf patterns/learned/
|
||||
# Restart Claude Code session
|
||||
```
|
||||
|
||||
### Configuration Reset
|
||||
|
||||
To reset all configurations to defaults:
|
||||
```bash
|
||||
git checkout config/*.yaml
|
||||
# Or restore from backup if modified
|
||||
```
|
||||
|
||||
The system is designed to be resilient with conservative defaults. Most issues resolve with basic file permission fixes and configuration validation.
|
||||
@@ -1,604 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
SuperClaude-Lite Notification Hook
|
||||
|
||||
Implements just-in-time MCP documentation loading and pattern updates.
|
||||
Performance target: <100ms execution time.
|
||||
|
||||
This hook runs when Claude Code sends notifications and provides:
|
||||
- Just-in-time loading of MCP server documentation
|
||||
- Dynamic pattern updates based on operation context
|
||||
- Framework intelligence updates and adaptations
|
||||
- Real-time learning from notification patterns
|
||||
- Performance optimization through intelligent caching
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional
|
||||
|
||||
# Add shared modules to path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
|
||||
|
||||
from framework_logic import FrameworkLogic
|
||||
from pattern_detection import PatternDetector
|
||||
from mcp_intelligence import MCPIntelligence
|
||||
from compression_engine import CompressionEngine
|
||||
from learning_engine import LearningEngine, LearningType, AdaptationScope
|
||||
from yaml_loader import config_loader
|
||||
from logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
|
||||
|
||||
class NotificationHook:
|
||||
"""
|
||||
Notification hook implementing just-in-time intelligence loading.
|
||||
|
||||
Responsibilities:
|
||||
- Process Claude Code notifications for intelligence opportunities
|
||||
- Load relevant MCP documentation on-demand
|
||||
- Update pattern detection based on real-time context
|
||||
- Provide framework intelligence updates
|
||||
- Cache and optimize frequently accessed information
|
||||
- Learn from notification patterns for future optimization
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
start_time = time.time()
|
||||
|
||||
# Initialize core components
|
||||
self.framework_logic = FrameworkLogic()
|
||||
self.pattern_detector = PatternDetector()
|
||||
self.mcp_intelligence = MCPIntelligence()
|
||||
self.compression_engine = CompressionEngine()
|
||||
|
||||
# Initialize learning engine with installation directory cache
|
||||
import os
|
||||
cache_dir = Path(os.path.expanduser("~/.claude/cache"))
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.learning_engine = LearningEngine(cache_dir)
|
||||
|
||||
# Load notification configuration
|
||||
self.notification_config = config_loader.get_section('session', 'notifications', {})
|
||||
|
||||
# Initialize notification cache
|
||||
self.notification_cache = {}
|
||||
self.pattern_cache = {}
|
||||
|
||||
# Load hook-specific configuration from SuperClaude config
|
||||
self.hook_config = config_loader.get_hook_config('notification')
|
||||
|
||||
# Performance tracking using configuration
|
||||
self.initialization_time = (time.time() - start_time) * 1000
|
||||
self.performance_target_ms = config_loader.get_hook_config('notification', 'performance_target_ms', 100)
|
||||
|
||||
def process_notification(self, notification: dict) -> dict:
|
||||
"""
|
||||
Process notification with just-in-time intelligence loading.
|
||||
|
||||
Args:
|
||||
notification: Notification from Claude Code
|
||||
|
||||
Returns:
|
||||
Enhanced notification response with intelligence updates
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# Log hook start
|
||||
log_hook_start("notification", {
|
||||
"notification_type": notification.get('type', 'unknown'),
|
||||
"has_context": bool(notification.get('context')),
|
||||
"priority": notification.get('priority', 'normal')
|
||||
})
|
||||
|
||||
try:
|
||||
# Extract notification context
|
||||
context = self._extract_notification_context(notification)
|
||||
|
||||
# Analyze notification for intelligence opportunities
|
||||
intelligence_analysis = self._analyze_intelligence_opportunities(context)
|
||||
|
||||
# Determine intelligence needs
|
||||
intelligence_needs = self._analyze_intelligence_needs(context)
|
||||
|
||||
# Log intelligence loading decision
|
||||
if intelligence_needs.get('mcp_docs_needed'):
|
||||
log_decision(
|
||||
"notification",
|
||||
"mcp_docs_loading",
|
||||
",".join(intelligence_needs.get('mcp_servers', [])),
|
||||
f"Documentation needed for: {intelligence_needs.get('reason', 'notification context')}"
|
||||
)
|
||||
|
||||
# Load just-in-time documentation if needed
|
||||
documentation_updates = self._load_jit_documentation(context, intelligence_analysis)
|
||||
|
||||
# Update patterns if needed
|
||||
pattern_updates = self._update_patterns_if_needed(context, intelligence_needs)
|
||||
|
||||
# Log pattern update decision
|
||||
if pattern_updates.get('patterns_updated'):
|
||||
log_decision(
|
||||
"notification",
|
||||
"pattern_update",
|
||||
pattern_updates.get('pattern_type', 'unknown'),
|
||||
f"Updated {pattern_updates.get('update_count', 0)} patterns"
|
||||
)
|
||||
|
||||
# Generate framework intelligence updates
|
||||
framework_updates = self._generate_framework_updates(context, intelligence_analysis)
|
||||
|
||||
# Record learning events
|
||||
self._record_notification_learning(context, intelligence_analysis)
|
||||
|
||||
# Create intelligence response
|
||||
intelligence_response = self._create_intelligence_response(
|
||||
context, documentation_updates, pattern_updates, framework_updates
|
||||
)
|
||||
|
||||
# Performance validation
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
intelligence_response['performance_metrics'] = {
|
||||
'processing_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'cache_hit_rate': self._calculate_cache_hit_rate()
|
||||
}
|
||||
|
||||
# Log successful completion
|
||||
log_hook_end(
|
||||
"notification",
|
||||
int(execution_time),
|
||||
True,
|
||||
{
|
||||
"notification_type": context['notification_type'],
|
||||
"intelligence_loaded": bool(intelligence_needs.get('mcp_docs_needed')),
|
||||
"patterns_updated": pattern_updates.get('patterns_updated', False)
|
||||
}
|
||||
)
|
||||
|
||||
return intelligence_response
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
log_error(
|
||||
"notification",
|
||||
str(e),
|
||||
{"notification_type": notification.get('type', 'unknown')}
|
||||
)
|
||||
log_hook_end("notification", int(execution_time), False)
|
||||
|
||||
# Graceful fallback on error
|
||||
return self._create_fallback_response(notification, str(e))
|
||||
|
||||
def _extract_notification_context(self, notification: dict) -> dict:
|
||||
"""Extract and enrich notification context."""
|
||||
context = {
|
||||
'notification_type': notification.get('type', 'unknown'),
|
||||
'notification_data': notification.get('data', {}),
|
||||
'session_context': notification.get('session_context', {}),
|
||||
'user_context': notification.get('user_context', {}),
|
||||
'operation_context': notification.get('operation_context', {}),
|
||||
'trigger_event': notification.get('trigger', ''),
|
||||
'timestamp': time.time()
|
||||
}
|
||||
|
||||
# Analyze notification importance
|
||||
context['priority'] = self._assess_notification_priority(context)
|
||||
|
||||
# Extract operation characteristics
|
||||
context.update(self._extract_operation_characteristics(context))
|
||||
|
||||
return context
|
||||
|
||||
def _assess_notification_priority(self, context: dict) -> str:
|
||||
"""Assess notification priority for processing."""
|
||||
notification_type = context['notification_type']
|
||||
|
||||
# High priority notifications
|
||||
if notification_type in ['error', 'failure', 'security_alert']:
|
||||
return 'high'
|
||||
elif notification_type in ['performance_issue', 'validation_failure']:
|
||||
return 'high'
|
||||
|
||||
# Medium priority notifications
|
||||
elif notification_type in ['tool_request', 'context_change', 'resource_constraint']:
|
||||
return 'medium'
|
||||
|
||||
# Low priority notifications
|
||||
elif notification_type in ['info', 'debug', 'status_update']:
|
||||
return 'low'
|
||||
|
||||
return 'medium'
|
||||
|
||||
def _extract_operation_characteristics(self, context: dict) -> dict:
|
||||
"""Extract operation characteristics from notification."""
|
||||
operation_context = context.get('operation_context', {})
|
||||
|
||||
return {
|
||||
'operation_type': operation_context.get('type', 'unknown'),
|
||||
'complexity_indicators': operation_context.get('complexity', 0.0),
|
||||
'tool_requests': operation_context.get('tools_requested', []),
|
||||
'mcp_server_hints': operation_context.get('mcp_hints', []),
|
||||
'performance_requirements': operation_context.get('performance', {}),
|
||||
'intelligence_requirements': operation_context.get('intelligence_needed', False)
|
||||
}
|
||||
|
||||
def _analyze_intelligence_opportunities(self, context: dict) -> dict:
|
||||
"""Analyze notification for intelligence loading opportunities."""
|
||||
analysis = {
|
||||
'documentation_needed': [],
|
||||
'pattern_updates_needed': [],
|
||||
'framework_updates_needed': [],
|
||||
'learning_opportunities': [],
|
||||
'optimization_opportunities': []
|
||||
}
|
||||
|
||||
notification_type = context['notification_type']
|
||||
operation_type = context.get('operation_type', 'unknown')
|
||||
|
||||
# Documentation loading opportunities
|
||||
if notification_type == 'tool_request':
|
||||
requested_tools = context.get('tool_requests', [])
|
||||
for tool in requested_tools:
|
||||
if tool in ['ui_component', 'component_generation']:
|
||||
analysis['documentation_needed'].append('magic_patterns')
|
||||
elif tool in ['library_integration', 'framework_usage']:
|
||||
analysis['documentation_needed'].append('context7_patterns')
|
||||
elif tool in ['complex_analysis', 'debugging']:
|
||||
analysis['documentation_needed'].append('sequential_patterns')
|
||||
elif tool in ['testing', 'validation']:
|
||||
analysis['documentation_needed'].append('playwright_patterns')
|
||||
|
||||
# Pattern update opportunities
|
||||
if notification_type in ['context_change', 'operation_start']:
|
||||
analysis['pattern_updates_needed'].extend([
|
||||
'operation_patterns',
|
||||
'context_patterns'
|
||||
])
|
||||
|
||||
# Framework update opportunities
|
||||
if notification_type in ['performance_issue', 'optimization_request']:
|
||||
analysis['framework_updates_needed'].extend([
|
||||
'performance_optimization',
|
||||
'resource_management'
|
||||
])
|
||||
|
||||
# Learning opportunities
|
||||
if notification_type in ['error', 'failure']:
|
||||
analysis['learning_opportunities'].append('error_pattern_learning')
|
||||
elif notification_type in ['success', 'completion']:
|
||||
analysis['learning_opportunities'].append('success_pattern_learning')
|
||||
|
||||
# Optimization opportunities
|
||||
if context.get('performance_requirements'):
|
||||
analysis['optimization_opportunities'].append('performance_optimization')
|
||||
|
||||
return analysis
|
||||
|
||||
def _analyze_intelligence_needs(self, context: dict) -> dict:
|
||||
"""Determine intelligence needs based on context."""
|
||||
needs = {
|
||||
'mcp_docs_needed': False,
|
||||
'mcp_servers': [],
|
||||
'reason': ''
|
||||
}
|
||||
|
||||
# Check for MCP server hints
|
||||
mcp_hints = context.get('mcp_server_hints', [])
|
||||
if mcp_hints:
|
||||
needs['mcp_docs_needed'] = True
|
||||
needs['mcp_servers'] = mcp_hints
|
||||
needs['reason'] = 'MCP server hints'
|
||||
|
||||
# Check for tool requests
|
||||
tool_requests = context.get('tool_requests', [])
|
||||
if tool_requests:
|
||||
needs['mcp_docs_needed'] = True
|
||||
needs['mcp_servers'] = [tool for tool in tool_requests if tool in ['ui_component', 'component_generation', 'library_integration', 'framework_usage', 'complex_analysis', 'debugging', 'testing', 'validation']]
|
||||
needs['reason'] = 'Tool requests'
|
||||
|
||||
# Check for performance requirements
|
||||
performance_requirements = context.get('performance_requirements', {})
|
||||
if performance_requirements:
|
||||
needs['mcp_docs_needed'] = True
|
||||
needs['mcp_servers'] = ['performance_optimization', 'resource_management']
|
||||
needs['reason'] = 'Performance requirements'
|
||||
|
||||
return needs
|
||||
|
||||
def _load_jit_documentation(self, context: dict, intelligence_analysis: dict) -> dict:
|
||||
"""Load just-in-time documentation based on analysis."""
|
||||
documentation_updates = {
|
||||
'loaded_patterns': [],
|
||||
'cached_content': {},
|
||||
'documentation_summaries': {}
|
||||
}
|
||||
|
||||
needed_docs = intelligence_analysis.get('documentation_needed', [])
|
||||
|
||||
for doc_type in needed_docs:
|
||||
# Check cache first
|
||||
if doc_type in self.notification_cache:
|
||||
documentation_updates['cached_content'][doc_type] = self.notification_cache[doc_type]
|
||||
documentation_updates['loaded_patterns'].append(f"{doc_type}_cached")
|
||||
continue
|
||||
|
||||
# Load documentation on-demand
|
||||
doc_content = self._load_documentation_content(doc_type, context)
|
||||
if doc_content:
|
||||
# Cache for future use
|
||||
self.notification_cache[doc_type] = doc_content
|
||||
documentation_updates['cached_content'][doc_type] = doc_content
|
||||
documentation_updates['loaded_patterns'].append(f"{doc_type}_loaded")
|
||||
|
||||
# Create summary for quick access
|
||||
summary = self._create_documentation_summary(doc_content)
|
||||
documentation_updates['documentation_summaries'][doc_type] = summary
|
||||
|
||||
return documentation_updates
|
||||
|
||||
def _load_documentation_content(self, doc_type: str, context: dict) -> Optional[dict]:
|
||||
"""Load specific documentation content."""
|
||||
# Simulated documentation loading - real implementation would fetch from MCP servers
|
||||
documentation_patterns = {
|
||||
'magic_patterns': {
|
||||
'ui_components': ['button', 'form', 'modal', 'card'],
|
||||
'design_systems': ['theme', 'tokens', 'spacing'],
|
||||
'accessibility': ['aria-labels', 'keyboard-navigation', 'screen-readers']
|
||||
},
|
||||
'context7_patterns': {
|
||||
'library_integration': ['import_patterns', 'configuration', 'best_practices'],
|
||||
'framework_usage': ['react_patterns', 'vue_patterns', 'angular_patterns'],
|
||||
'documentation_access': ['api_docs', 'examples', 'tutorials']
|
||||
},
|
||||
'sequential_patterns': {
|
||||
'analysis_workflows': ['step_by_step', 'hypothesis_testing', 'validation'],
|
||||
'debugging_strategies': ['systematic_approach', 'root_cause', 'verification'],
|
||||
'complex_reasoning': ['decomposition', 'synthesis', 'optimization']
|
||||
},
|
||||
'playwright_patterns': {
|
||||
'testing_strategies': ['e2e_tests', 'unit_tests', 'integration_tests'],
|
||||
'automation_patterns': ['page_objects', 'test_data', 'assertions'],
|
||||
'performance_testing': ['load_testing', 'stress_testing', 'monitoring']
|
||||
}
|
||||
}
|
||||
|
||||
return documentation_patterns.get(doc_type, {})
|
||||
|
||||
def _create_documentation_summary(self, doc_content: dict) -> dict:
|
||||
"""Create summary of documentation content for quick access."""
|
||||
summary = {
|
||||
'categories': list(doc_content.keys()),
|
||||
'total_patterns': sum(len(patterns) if isinstance(patterns, list) else 1
|
||||
for patterns in doc_content.values()),
|
||||
'quick_access_items': []
|
||||
}
|
||||
|
||||
# Extract most commonly used patterns
|
||||
for category, patterns in doc_content.items():
|
||||
if isinstance(patterns, list) and patterns:
|
||||
summary['quick_access_items'].append({
|
||||
'category': category,
|
||||
'top_pattern': patterns[0],
|
||||
'pattern_count': len(patterns)
|
||||
})
|
||||
|
||||
return summary
|
||||
|
||||
def _update_patterns_if_needed(self, context: dict, intelligence_needs: dict) -> dict:
|
||||
"""Update pattern detection based on context."""
|
||||
pattern_updates = {
|
||||
'updated_patterns': [],
|
||||
'new_patterns_detected': [],
|
||||
'pattern_effectiveness': {}
|
||||
}
|
||||
|
||||
if intelligence_needs.get('mcp_docs_needed'):
|
||||
# Update operation-specific patterns
|
||||
operation_type = context.get('operation_type', 'unknown')
|
||||
self._update_operation_patterns(operation_type, pattern_updates)
|
||||
|
||||
# Update context-specific patterns
|
||||
session_context = context.get('session_context', {})
|
||||
self._update_context_patterns(session_context, pattern_updates)
|
||||
|
||||
return pattern_updates
|
||||
|
||||
def _update_operation_patterns(self, operation_type: str, pattern_updates: dict):
|
||||
"""Update operation-specific patterns."""
|
||||
if operation_type in ['build', 'implement']:
|
||||
pattern_updates['updated_patterns'].append('build_operation_patterns')
|
||||
# Update pattern detection for build operations
|
||||
elif operation_type in ['analyze', 'debug']:
|
||||
pattern_updates['updated_patterns'].append('analysis_operation_patterns')
|
||||
# Update pattern detection for analysis operations
|
||||
elif operation_type in ['test', 'validate']:
|
||||
pattern_updates['updated_patterns'].append('testing_operation_patterns')
|
||||
# Update pattern detection for testing operations
|
||||
|
||||
def _update_context_patterns(self, session_context: dict, pattern_updates: dict):
|
||||
"""Update context-specific patterns."""
|
||||
if session_context.get('project_type') == 'frontend':
|
||||
pattern_updates['updated_patterns'].append('frontend_context_patterns')
|
||||
elif session_context.get('project_type') == 'backend':
|
||||
pattern_updates['updated_patterns'].append('backend_context_patterns')
|
||||
elif session_context.get('project_type') == 'fullstack':
|
||||
pattern_updates['updated_patterns'].append('fullstack_context_patterns')
|
||||
|
||||
def _generate_framework_updates(self, context: dict, intelligence_analysis: dict) -> dict:
|
||||
"""Generate framework intelligence updates."""
|
||||
framework_updates = {
|
||||
'configuration_updates': {},
|
||||
'optimization_recommendations': [],
|
||||
'intelligence_enhancements': []
|
||||
}
|
||||
|
||||
needed_updates = intelligence_analysis.get('framework_updates_needed', [])
|
||||
|
||||
for update_type in needed_updates:
|
||||
if update_type == 'performance_optimization':
|
||||
framework_updates['optimization_recommendations'].extend([
|
||||
'Enable parallel processing for multi-file operations',
|
||||
'Activate compression for resource-constrained scenarios',
|
||||
'Use intelligent caching for repeated operations'
|
||||
])
|
||||
|
||||
elif update_type == 'resource_management':
|
||||
resource_usage = context.get('session_context', {}).get('resource_usage', 0)
|
||||
if resource_usage > 75:
|
||||
framework_updates['configuration_updates']['compression'] = 'enable_aggressive'
|
||||
framework_updates['optimization_recommendations'].append(
|
||||
'Resource usage high - enabling aggressive compression'
|
||||
)
|
||||
|
||||
return framework_updates
|
||||
|
||||
def _record_notification_learning(self, context: dict, intelligence_analysis: dict):
|
||||
"""Record notification learning for optimization."""
|
||||
learning_opportunities = intelligence_analysis.get('learning_opportunities', [])
|
||||
|
||||
for opportunity in learning_opportunities:
|
||||
if opportunity == 'error_pattern_learning':
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.ERROR_RECOVERY,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'notification_type': context['notification_type'],
|
||||
'error_context': context.get('notification_data', {}),
|
||||
'intelligence_loaded': len(intelligence_analysis.get('documentation_needed', []))
|
||||
},
|
||||
0.7, # Learning value from errors
|
||||
0.8,
|
||||
{'hook': 'notification', 'learning_type': 'error'}
|
||||
)
|
||||
|
||||
elif opportunity == 'success_pattern_learning':
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.OPERATION_PATTERN,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'notification_type': context['notification_type'],
|
||||
'success_context': context.get('notification_data', {}),
|
||||
'patterns_updated': len(intelligence_analysis.get('pattern_updates_needed', []))
|
||||
},
|
||||
0.9, # High learning value from success
|
||||
0.9,
|
||||
{'hook': 'notification', 'learning_type': 'success'}
|
||||
)
|
||||
|
||||
def _calculate_cache_hit_rate(self) -> float:
|
||||
"""Calculate cache hit ratio for performance metrics."""
|
||||
if not hasattr(self, '_cache_requests'):
|
||||
self._cache_requests = 0
|
||||
self._cache_hits = 0
|
||||
|
||||
if self._cache_requests == 0:
|
||||
return 0.0
|
||||
|
||||
return self._cache_hits / self._cache_requests
|
||||
|
||||
def _create_intelligence_response(self, context: dict, documentation_updates: dict,
|
||||
pattern_updates: dict, framework_updates: dict) -> dict:
|
||||
"""Create comprehensive intelligence response."""
|
||||
return {
|
||||
'notification_type': context['notification_type'],
|
||||
'priority': context['priority'],
|
||||
'timestamp': context['timestamp'],
|
||||
|
||||
'intelligence_updates': {
|
||||
'documentation_loaded': len(documentation_updates.get('loaded_patterns', [])) > 0,
|
||||
'patterns_updated': len(pattern_updates.get('updated_patterns', [])) > 0,
|
||||
'framework_enhanced': len(framework_updates.get('optimization_recommendations', [])) > 0
|
||||
},
|
||||
|
||||
'documentation': {
|
||||
'patterns_loaded': documentation_updates.get('loaded_patterns', []),
|
||||
'summaries': documentation_updates.get('documentation_summaries', {}),
|
||||
'cache_status': 'active'
|
||||
},
|
||||
|
||||
'patterns': {
|
||||
'updated_patterns': pattern_updates.get('updated_patterns', []),
|
||||
'new_patterns': pattern_updates.get('new_patterns_detected', []),
|
||||
'effectiveness': pattern_updates.get('pattern_effectiveness', {})
|
||||
},
|
||||
|
||||
'framework': {
|
||||
'configuration_updates': framework_updates.get('configuration_updates', {}),
|
||||
'optimization_recommendations': framework_updates.get('optimization_recommendations', []),
|
||||
'intelligence_enhancements': framework_updates.get('intelligence_enhancements', [])
|
||||
},
|
||||
|
||||
'optimization': {
|
||||
'just_in_time_loading': True,
|
||||
'intelligent_caching': True,
|
||||
'performance_optimized': True,
|
||||
'learning_enabled': True
|
||||
},
|
||||
|
||||
'metadata': {
|
||||
'hook_version': 'notification_1.0',
|
||||
'processing_timestamp': time.time(),
|
||||
'intelligence_level': 'adaptive'
|
||||
}
|
||||
}
|
||||
|
||||
def _create_fallback_response(self, notification: dict, error: str) -> dict:
|
||||
"""Create fallback response on error."""
|
||||
return {
|
||||
'notification_type': notification.get('type', 'unknown'),
|
||||
'priority': 'low',
|
||||
'error': error,
|
||||
'fallback_mode': True,
|
||||
|
||||
'intelligence_updates': {
|
||||
'documentation_loaded': False,
|
||||
'patterns_updated': False,
|
||||
'framework_enhanced': False
|
||||
},
|
||||
|
||||
'documentation': {
|
||||
'patterns_loaded': [],
|
||||
'summaries': {},
|
||||
'cache_status': 'error'
|
||||
},
|
||||
|
||||
'performance_metrics': {
|
||||
'processing_time_ms': 0,
|
||||
'target_met': False,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
"""Main hook execution function."""
|
||||
try:
|
||||
# Read notification from stdin
|
||||
notification = json.loads(sys.stdin.read())
|
||||
|
||||
# Initialize and run hook
|
||||
hook = NotificationHook()
|
||||
result = hook.process_notification(notification)
|
||||
|
||||
# Output result as JSON
|
||||
print(json.dumps(result, indent=2))
|
||||
|
||||
except Exception as e:
|
||||
# Output error as JSON
|
||||
error_result = {
|
||||
'intelligence_updates_enabled': False,
|
||||
'error': str(e),
|
||||
'fallback_mode': True
|
||||
}
|
||||
print(json.dumps(error_result, indent=2))
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,794 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
SuperClaude-Lite Post-Tool-Use Hook
|
||||
|
||||
Implements RULES.md + PRINCIPLES.md validation and learning system.
|
||||
Performance target: <100ms execution time.
|
||||
|
||||
This hook runs after every tool usage and provides:
|
||||
- Quality validation against SuperClaude principles
|
||||
- Effectiveness measurement and learning
|
||||
- Error pattern detection and prevention
|
||||
- Performance optimization feedback
|
||||
- Adaptation and improvement recommendations
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional, Tuple
|
||||
|
||||
# Add shared modules to path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
|
||||
|
||||
from framework_logic import FrameworkLogic, ValidationResult, OperationContext, OperationType, RiskLevel
|
||||
from pattern_detection import PatternDetector
|
||||
from mcp_intelligence import MCPIntelligence
|
||||
from compression_engine import CompressionEngine
|
||||
from learning_engine import LearningEngine, LearningType, AdaptationScope
|
||||
from yaml_loader import config_loader
|
||||
from logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
|
||||
|
||||
class PostToolUseHook:
|
||||
"""
|
||||
Post-tool-use hook implementing SuperClaude validation and learning.
|
||||
|
||||
Responsibilities:
|
||||
- Validate tool execution against RULES.md and PRINCIPLES.md
|
||||
- Measure operation effectiveness and quality
|
||||
- Learn from successful and failed patterns
|
||||
- Detect error patterns and suggest improvements
|
||||
- Record performance metrics for optimization
|
||||
- Generate adaptation recommendations
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
start_time = time.time()
|
||||
|
||||
# Initialize core components
|
||||
self.framework_logic = FrameworkLogic()
|
||||
self.pattern_detector = PatternDetector()
|
||||
self.mcp_intelligence = MCPIntelligence()
|
||||
self.compression_engine = CompressionEngine()
|
||||
|
||||
# Initialize learning engine with installation directory cache
|
||||
import os
|
||||
cache_dir = Path(os.path.expanduser("~/.claude/cache"))
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.learning_engine = LearningEngine(cache_dir)
|
||||
|
||||
# Load hook-specific configuration from SuperClaude config
|
||||
self.hook_config = config_loader.get_hook_config('post_tool_use')
|
||||
|
||||
# Load validation configuration (from YAML if exists, otherwise use hook config)
|
||||
try:
|
||||
self.validation_config = config_loader.load_config('validation')
|
||||
except FileNotFoundError:
|
||||
# Fall back to hook configuration if YAML file not found
|
||||
self.validation_config = self.hook_config.get('configuration', {})
|
||||
|
||||
# Load quality standards (from YAML if exists, otherwise use hook config)
|
||||
try:
|
||||
self.quality_standards = config_loader.load_config('performance')
|
||||
except FileNotFoundError:
|
||||
# Fall back to performance targets from global configuration
|
||||
self.quality_standards = config_loader.get_performance_targets()
|
||||
|
||||
# Performance tracking using configuration
|
||||
self.initialization_time = (time.time() - start_time) * 1000
|
||||
self.performance_target_ms = config_loader.get_hook_config('post_tool_use', 'performance_target_ms', 100)
|
||||
|
||||
def process_tool_result(self, tool_result: dict) -> dict:
|
||||
"""
|
||||
Process tool execution result with validation and learning.
|
||||
|
||||
Args:
|
||||
tool_result: Tool execution result from Claude Code
|
||||
|
||||
Returns:
|
||||
Enhanced result with SuperClaude validation and insights
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# Log hook start
|
||||
log_hook_start("post_tool_use", {
|
||||
"tool_name": tool_result.get('tool_name', 'unknown'),
|
||||
"success": tool_result.get('success', False),
|
||||
"has_error": bool(tool_result.get('error'))
|
||||
})
|
||||
|
||||
try:
|
||||
# Extract execution context
|
||||
context = self._extract_execution_context(tool_result)
|
||||
|
||||
# Validate against SuperClaude principles
|
||||
validation_result = self._validate_tool_result(context)
|
||||
|
||||
# Log validation decision
|
||||
if not validation_result.is_valid:
|
||||
log_decision(
|
||||
"post_tool_use",
|
||||
"validation_failure",
|
||||
validation_result.failed_checks[0] if validation_result.failed_checks else "unknown",
|
||||
f"Tool '{context['tool_name']}' failed validation: {validation_result.message}"
|
||||
)
|
||||
|
||||
# Measure effectiveness and quality
|
||||
effectiveness_metrics = self._measure_effectiveness(context, validation_result)
|
||||
|
||||
# Detect patterns and learning opportunities
|
||||
learning_analysis = self._analyze_learning_opportunities(context, effectiveness_metrics)
|
||||
|
||||
# Record learning events
|
||||
self._record_learning_events(context, effectiveness_metrics, learning_analysis)
|
||||
|
||||
# Generate recommendations
|
||||
recommendations = self._generate_recommendations(context, validation_result, learning_analysis)
|
||||
|
||||
# Create validation report
|
||||
validation_report = self._create_validation_report(
|
||||
context, validation_result, effectiveness_metrics,
|
||||
learning_analysis, recommendations
|
||||
)
|
||||
|
||||
# Detect patterns in tool execution
|
||||
pattern_analysis = self._analyze_execution_patterns(context, validation_result)
|
||||
|
||||
# Log pattern detection
|
||||
if pattern_analysis.get('error_pattern_detected'):
|
||||
log_decision(
|
||||
"post_tool_use",
|
||||
"error_pattern_detected",
|
||||
pattern_analysis.get('pattern_type', 'unknown'),
|
||||
pattern_analysis.get('description', 'Error pattern identified')
|
||||
)
|
||||
|
||||
# Performance tracking
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
validation_report['performance_metrics'] = {
|
||||
'processing_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'quality_score': self._calculate_quality_score(context, validation_result)
|
||||
}
|
||||
|
||||
# Log successful completion
|
||||
log_hook_end(
|
||||
"post_tool_use",
|
||||
int(execution_time),
|
||||
True,
|
||||
{
|
||||
"tool_name": context['tool_name'],
|
||||
"validation_passed": validation_result.is_valid,
|
||||
"quality_score": validation_report['performance_metrics']['quality_score']
|
||||
}
|
||||
)
|
||||
|
||||
return validation_report
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
log_error(
|
||||
"post_tool_use",
|
||||
str(e),
|
||||
{"tool_name": tool_result.get('tool_name', 'unknown')}
|
||||
)
|
||||
log_hook_end("post_tool_use", int(execution_time), False)
|
||||
|
||||
# Graceful fallback on error
|
||||
return self._create_fallback_result(tool_result, str(e))
|
||||
|
||||
def _extract_execution_context(self, tool_result: dict) -> dict:
|
||||
"""Extract and enrich tool execution context."""
|
||||
context = {
|
||||
'tool_name': tool_result.get('tool_name', ''),
|
||||
'execution_status': tool_result.get('status', 'unknown'),
|
||||
'execution_time_ms': tool_result.get('execution_time_ms', 0),
|
||||
'parameters_used': tool_result.get('parameters', {}),
|
||||
'result_data': tool_result.get('result', {}),
|
||||
'error_info': tool_result.get('error', {}),
|
||||
'mcp_servers_used': tool_result.get('mcp_servers', []),
|
||||
'performance_data': tool_result.get('performance', {}),
|
||||
'user_intent': tool_result.get('user_intent', ''),
|
||||
'session_context': tool_result.get('session_context', {}),
|
||||
'timestamp': time.time()
|
||||
}
|
||||
|
||||
# Analyze operation characteristics
|
||||
context.update(self._analyze_operation_outcome(context))
|
||||
|
||||
# Extract quality indicators
|
||||
context.update(self._extract_quality_indicators(context))
|
||||
|
||||
return context
|
||||
|
||||
def _analyze_operation_outcome(self, context: dict) -> dict:
|
||||
"""Analyze the outcome of the tool operation."""
|
||||
outcome_analysis = {
|
||||
'success': context['execution_status'] == 'success',
|
||||
'partial_success': False,
|
||||
'error_occurred': context['execution_status'] == 'error',
|
||||
'performance_acceptable': True,
|
||||
'quality_indicators': [],
|
||||
'risk_factors': []
|
||||
}
|
||||
|
||||
# Analyze execution status
|
||||
if context['execution_status'] in ['partial', 'warning']:
|
||||
outcome_analysis['partial_success'] = True
|
||||
|
||||
# Performance analysis
|
||||
execution_time = context.get('execution_time_ms', 0)
|
||||
if execution_time > 5000: # 5 second threshold
|
||||
outcome_analysis['performance_acceptable'] = False
|
||||
outcome_analysis['risk_factors'].append('slow_execution')
|
||||
|
||||
# Error analysis
|
||||
if context.get('error_info'):
|
||||
error_type = context['error_info'].get('type', 'unknown')
|
||||
outcome_analysis['error_type'] = error_type
|
||||
outcome_analysis['error_recoverable'] = error_type not in ['fatal', 'security', 'corruption']
|
||||
|
||||
# Quality indicators from result data
|
||||
result_data = context.get('result_data', {})
|
||||
if result_data:
|
||||
if result_data.get('validation_passed'):
|
||||
outcome_analysis['quality_indicators'].append('validation_passed')
|
||||
if result_data.get('tests_passed'):
|
||||
outcome_analysis['quality_indicators'].append('tests_passed')
|
||||
if result_data.get('linting_clean'):
|
||||
outcome_analysis['quality_indicators'].append('linting_clean')
|
||||
|
||||
return outcome_analysis
|
||||
|
||||
def _extract_quality_indicators(self, context: dict) -> dict:
|
||||
"""Extract quality indicators from execution context."""
|
||||
quality_indicators = {
|
||||
'code_quality_score': 0.0,
|
||||
'security_compliance': True,
|
||||
'performance_efficiency': 1.0,
|
||||
'error_handling_present': False,
|
||||
'documentation_adequate': False,
|
||||
'test_coverage_acceptable': False
|
||||
}
|
||||
|
||||
# Analyze tool output for quality indicators
|
||||
tool_name = context['tool_name']
|
||||
result_data = context.get('result_data', {})
|
||||
|
||||
# Code quality analysis
|
||||
if tool_name in ['Write', 'Edit', 'Generate']:
|
||||
# Check for quality indicators in the result
|
||||
if 'quality_score' in result_data:
|
||||
quality_indicators['code_quality_score'] = result_data['quality_score']
|
||||
|
||||
# Infer quality from operation success and performance
|
||||
if context.get('success') and context.get('performance_acceptable'):
|
||||
quality_indicators['code_quality_score'] = max(
|
||||
quality_indicators['code_quality_score'], 0.7
|
||||
)
|
||||
|
||||
# Security compliance
|
||||
if context.get('error_type') in ['security', 'vulnerability']:
|
||||
quality_indicators['security_compliance'] = False
|
||||
|
||||
# Performance efficiency
|
||||
execution_time = context.get('execution_time_ms', 0)
|
||||
expected_time = context.get('performance_data', {}).get('expected_time_ms', 1000)
|
||||
if execution_time > 0 and expected_time > 0:
|
||||
quality_indicators['performance_efficiency'] = min(expected_time / execution_time, 2.0)
|
||||
|
||||
# Error handling detection
|
||||
if tool_name in ['Write', 'Edit'] and 'try' in str(result_data).lower():
|
||||
quality_indicators['error_handling_present'] = True
|
||||
|
||||
# Documentation assessment
|
||||
if tool_name in ['Document', 'Generate'] or 'doc' in context.get('user_intent', '').lower():
|
||||
quality_indicators['documentation_adequate'] = context.get('success', False)
|
||||
|
||||
return quality_indicators
|
||||
|
||||
def _validate_tool_result(self, context: dict) -> ValidationResult:
|
||||
"""Validate execution against SuperClaude principles."""
|
||||
# Create operation data for validation
|
||||
operation_data = {
|
||||
'operation_type': context['tool_name'],
|
||||
'has_error_handling': context.get('error_handling_present', False),
|
||||
'affects_logic': context['tool_name'] in ['Write', 'Edit', 'Generate'],
|
||||
'has_tests': context.get('test_coverage_acceptable', False),
|
||||
'is_public_api': 'api' in context.get('user_intent', '').lower(),
|
||||
'has_documentation': context.get('documentation_adequate', False),
|
||||
'handles_user_input': 'input' in context.get('user_intent', '').lower(),
|
||||
'has_input_validation': context.get('security_compliance', True),
|
||||
'evidence': context.get('success', False)
|
||||
}
|
||||
|
||||
# Run framework validation
|
||||
validation_result = self.framework_logic.validate_operation(operation_data)
|
||||
|
||||
# Enhance with SuperClaude-specific validations
|
||||
validation_result = self._enhance_validation_with_superclaude_rules(
|
||||
validation_result, context
|
||||
)
|
||||
|
||||
return validation_result
|
||||
|
||||
def _enhance_validation_with_superclaude_rules(self,
|
||||
base_validation: ValidationResult,
|
||||
context: dict) -> ValidationResult:
|
||||
"""Enhance validation with SuperClaude-specific rules."""
|
||||
enhanced_validation = ValidationResult(
|
||||
is_valid=base_validation.is_valid,
|
||||
issues=base_validation.issues.copy(),
|
||||
warnings=base_validation.warnings.copy(),
|
||||
suggestions=base_validation.suggestions.copy(),
|
||||
quality_score=base_validation.quality_score
|
||||
)
|
||||
|
||||
# RULES.md validation
|
||||
|
||||
# Rule: Always use Read tool before Write or Edit operations
|
||||
if context['tool_name'] in ['Write', 'Edit']:
|
||||
session_context = context.get('session_context', {})
|
||||
recent_tools = session_context.get('recent_tools', [])
|
||||
if not any('Read' in tool for tool in recent_tools[-3:]):
|
||||
enhanced_validation.warnings.append(
|
||||
"RULES violation: No Read operation detected before Write/Edit"
|
||||
)
|
||||
enhanced_validation.quality_score -= 0.1
|
||||
|
||||
# Rule: Use absolute paths only
|
||||
params = context.get('parameters_used', {})
|
||||
for param_name, param_value in params.items():
|
||||
if 'path' in param_name.lower() and isinstance(param_value, str):
|
||||
if not os.path.isabs(param_value) and not param_value.startswith(('http', 'https')):
|
||||
enhanced_validation.issues.append(
|
||||
f"RULES violation: Relative path used in {param_name}: {param_value}"
|
||||
)
|
||||
enhanced_validation.quality_score -= 0.2
|
||||
|
||||
# Rule: Validate before execution for high-risk operations
|
||||
if context.get('risk_factors'):
|
||||
if not context.get('validation_performed', False):
|
||||
enhanced_validation.warnings.append(
|
||||
"RULES recommendation: High-risk operation should include validation"
|
||||
)
|
||||
|
||||
# PRINCIPLES.md validation
|
||||
|
||||
# Principle: Evidence > assumptions
|
||||
if not context.get('evidence_provided', False) and context.get('assumptions_made', False):
|
||||
enhanced_validation.suggestions.append(
|
||||
"PRINCIPLES: Provide evidence to support assumptions"
|
||||
)
|
||||
|
||||
# Principle: Code > documentation
|
||||
if context['tool_name'] == 'Document' and not context.get('working_code_exists', True):
|
||||
enhanced_validation.warnings.append(
|
||||
"PRINCIPLES: Documentation should follow working code, not precede it"
|
||||
)
|
||||
|
||||
# Principle: Efficiency > verbosity
|
||||
result_size = len(str(context.get('result_data', '')))
|
||||
if result_size > 5000 and not context.get('complexity_justifies_length', False):
|
||||
enhanced_validation.suggestions.append(
|
||||
"PRINCIPLES: Consider token efficiency techniques for large outputs"
|
||||
)
|
||||
|
||||
# Recalculate overall validity
|
||||
enhanced_validation.is_valid = (
|
||||
len(enhanced_validation.issues) == 0 and
|
||||
enhanced_validation.quality_score >= 0.7
|
||||
)
|
||||
|
||||
return enhanced_validation
|
||||
|
||||
def _measure_effectiveness(self, context: dict, validation_result: ValidationResult) -> dict:
|
||||
"""Measure operation effectiveness and quality."""
|
||||
effectiveness_metrics = {
|
||||
'overall_effectiveness': 0.0,
|
||||
'quality_score': validation_result.quality_score,
|
||||
'performance_score': 0.0,
|
||||
'user_satisfaction_estimate': 0.0,
|
||||
'learning_value': 0.0,
|
||||
'improvement_potential': 0.0
|
||||
}
|
||||
|
||||
# Performance scoring
|
||||
execution_time = context.get('execution_time_ms', 0)
|
||||
expected_time = context.get('performance_data', {}).get('expected_time_ms', 1000)
|
||||
if execution_time > 0:
|
||||
time_ratio = expected_time / max(execution_time, 1)
|
||||
effectiveness_metrics['performance_score'] = min(time_ratio, 1.0)
|
||||
else:
|
||||
effectiveness_metrics['performance_score'] = 1.0
|
||||
|
||||
# User satisfaction estimation
|
||||
if context.get('success'):
|
||||
base_satisfaction = 0.8
|
||||
if validation_result.quality_score > 0.8:
|
||||
base_satisfaction += 0.15
|
||||
if effectiveness_metrics['performance_score'] > 0.8:
|
||||
base_satisfaction += 0.05
|
||||
effectiveness_metrics['user_satisfaction_estimate'] = min(base_satisfaction, 1.0)
|
||||
else:
|
||||
# Reduce satisfaction based on error severity
|
||||
error_severity = self._assess_error_severity(context)
|
||||
effectiveness_metrics['user_satisfaction_estimate'] = max(0.3 - error_severity * 0.3, 0.0)
|
||||
|
||||
# Learning value assessment
|
||||
if context.get('mcp_servers_used'):
|
||||
effectiveness_metrics['learning_value'] += 0.2 # MCP usage provides learning
|
||||
if context.get('error_occurred'):
|
||||
effectiveness_metrics['learning_value'] += 0.3 # Errors provide valuable learning
|
||||
if context.get('complexity_score', 0) > 0.6:
|
||||
effectiveness_metrics['learning_value'] += 0.2 # Complex operations provide insights
|
||||
|
||||
effectiveness_metrics['learning_value'] = min(effectiveness_metrics['learning_value'], 1.0)
|
||||
|
||||
# Improvement potential
|
||||
if len(validation_result.suggestions) > 0:
|
||||
effectiveness_metrics['improvement_potential'] = min(len(validation_result.suggestions) * 0.2, 1.0)
|
||||
|
||||
# Overall effectiveness calculation
|
||||
weights = {
|
||||
'quality': 0.3,
|
||||
'performance': 0.25,
|
||||
'satisfaction': 0.35,
|
||||
'learning': 0.1
|
||||
}
|
||||
|
||||
effectiveness_metrics['overall_effectiveness'] = (
|
||||
effectiveness_metrics['quality_score'] * weights['quality'] +
|
||||
effectiveness_metrics['performance_score'] * weights['performance'] +
|
||||
effectiveness_metrics['user_satisfaction_estimate'] * weights['satisfaction'] +
|
||||
effectiveness_metrics['learning_value'] * weights['learning']
|
||||
)
|
||||
|
||||
return effectiveness_metrics
|
||||
|
||||
def _assess_error_severity(self, context: dict) -> float:
|
||||
"""Assess error severity on a scale of 0.0 to 1.0."""
|
||||
if not context.get('error_occurred'):
|
||||
return 0.0
|
||||
|
||||
error_type = context.get('error_type', 'unknown')
|
||||
|
||||
severity_map = {
|
||||
'fatal': 1.0,
|
||||
'security': 0.9,
|
||||
'corruption': 0.8,
|
||||
'timeout': 0.6,
|
||||
'validation': 0.4,
|
||||
'warning': 0.2,
|
||||
'unknown': 0.5
|
||||
}
|
||||
|
||||
return severity_map.get(error_type, 0.5)
|
||||
|
||||
def _analyze_learning_opportunities(self, context: dict, effectiveness_metrics: dict) -> dict:
|
||||
"""Analyze learning opportunities from the execution."""
|
||||
learning_analysis = {
|
||||
'patterns_detected': [],
|
||||
'success_factors': [],
|
||||
'failure_factors': [],
|
||||
'optimization_opportunities': [],
|
||||
'adaptation_recommendations': []
|
||||
}
|
||||
|
||||
# Pattern detection
|
||||
if context.get('mcp_servers_used'):
|
||||
for server in context['mcp_servers_used']:
|
||||
if effectiveness_metrics['overall_effectiveness'] > 0.8:
|
||||
learning_analysis['patterns_detected'].append(f"effective_{server}_usage")
|
||||
elif effectiveness_metrics['overall_effectiveness'] < 0.5:
|
||||
learning_analysis['patterns_detected'].append(f"ineffective_{server}_usage")
|
||||
|
||||
# Success factor analysis
|
||||
if effectiveness_metrics['overall_effectiveness'] > 0.8:
|
||||
if effectiveness_metrics['performance_score'] > 0.8:
|
||||
learning_analysis['success_factors'].append('optimal_performance')
|
||||
if effectiveness_metrics['quality_score'] > 0.8:
|
||||
learning_analysis['success_factors'].append('high_quality_output')
|
||||
if context.get('mcp_servers_used'):
|
||||
learning_analysis['success_factors'].append('effective_mcp_coordination')
|
||||
|
||||
# Failure factor analysis
|
||||
if effectiveness_metrics['overall_effectiveness'] < 0.5:
|
||||
if effectiveness_metrics['performance_score'] < 0.5:
|
||||
learning_analysis['failure_factors'].append('poor_performance')
|
||||
if effectiveness_metrics['quality_score'] < 0.5:
|
||||
learning_analysis['failure_factors'].append('quality_issues')
|
||||
if context.get('error_occurred'):
|
||||
learning_analysis['failure_factors'].append(f"error_{context.get('error_type', 'unknown')}")
|
||||
|
||||
# Optimization opportunities
|
||||
if effectiveness_metrics['improvement_potential'] > 0.3:
|
||||
learning_analysis['optimization_opportunities'].append('validation_improvements_available')
|
||||
|
||||
if context.get('execution_time_ms', 0) > 2000:
|
||||
learning_analysis['optimization_opportunities'].append('performance_optimization_needed')
|
||||
|
||||
# Adaptation recommendations
|
||||
if len(learning_analysis['success_factors']) > 0:
|
||||
learning_analysis['adaptation_recommendations'].append(
|
||||
f"Reinforce patterns: {', '.join(learning_analysis['success_factors'])}"
|
||||
)
|
||||
|
||||
if len(learning_analysis['failure_factors']) > 0:
|
||||
learning_analysis['adaptation_recommendations'].append(
|
||||
f"Address failure patterns: {', '.join(learning_analysis['failure_factors'])}"
|
||||
)
|
||||
|
||||
return learning_analysis
|
||||
|
||||
def _record_learning_events(self, context: dict, effectiveness_metrics: dict, learning_analysis: dict):
|
||||
"""Record learning events for future adaptation."""
|
||||
overall_effectiveness = effectiveness_metrics['overall_effectiveness']
|
||||
|
||||
# Record general operation learning
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.OPERATION_PATTERN,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'tool_name': context['tool_name'],
|
||||
'mcp_servers': context.get('mcp_servers_used', []),
|
||||
'success_factors': learning_analysis['success_factors'],
|
||||
'failure_factors': learning_analysis['failure_factors']
|
||||
},
|
||||
overall_effectiveness,
|
||||
0.8, # High confidence in post-execution analysis
|
||||
{'hook': 'post_tool_use', 'effectiveness': overall_effectiveness}
|
||||
)
|
||||
|
||||
# Track tool preference if execution was successful
|
||||
if context.get('success') and overall_effectiveness > 0.7:
|
||||
operation_type = self._categorize_operation(context['tool_name'])
|
||||
if operation_type:
|
||||
self.learning_engine.update_last_preference(
|
||||
f"tool_{operation_type}",
|
||||
context['tool_name']
|
||||
)
|
||||
|
||||
# Record MCP server effectiveness
|
||||
for server in context.get('mcp_servers_used', []):
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.EFFECTIVENESS_FEEDBACK,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{'mcp_server': server},
|
||||
overall_effectiveness,
|
||||
0.9, # Very high confidence in direct feedback
|
||||
{'server_performance': effectiveness_metrics['performance_score']}
|
||||
)
|
||||
|
||||
# Record error patterns if applicable
|
||||
if context.get('error_occurred'):
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.ERROR_RECOVERY,
|
||||
AdaptationScope.PROJECT,
|
||||
context,
|
||||
{
|
||||
'error_type': context.get('error_type'),
|
||||
'recovery_successful': context.get('error_recoverable', False),
|
||||
'context_factors': learning_analysis['failure_factors']
|
||||
},
|
||||
1.0 - self._assess_error_severity(context), # Inverse of severity
|
||||
1.0, # Full confidence in error data
|
||||
{'error_learning': True}
|
||||
)
|
||||
|
||||
def _generate_recommendations(self, context: dict, validation_result: ValidationResult,
|
||||
learning_analysis: dict) -> dict:
|
||||
"""Generate recommendations for improvement."""
|
||||
recommendations = {
|
||||
'immediate_actions': [],
|
||||
'optimization_suggestions': [],
|
||||
'learning_adaptations': [],
|
||||
'prevention_measures': []
|
||||
}
|
||||
|
||||
# Immediate actions from validation issues
|
||||
for issue in validation_result.issues:
|
||||
recommendations['immediate_actions'].append(f"Fix: {issue}")
|
||||
|
||||
for warning in validation_result.warnings:
|
||||
recommendations['immediate_actions'].append(f"Address: {warning}")
|
||||
|
||||
# Optimization suggestions
|
||||
for suggestion in validation_result.suggestions:
|
||||
recommendations['optimization_suggestions'].append(suggestion)
|
||||
|
||||
for opportunity in learning_analysis['optimization_opportunities']:
|
||||
recommendations['optimization_suggestions'].append(f"Optimize: {opportunity}")
|
||||
|
||||
# Learning adaptations
|
||||
for adaptation in learning_analysis['adaptation_recommendations']:
|
||||
recommendations['learning_adaptations'].append(adaptation)
|
||||
|
||||
# Prevention measures for errors
|
||||
if context.get('error_occurred'):
|
||||
error_type = context.get('error_type', 'unknown')
|
||||
if error_type == 'timeout':
|
||||
recommendations['prevention_measures'].append("Consider parallel execution for large operations")
|
||||
elif error_type == 'validation':
|
||||
recommendations['prevention_measures'].append("Enable pre-validation for similar operations")
|
||||
elif error_type == 'security':
|
||||
recommendations['prevention_measures'].append("Implement security validation checks")
|
||||
|
||||
return recommendations
|
||||
|
||||
def _calculate_quality_score(self, context: dict, validation_result: ValidationResult) -> float:
|
||||
"""Calculate quality score based on validation and execution."""
|
||||
base_score = validation_result.quality_score
|
||||
|
||||
# Adjust for execution time
|
||||
execution_time = context.get('execution_time_ms', 0)
|
||||
time_ratio = execution_time / max(self.performance_target_ms, 1)
|
||||
time_penalty = min(time_ratio, 1.0)
|
||||
|
||||
# Initialize error penalty (no penalty when no error occurs)
|
||||
error_penalty = 1.0
|
||||
|
||||
# Adjust for error occurrence
|
||||
if context.get('error_occurred'):
|
||||
error_severity = self._assess_error_severity(context)
|
||||
error_penalty = 1.0 - error_severity
|
||||
|
||||
# Combine adjustments
|
||||
quality_score = base_score * time_penalty * error_penalty
|
||||
|
||||
return quality_score
|
||||
|
||||
def _create_validation_report(self, context: dict, validation_result: ValidationResult,
|
||||
effectiveness_metrics: dict, learning_analysis: dict,
|
||||
recommendations: dict) -> dict:
|
||||
"""Create comprehensive validation report."""
|
||||
return {
|
||||
'tool_name': context['tool_name'],
|
||||
'execution_status': context['execution_status'],
|
||||
'timestamp': context['timestamp'],
|
||||
|
||||
'validation': {
|
||||
'is_valid': validation_result.is_valid,
|
||||
'quality_score': validation_result.quality_score,
|
||||
'issues': validation_result.issues,
|
||||
'warnings': validation_result.warnings,
|
||||
'suggestions': validation_result.suggestions
|
||||
},
|
||||
|
||||
'effectiveness': effectiveness_metrics,
|
||||
|
||||
'learning': {
|
||||
'patterns_detected': learning_analysis['patterns_detected'],
|
||||
'success_factors': learning_analysis['success_factors'],
|
||||
'failure_factors': learning_analysis['failure_factors'],
|
||||
'learning_value': effectiveness_metrics['learning_value']
|
||||
},
|
||||
|
||||
'recommendations': recommendations,
|
||||
|
||||
'compliance': {
|
||||
'rules_compliance': len([i for i in validation_result.issues if 'RULES' in i]) == 0,
|
||||
'principles_alignment': len([w for w in validation_result.warnings if 'PRINCIPLES' in w]) == 0,
|
||||
'superclaude_score': self._calculate_superclaude_compliance_score(validation_result)
|
||||
},
|
||||
|
||||
'metadata': {
|
||||
'hook_version': 'post_tool_use_1.0',
|
||||
'validation_timestamp': time.time(),
|
||||
'learning_events_recorded': len(learning_analysis['patterns_detected']) + 1
|
||||
}
|
||||
}
|
||||
|
||||
def _calculate_superclaude_compliance_score(self, validation_result: ValidationResult) -> float:
|
||||
"""Calculate overall SuperClaude compliance score."""
|
||||
base_score = validation_result.quality_score
|
||||
|
||||
# Penalties for specific violations
|
||||
rules_violations = len([i for i in validation_result.issues if 'RULES' in i])
|
||||
principles_violations = len([w for w in validation_result.warnings if 'PRINCIPLES' in w])
|
||||
|
||||
penalty = (rules_violations * 0.2) + (principles_violations * 0.1)
|
||||
|
||||
return max(base_score - penalty, 0.0)
|
||||
|
||||
def _create_fallback_result(self, tool_result: dict, error: str) -> dict:
|
||||
"""Create fallback validation report on error."""
|
||||
return {
|
||||
'tool_name': tool_result.get('tool_name', 'unknown'),
|
||||
'execution_status': 'validation_error',
|
||||
'timestamp': time.time(),
|
||||
'error': error,
|
||||
'fallback_mode': True,
|
||||
|
||||
'validation': {
|
||||
'is_valid': False,
|
||||
'quality_score': 0.0,
|
||||
'issues': [f"Validation hook error: {error}"],
|
||||
'warnings': [],
|
||||
'suggestions': ['Fix validation hook error']
|
||||
},
|
||||
|
||||
'effectiveness': {
|
||||
'overall_effectiveness': 0.0,
|
||||
'quality_score': 0.0,
|
||||
'performance_score': 0.0,
|
||||
'user_satisfaction_estimate': 0.0,
|
||||
'learning_value': 0.0
|
||||
},
|
||||
|
||||
'performance_metrics': {
|
||||
'processing_time_ms': 0,
|
||||
'target_met': False,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
|
||||
def _analyze_execution_patterns(self, context: dict, validation_result: ValidationResult) -> dict:
|
||||
"""Analyze patterns in tool execution."""
|
||||
pattern_analysis = {
|
||||
'error_pattern_detected': False,
|
||||
'pattern_type': 'unknown',
|
||||
'description': 'No error pattern detected'
|
||||
}
|
||||
|
||||
# Check for error occurrence
|
||||
if context.get('error_occurred'):
|
||||
error_type = context.get('error_type', 'unknown')
|
||||
|
||||
# Check for specific error types
|
||||
if error_type in ['fatal', 'security', 'corruption']:
|
||||
pattern_analysis['error_pattern_detected'] = True
|
||||
pattern_analysis['pattern_type'] = error_type
|
||||
pattern_analysis['description'] = f"Error pattern detected: {error_type}"
|
||||
|
||||
return pattern_analysis
|
||||
|
||||
def _categorize_operation(self, tool_name: str) -> Optional[str]:
|
||||
"""Categorize tool into operation type for preference tracking."""
|
||||
operation_map = {
|
||||
'read': ['Read', 'Get', 'List', 'Search', 'Find'],
|
||||
'write': ['Write', 'Create', 'Generate'],
|
||||
'edit': ['Edit', 'Update', 'Modify', 'Replace'],
|
||||
'analyze': ['Analyze', 'Validate', 'Check', 'Test'],
|
||||
'mcp': ['Context7', 'Sequential', 'Magic', 'Playwright', 'Morphllm', 'Serena']
|
||||
}
|
||||
|
||||
for operation_type, tools in operation_map.items():
|
||||
if any(tool in tool_name for tool in tools):
|
||||
return operation_type
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def main():
|
||||
"""Main hook execution function."""
|
||||
try:
|
||||
# Read tool result from stdin
|
||||
tool_result = json.loads(sys.stdin.read())
|
||||
|
||||
# Initialize and run hook
|
||||
hook = PostToolUseHook()
|
||||
result = hook.process_tool_result(tool_result)
|
||||
|
||||
# Output result as JSON
|
||||
print(json.dumps(result, indent=2))
|
||||
|
||||
except Exception as e:
|
||||
# Output error as JSON
|
||||
error_result = {
|
||||
'validation_error': True,
|
||||
'error': str(e),
|
||||
'fallback_mode': True
|
||||
}
|
||||
print(json.dumps(error_result, indent=2))
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,773 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
SuperClaude-Lite Pre-Compact Hook
|
||||
|
||||
Implements MODE_Token_Efficiency.md compression algorithms for intelligent context optimization.
|
||||
Performance target: <150ms execution time.
|
||||
|
||||
This hook runs before context compaction and provides:
|
||||
- Intelligent compression strategy selection
|
||||
- Selective content preservation with framework exclusion
|
||||
- Symbol systems and abbreviation optimization
|
||||
- Quality-gated compression with ≥95% information preservation
|
||||
- Adaptive compression based on resource constraints
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional, Tuple
|
||||
|
||||
# Add shared modules to path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
|
||||
|
||||
from framework_logic import FrameworkLogic
|
||||
from pattern_detection import PatternDetector
|
||||
from mcp_intelligence import MCPIntelligence
|
||||
from compression_engine import (
|
||||
CompressionEngine, CompressionLevel, ContentType, CompressionResult, CompressionStrategy
|
||||
)
|
||||
from learning_engine import LearningEngine, LearningType, AdaptationScope
|
||||
from yaml_loader import config_loader
|
||||
from logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
|
||||
|
||||
class PreCompactHook:
|
||||
"""
|
||||
Pre-compact hook implementing SuperClaude token efficiency intelligence.
|
||||
|
||||
Responsibilities:
|
||||
- Analyze context for compression opportunities
|
||||
- Apply selective compression with framework protection
|
||||
- Implement symbol systems and abbreviation optimization
|
||||
- Maintain ≥95% information preservation quality
|
||||
- Adapt compression strategy based on resource constraints
|
||||
- Learn from compression effectiveness and user preferences
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
start_time = time.time()
|
||||
|
||||
# Initialize core components
|
||||
self.framework_logic = FrameworkLogic()
|
||||
self.pattern_detector = PatternDetector()
|
||||
self.mcp_intelligence = MCPIntelligence()
|
||||
self.compression_engine = CompressionEngine()
|
||||
|
||||
# Initialize learning engine with installation directory cache
|
||||
import os
|
||||
cache_dir = Path(os.path.expanduser("~/.claude/cache"))
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.learning_engine = LearningEngine(cache_dir)
|
||||
|
||||
# Load hook-specific configuration from SuperClaude config
|
||||
self.hook_config = config_loader.get_hook_config('pre_compact')
|
||||
|
||||
# Load compression configuration (from YAML if exists, otherwise use hook config)
|
||||
try:
|
||||
self.compression_config = config_loader.load_config('compression')
|
||||
except FileNotFoundError:
|
||||
# Fall back to hook configuration if YAML file not found
|
||||
self.compression_config = self.hook_config.get('configuration', {})
|
||||
|
||||
# Performance tracking using configuration
|
||||
self.initialization_time = (time.time() - start_time) * 1000
|
||||
self.performance_target_ms = config_loader.get_hook_config('pre_compact', 'performance_target_ms', 150)
|
||||
|
||||
def process_pre_compact(self, compact_request: dict) -> dict:
|
||||
"""
|
||||
Process pre-compact request with intelligent compression.
|
||||
|
||||
Args:
|
||||
compact_request: Context compaction request from Claude Code
|
||||
|
||||
Returns:
|
||||
Compression configuration and optimized content strategy
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# Log hook start
|
||||
log_hook_start("pre_compact", {
|
||||
"session_id": compact_request.get('session_id', ''),
|
||||
"content_size": len(compact_request.get('content', '')),
|
||||
"resource_state": compact_request.get('resource_state', {}),
|
||||
"triggers": compact_request.get('triggers', [])
|
||||
})
|
||||
|
||||
try:
|
||||
# Extract compression context
|
||||
context = self._extract_compression_context(compact_request)
|
||||
|
||||
# Analyze content for compression strategy
|
||||
content_analysis = self._analyze_content_for_compression(context)
|
||||
|
||||
# Determine optimal compression strategy
|
||||
compression_strategy = self._determine_compression_strategy(context, content_analysis)
|
||||
|
||||
# Log compression strategy decision
|
||||
log_decision(
|
||||
"pre_compact",
|
||||
"compression_strategy",
|
||||
compression_strategy.level.value,
|
||||
f"Based on resource usage: {context.get('token_usage_percent', 0)}%, content type: {content_analysis['content_type'].value}"
|
||||
)
|
||||
|
||||
# Apply selective compression with framework protection
|
||||
compression_results = self._apply_selective_compression(
|
||||
context, compression_strategy, content_analysis
|
||||
)
|
||||
|
||||
# Validate compression quality
|
||||
quality_validation = self._validate_compression_quality(
|
||||
compression_results, compression_strategy
|
||||
)
|
||||
|
||||
# Log quality validation results
|
||||
if not quality_validation['overall_quality_met']:
|
||||
log_decision(
|
||||
"pre_compact",
|
||||
"quality_validation",
|
||||
"failed",
|
||||
f"Preservation score: {quality_validation['preservation_score']:.2f}, Issues: {', '.join(quality_validation['quality_issues'])}"
|
||||
)
|
||||
|
||||
# Record learning events
|
||||
self._record_compression_learning(context, compression_results, quality_validation)
|
||||
|
||||
# Generate compression configuration
|
||||
compression_config = self._generate_compression_config(
|
||||
context, compression_strategy, compression_results, quality_validation
|
||||
)
|
||||
|
||||
# Performance tracking
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
compression_config['performance_metrics'] = {
|
||||
'compression_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'efficiency_score': self._calculate_compression_efficiency(context, execution_time)
|
||||
}
|
||||
|
||||
# Log compression results
|
||||
log_decision(
|
||||
"pre_compact",
|
||||
"compression_results",
|
||||
f"{compression_config['results']['compression_ratio']:.1%}",
|
||||
f"Saved {compression_config['optimization']['estimated_token_savings']} tokens"
|
||||
)
|
||||
|
||||
# Log hook end
|
||||
log_hook_end(
|
||||
"pre_compact",
|
||||
int(execution_time),
|
||||
True,
|
||||
{
|
||||
"compression_ratio": compression_config['results']['compression_ratio'],
|
||||
"preservation_score": compression_config['quality']['preservation_score'],
|
||||
"token_savings": compression_config['optimization']['estimated_token_savings'],
|
||||
"performance_target_met": execution_time < self.performance_target_ms
|
||||
}
|
||||
)
|
||||
|
||||
return compression_config
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
log_error("pre_compact", str(e), {"request": compact_request})
|
||||
|
||||
# Log hook end with failure
|
||||
log_hook_end("pre_compact", int((time.time() - start_time) * 1000), False)
|
||||
|
||||
# Graceful fallback on error
|
||||
return self._create_fallback_compression_config(compact_request, str(e))
|
||||
|
||||
def _extract_compression_context(self, compact_request: dict) -> dict:
|
||||
"""Extract and enrich compression context."""
|
||||
context = {
|
||||
'session_id': compact_request.get('session_id', ''),
|
||||
'content_to_compress': compact_request.get('content', ''),
|
||||
'content_metadata': compact_request.get('metadata', {}),
|
||||
'resource_constraints': compact_request.get('resource_state', {}),
|
||||
'user_preferences': compact_request.get('user_preferences', {}),
|
||||
'compression_triggers': compact_request.get('triggers', []),
|
||||
'previous_compressions': compact_request.get('compression_history', []),
|
||||
'session_context': compact_request.get('session_context', {}),
|
||||
'timestamp': time.time()
|
||||
}
|
||||
|
||||
# Analyze content characteristics
|
||||
context.update(self._analyze_content_characteristics(context))
|
||||
|
||||
# Extract resource state
|
||||
context.update(self._extract_resource_state(context))
|
||||
|
||||
return context
|
||||
|
||||
def _analyze_content_characteristics(self, context: dict) -> dict:
|
||||
"""Analyze content characteristics for compression decisions."""
|
||||
content = context.get('content_to_compress', '')
|
||||
metadata = context.get('content_metadata', {})
|
||||
|
||||
characteristics = {
|
||||
'content_length': len(content),
|
||||
'content_complexity': 0.0,
|
||||
'repetition_factor': 0.0,
|
||||
'technical_density': 0.0,
|
||||
'framework_content_ratio': 0.0,
|
||||
'user_content_ratio': 0.0,
|
||||
'compressibility_score': 0.0
|
||||
}
|
||||
|
||||
if not content:
|
||||
return characteristics
|
||||
|
||||
# Content complexity analysis
|
||||
lines = content.split('\n')
|
||||
characteristics['content_complexity'] = self._calculate_content_complexity(content, lines)
|
||||
|
||||
# Repetition analysis
|
||||
characteristics['repetition_factor'] = self._calculate_repetition_factor(content, lines)
|
||||
|
||||
# Technical density
|
||||
characteristics['technical_density'] = self._calculate_technical_density(content)
|
||||
|
||||
# Framework vs user content ratio
|
||||
framework_ratio, user_ratio = self._analyze_content_sources(content, metadata)
|
||||
characteristics['framework_content_ratio'] = framework_ratio
|
||||
characteristics['user_content_ratio'] = user_ratio
|
||||
|
||||
# Overall compressibility score
|
||||
characteristics['compressibility_score'] = self._calculate_compressibility_score(characteristics)
|
||||
|
||||
return characteristics
|
||||
|
||||
def _calculate_content_complexity(self, content: str, lines: List[str]) -> float:
|
||||
"""Calculate content complexity score (0.0 to 1.0)."""
|
||||
complexity_indicators = [
|
||||
len([line for line in lines if len(line) > 100]) / max(len(lines), 1), # Long lines
|
||||
len([char for char in content if char in '{}[]()']) / max(len(content), 1), # Structural chars
|
||||
len(set(content.split())) / max(len(content.split()), 1), # Vocabulary richness
|
||||
]
|
||||
|
||||
return min(sum(complexity_indicators) / len(complexity_indicators), 1.0)
|
||||
|
||||
def _calculate_repetition_factor(self, content: str, lines: List[str]) -> float:
|
||||
"""Calculate repetition factor for compression potential."""
|
||||
if not lines:
|
||||
return 0.0
|
||||
|
||||
# Line repetition
|
||||
unique_lines = len(set(lines))
|
||||
line_repetition = 1.0 - (unique_lines / len(lines))
|
||||
|
||||
# Word repetition
|
||||
words = content.split()
|
||||
if words:
|
||||
unique_words = len(set(words))
|
||||
word_repetition = 1.0 - (unique_words / len(words))
|
||||
else:
|
||||
word_repetition = 0.0
|
||||
|
||||
return (line_repetition + word_repetition) / 2
|
||||
|
||||
def _calculate_technical_density(self, content: str) -> float:
|
||||
"""Calculate technical density for compression strategy."""
|
||||
technical_patterns = [
|
||||
r'\b[A-Z][a-zA-Z]*\b', # CamelCase
|
||||
r'\b\w+\.\w+\b', # Dotted notation
|
||||
r'\b\d+\.\d+\.\d+\b', # Version numbers
|
||||
r'\b[a-z]+_[a-z]+\b', # Snake_case
|
||||
r'\b[A-Z]{2,}\b', # CONSTANTS
|
||||
]
|
||||
|
||||
import re
|
||||
technical_matches = 0
|
||||
for pattern in technical_patterns:
|
||||
technical_matches += len(re.findall(pattern, content))
|
||||
|
||||
total_words = len(content.split())
|
||||
return min(technical_matches / max(total_words, 1), 1.0)
|
||||
|
||||
def _analyze_content_sources(self, content: str, metadata: dict) -> Tuple[float, float]:
|
||||
"""Analyze ratio of framework vs user content."""
|
||||
# Framework content indicators
|
||||
framework_indicators = [
|
||||
'SuperClaude', 'CLAUDE.md', 'FLAGS.md', 'PRINCIPLES.md',
|
||||
'ORCHESTRATOR.md', 'MCP_', 'MODE_', 'SESSION_LIFECYCLE'
|
||||
]
|
||||
|
||||
# User content indicators
|
||||
user_indicators = [
|
||||
'project_files', 'user_documentation', 'source_code',
|
||||
'configuration_files', 'custom_content'
|
||||
]
|
||||
|
||||
framework_score = 0
|
||||
user_score = 0
|
||||
|
||||
# Check content text
|
||||
content_lower = content.lower()
|
||||
for indicator in framework_indicators:
|
||||
if indicator.lower() in content_lower:
|
||||
framework_score += 1
|
||||
|
||||
for indicator in user_indicators:
|
||||
if indicator.lower() in content_lower:
|
||||
user_score += 1
|
||||
|
||||
# Check metadata
|
||||
content_type = metadata.get('content_type', '')
|
||||
file_path = metadata.get('file_path', '')
|
||||
|
||||
if any(pattern in file_path for pattern in ['/.claude/', 'framework']):
|
||||
framework_score += 3
|
||||
|
||||
if any(pattern in content_type for pattern in user_indicators):
|
||||
user_score += 3
|
||||
|
||||
total_score = framework_score + user_score
|
||||
if total_score == 0:
|
||||
return 0.5, 0.5 # Unknown, assume mixed
|
||||
|
||||
return framework_score / total_score, user_score / total_score
|
||||
|
||||
def _calculate_compressibility_score(self, characteristics: dict) -> float:
|
||||
"""Calculate overall compressibility score."""
|
||||
# Higher repetition = higher compressibility
|
||||
repetition_contribution = characteristics['repetition_factor'] * 0.4
|
||||
|
||||
# Higher technical density = better compression with abbreviations
|
||||
technical_contribution = characteristics['technical_density'] * 0.3
|
||||
|
||||
# Framework content is not compressed (exclusion)
|
||||
framework_penalty = characteristics['framework_content_ratio'] * 0.5
|
||||
|
||||
# Content complexity affects compression effectiveness
|
||||
complexity_factor = 1.0 - (characteristics['content_complexity'] * 0.2)
|
||||
|
||||
score = (repetition_contribution + technical_contribution) * complexity_factor - framework_penalty
|
||||
|
||||
return max(min(score, 1.0), 0.0)
|
||||
|
||||
def _extract_resource_state(self, context: dict) -> dict:
|
||||
"""Extract resource state for compression decisions."""
|
||||
resource_constraints = context.get('resource_constraints', {})
|
||||
|
||||
return {
|
||||
'memory_usage_percent': resource_constraints.get('memory_usage', 0),
|
||||
'token_usage_percent': resource_constraints.get('token_usage', 0),
|
||||
'conversation_length': resource_constraints.get('conversation_length', 0),
|
||||
'resource_pressure': resource_constraints.get('pressure_level', 'normal'),
|
||||
'user_requests_compression': resource_constraints.get('user_compression_request', False)
|
||||
}
|
||||
|
||||
def _analyze_content_for_compression(self, context: dict) -> dict:
|
||||
"""Analyze content to determine compression approach."""
|
||||
content = context.get('content_to_compress', '')
|
||||
metadata = context.get('content_metadata', {})
|
||||
|
||||
# Classify content type
|
||||
content_type = self.compression_engine.classify_content(content, metadata)
|
||||
|
||||
# Analyze compression opportunities
|
||||
analysis = {
|
||||
'content_type': content_type,
|
||||
'compression_opportunities': [],
|
||||
'preservation_requirements': [],
|
||||
'optimization_techniques': []
|
||||
}
|
||||
|
||||
# Framework content - complete exclusion
|
||||
if content_type == ContentType.FRAMEWORK_CONTENT:
|
||||
analysis['preservation_requirements'].append('complete_exclusion')
|
||||
analysis['compression_opportunities'] = []
|
||||
log_decision(
|
||||
"pre_compact",
|
||||
"content_classification",
|
||||
"framework_content",
|
||||
"Complete exclusion from compression - framework protection"
|
||||
)
|
||||
return analysis
|
||||
|
||||
# User content - minimal compression only
|
||||
if content_type == ContentType.USER_CONTENT:
|
||||
analysis['preservation_requirements'].extend([
|
||||
'high_fidelity_preservation',
|
||||
'minimal_compression_only'
|
||||
])
|
||||
analysis['compression_opportunities'].append('whitespace_optimization')
|
||||
log_decision(
|
||||
"pre_compact",
|
||||
"content_classification",
|
||||
"user_content",
|
||||
"Minimal compression only - user content preservation"
|
||||
)
|
||||
return analysis
|
||||
|
||||
# Session/working data - full compression applicable
|
||||
compressibility = context.get('compressibility_score', 0.0)
|
||||
|
||||
if compressibility > 0.7:
|
||||
analysis['compression_opportunities'].extend([
|
||||
'symbol_systems',
|
||||
'abbreviation_systems',
|
||||
'structural_optimization',
|
||||
'redundancy_removal'
|
||||
])
|
||||
elif compressibility > 0.4:
|
||||
analysis['compression_opportunities'].extend([
|
||||
'symbol_systems',
|
||||
'structural_optimization'
|
||||
])
|
||||
else:
|
||||
analysis['compression_opportunities'].append('minimal_optimization')
|
||||
|
||||
# Technical content optimization
|
||||
if context.get('technical_density', 0) > 0.6:
|
||||
analysis['optimization_techniques'].append('technical_abbreviations')
|
||||
|
||||
# Repetitive content optimization
|
||||
if context.get('repetition_factor', 0) > 0.5:
|
||||
analysis['optimization_techniques'].append('pattern_compression')
|
||||
|
||||
return analysis
|
||||
|
||||
def _determine_compression_strategy(self, context: dict, content_analysis: dict) -> CompressionStrategy:
|
||||
"""Determine optimal compression strategy."""
|
||||
# Determine compression level based on resource state
|
||||
compression_level = self.compression_engine.determine_compression_level({
|
||||
'resource_usage_percent': context.get('token_usage_percent', 0),
|
||||
'conversation_length': context.get('conversation_length', 0),
|
||||
'user_requests_brevity': context.get('user_requests_compression', False),
|
||||
'complexity_score': context.get('content_complexity', 0.0)
|
||||
})
|
||||
|
||||
# Adjust for content type
|
||||
content_type = content_analysis['content_type']
|
||||
if content_type == ContentType.FRAMEWORK_CONTENT:
|
||||
compression_level = CompressionLevel.MINIMAL # Actually no compression
|
||||
elif content_type == ContentType.USER_CONTENT:
|
||||
compression_level = CompressionLevel.MINIMAL
|
||||
|
||||
# Create strategy
|
||||
strategy = self.compression_engine._create_compression_strategy(compression_level, content_type)
|
||||
|
||||
# Customize based on content analysis
|
||||
opportunities = content_analysis.get('compression_opportunities', [])
|
||||
|
||||
if 'symbol_systems' not in opportunities:
|
||||
strategy.symbol_systems_enabled = False
|
||||
if 'abbreviation_systems' not in opportunities:
|
||||
strategy.abbreviation_systems_enabled = False
|
||||
if 'structural_optimization' not in opportunities:
|
||||
strategy.structural_optimization = False
|
||||
|
||||
return strategy
|
||||
|
||||
def _apply_selective_compression(self, context: dict, strategy: CompressionStrategy,
|
||||
content_analysis: dict) -> Dict[str, CompressionResult]:
|
||||
"""Apply selective compression with framework protection."""
|
||||
content = context.get('content_to_compress', '')
|
||||
metadata = context.get('content_metadata', {})
|
||||
|
||||
# Split content into sections for selective processing
|
||||
content_sections = self._split_content_into_sections(content, metadata)
|
||||
|
||||
compression_results = {}
|
||||
|
||||
for section_name, section_data in content_sections.items():
|
||||
section_content = section_data['content']
|
||||
section_metadata = section_data['metadata']
|
||||
|
||||
# Apply compression to each section
|
||||
result = self.compression_engine.compress_content(
|
||||
section_content,
|
||||
context,
|
||||
section_metadata
|
||||
)
|
||||
|
||||
compression_results[section_name] = result
|
||||
|
||||
return compression_results
|
||||
|
||||
def _split_content_into_sections(self, content: str, metadata: dict) -> dict:
|
||||
"""Split content into sections for selective compression."""
|
||||
sections = {}
|
||||
|
||||
# Simple splitting strategy - can be enhanced
|
||||
lines = content.split('\n')
|
||||
|
||||
# Detect different content types within the text
|
||||
current_section = 'default'
|
||||
current_content = []
|
||||
|
||||
for line in lines:
|
||||
# Framework content detection
|
||||
if any(indicator in line for indicator in ['SuperClaude', 'CLAUDE.md', 'FLAGS.md']):
|
||||
if current_content and current_section != 'framework':
|
||||
sections[current_section] = {
|
||||
'content': '\n'.join(current_content),
|
||||
'metadata': {**metadata, 'content_type': current_section}
|
||||
}
|
||||
current_content = []
|
||||
current_section = 'framework'
|
||||
|
||||
# User code detection
|
||||
elif any(indicator in line for indicator in ['def ', 'class ', 'function', 'import ']):
|
||||
if current_content and current_section != 'user_code':
|
||||
sections[current_section] = {
|
||||
'content': '\n'.join(current_content),
|
||||
'metadata': {**metadata, 'content_type': current_section}
|
||||
}
|
||||
current_content = []
|
||||
current_section = 'user_code'
|
||||
|
||||
# Session data detection
|
||||
elif any(indicator in line for indicator in ['session_', 'checkpoint_', 'cache_']):
|
||||
if current_content and current_section != 'session_data':
|
||||
sections[current_section] = {
|
||||
'content': '\n'.join(current_content),
|
||||
'metadata': {**metadata, 'content_type': current_section}
|
||||
}
|
||||
current_content = []
|
||||
current_section = 'session_data'
|
||||
|
||||
current_content.append(line)
|
||||
|
||||
# Add final section
|
||||
if current_content:
|
||||
sections[current_section] = {
|
||||
'content': '\n'.join(current_content),
|
||||
'metadata': {**metadata, 'content_type': current_section}
|
||||
}
|
||||
|
||||
# If no sections detected, treat as single section
|
||||
if not sections:
|
||||
sections['default'] = {
|
||||
'content': content,
|
||||
'metadata': metadata
|
||||
}
|
||||
|
||||
return sections
|
||||
|
||||
def _validate_compression_quality(self, compression_results: Dict[str, CompressionResult],
|
||||
strategy: CompressionStrategy) -> dict:
|
||||
"""Validate compression quality against standards."""
|
||||
validation = {
|
||||
'overall_quality_met': True,
|
||||
'preservation_score': 0.0,
|
||||
'compression_efficiency': 0.0,
|
||||
'quality_issues': [],
|
||||
'quality_warnings': []
|
||||
}
|
||||
|
||||
if not compression_results:
|
||||
return validation
|
||||
|
||||
# Calculate overall metrics
|
||||
total_original = sum(result.original_length for result in compression_results.values())
|
||||
total_compressed = sum(result.compressed_length for result in compression_results.values())
|
||||
total_preservation = sum(result.preservation_score for result in compression_results.values())
|
||||
|
||||
if total_original > 0:
|
||||
validation['compression_efficiency'] = (total_original - total_compressed) / total_original
|
||||
|
||||
validation['preservation_score'] = total_preservation / len(compression_results)
|
||||
|
||||
# Quality threshold validation
|
||||
if validation['preservation_score'] < strategy.quality_threshold:
|
||||
validation['overall_quality_met'] = False
|
||||
validation['quality_issues'].append(
|
||||
f"Preservation score {validation['preservation_score']:.2f} below threshold {strategy.quality_threshold}"
|
||||
)
|
||||
|
||||
# Individual section validation
|
||||
for section_name, result in compression_results.items():
|
||||
if result.quality_score < 0.8:
|
||||
validation['quality_warnings'].append(
|
||||
f"Section '{section_name}' quality score low: {result.quality_score:.2f}"
|
||||
)
|
||||
|
||||
if result.compression_ratio > 0.9: # Over 90% compression might be too aggressive
|
||||
validation['quality_warnings'].append(
|
||||
f"Section '{section_name}' compression ratio very high: {result.compression_ratio:.2f}"
|
||||
)
|
||||
|
||||
return validation
|
||||
|
||||
def _record_compression_learning(self, context: dict, compression_results: Dict[str, CompressionResult],
|
||||
quality_validation: dict):
|
||||
"""Record compression learning for future optimization."""
|
||||
overall_effectiveness = quality_validation['preservation_score'] * quality_validation['compression_efficiency']
|
||||
|
||||
# Record compression effectiveness
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.PERFORMANCE_OPTIMIZATION,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'compression_level': self.compression_engine.determine_compression_level(context).value,
|
||||
'techniques_used': list(set().union(*[result.techniques_used for result in compression_results.values()])),
|
||||
'preservation_score': quality_validation['preservation_score'],
|
||||
'compression_efficiency': quality_validation['compression_efficiency']
|
||||
},
|
||||
overall_effectiveness,
|
||||
0.9, # High confidence in compression metrics
|
||||
{'hook': 'pre_compact', 'compression_learning': True}
|
||||
)
|
||||
|
||||
# Record user preference if compression was requested
|
||||
if context.get('user_requests_compression'):
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.USER_PREFERENCE,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{'compression_preference': 'enabled', 'user_satisfaction': overall_effectiveness},
|
||||
overall_effectiveness,
|
||||
0.8,
|
||||
{'user_initiated_compression': True}
|
||||
)
|
||||
|
||||
def _calculate_compression_efficiency(self, context: dict, execution_time_ms: float) -> float:
|
||||
"""Calculate compression processing efficiency."""
|
||||
content_length = context.get('content_length', 1)
|
||||
|
||||
# Efficiency based on processing speed per character
|
||||
chars_per_ms = content_length / max(execution_time_ms, 1)
|
||||
|
||||
# Target: 100 chars per ms for good efficiency
|
||||
target_chars_per_ms = 100
|
||||
efficiency = min(chars_per_ms / target_chars_per_ms, 1.0)
|
||||
|
||||
return efficiency
|
||||
|
||||
def _generate_compression_config(self, context: dict, strategy: CompressionStrategy,
|
||||
compression_results: Dict[str, CompressionResult],
|
||||
quality_validation: dict) -> dict:
|
||||
"""Generate comprehensive compression configuration."""
|
||||
total_original = sum(result.original_length for result in compression_results.values())
|
||||
total_compressed = sum(result.compressed_length for result in compression_results.values())
|
||||
|
||||
config = {
|
||||
'compression_enabled': True,
|
||||
'compression_level': strategy.level.value,
|
||||
'selective_compression': True,
|
||||
|
||||
'strategy': {
|
||||
'symbol_systems_enabled': strategy.symbol_systems_enabled,
|
||||
'abbreviation_systems_enabled': strategy.abbreviation_systems_enabled,
|
||||
'structural_optimization': strategy.structural_optimization,
|
||||
'quality_threshold': strategy.quality_threshold
|
||||
},
|
||||
|
||||
'results': {
|
||||
'original_length': total_original,
|
||||
'compressed_length': total_compressed,
|
||||
'compression_ratio': (total_original - total_compressed) / max(total_original, 1),
|
||||
'sections_processed': len(compression_results),
|
||||
'techniques_used': list(set().union(*[result.techniques_used for result in compression_results.values()]))
|
||||
},
|
||||
|
||||
'quality': {
|
||||
'preservation_score': quality_validation['preservation_score'],
|
||||
'quality_met': quality_validation['overall_quality_met'],
|
||||
'issues': quality_validation['quality_issues'],
|
||||
'warnings': quality_validation['quality_warnings']
|
||||
},
|
||||
|
||||
'framework_protection': {
|
||||
'framework_content_excluded': True,
|
||||
'user_content_preserved': True,
|
||||
'selective_processing_enabled': True
|
||||
},
|
||||
|
||||
'optimization': {
|
||||
'estimated_token_savings': int((total_original - total_compressed) * 0.7), # Rough estimate
|
||||
'processing_efficiency': quality_validation['compression_efficiency'],
|
||||
'recommendation': self._get_compression_recommendation(context, quality_validation)
|
||||
},
|
||||
|
||||
'metadata': {
|
||||
'hook_version': 'pre_compact_1.0',
|
||||
'compression_timestamp': context['timestamp'],
|
||||
'content_classification': 'selective_compression_applied'
|
||||
}
|
||||
}
|
||||
|
||||
return config
|
||||
|
||||
def _get_compression_recommendation(self, context: dict, quality_validation: dict) -> str:
|
||||
"""Get compression recommendation based on results."""
|
||||
if not quality_validation['overall_quality_met']:
|
||||
return "Reduce compression level to maintain quality"
|
||||
elif quality_validation['compression_efficiency'] > 0.7:
|
||||
return "Excellent compression efficiency achieved"
|
||||
elif quality_validation['compression_efficiency'] > 0.4:
|
||||
return "Good compression efficiency, consider slight optimization"
|
||||
else:
|
||||
return "Low compression efficiency, consider alternative strategies"
|
||||
|
||||
def _create_fallback_compression_config(self, compact_request: dict, error: str) -> dict:
|
||||
"""Create fallback compression configuration on error."""
|
||||
return {
|
||||
'compression_enabled': False,
|
||||
'fallback_mode': True,
|
||||
'error': error,
|
||||
|
||||
'strategy': {
|
||||
'symbol_systems_enabled': False,
|
||||
'abbreviation_systems_enabled': False,
|
||||
'structural_optimization': False,
|
||||
'quality_threshold': 1.0
|
||||
},
|
||||
|
||||
'results': {
|
||||
'original_length': len(compact_request.get('content', '')),
|
||||
'compressed_length': len(compact_request.get('content', '')),
|
||||
'compression_ratio': 0.0,
|
||||
'sections_processed': 0,
|
||||
'techniques_used': []
|
||||
},
|
||||
|
||||
'quality': {
|
||||
'preservation_score': 1.0,
|
||||
'quality_met': False,
|
||||
'issues': [f"Compression hook error: {error}"],
|
||||
'warnings': []
|
||||
},
|
||||
|
||||
'performance_metrics': {
|
||||
'compression_time_ms': 0,
|
||||
'target_met': False,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
"""Main hook execution function."""
|
||||
try:
|
||||
# Read compact request from stdin
|
||||
compact_request = json.loads(sys.stdin.read())
|
||||
|
||||
# Initialize and run hook
|
||||
hook = PreCompactHook()
|
||||
result = hook.process_pre_compact(compact_request)
|
||||
|
||||
# Output result as JSON
|
||||
print(json.dumps(result, indent=2))
|
||||
|
||||
except Exception as e:
|
||||
# Output error as JSON
|
||||
error_result = {
|
||||
'compression_enabled': False,
|
||||
'error': str(e),
|
||||
'fallback_mode': True
|
||||
}
|
||||
print(json.dumps(error_result, indent=2))
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,648 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
SuperClaude-Lite Pre-Tool-Use Hook
|
||||
|
||||
Implements ORCHESTRATOR.md + MCP routing intelligence for optimal tool selection.
|
||||
Performance target: <200ms execution time.
|
||||
|
||||
This hook runs before every tool usage and provides:
|
||||
- Intelligent tool routing and MCP server selection
|
||||
- Performance optimization and parallel execution planning
|
||||
- Context-aware tool configuration
|
||||
- Fallback strategy implementation
|
||||
- Real-time adaptation based on effectiveness
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional
|
||||
|
||||
# Add shared modules to path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
|
||||
|
||||
from framework_logic import FrameworkLogic, OperationContext, OperationType, RiskLevel
|
||||
from pattern_detection import PatternDetector, PatternMatch
|
||||
from mcp_intelligence import MCPIntelligence, MCPActivationPlan
|
||||
from compression_engine import CompressionEngine
|
||||
from learning_engine import LearningEngine, LearningType, AdaptationScope
|
||||
from yaml_loader import config_loader
|
||||
from logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
|
||||
|
||||
class PreToolUseHook:
|
||||
"""
|
||||
Pre-tool-use hook implementing SuperClaude orchestration intelligence.
|
||||
|
||||
Responsibilities:
|
||||
- Analyze tool usage context and requirements
|
||||
- Route to optimal MCP servers based on capability matching
|
||||
- Configure parallel execution and performance optimization
|
||||
- Apply learned adaptations for tool selection
|
||||
- Implement fallback strategies for server failures
|
||||
- Track tool effectiveness and performance metrics
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
start_time = time.time()
|
||||
|
||||
# Initialize core components
|
||||
self.framework_logic = FrameworkLogic()
|
||||
self.pattern_detector = PatternDetector()
|
||||
self.mcp_intelligence = MCPIntelligence()
|
||||
self.compression_engine = CompressionEngine()
|
||||
|
||||
# Initialize learning engine with installation directory cache
|
||||
import os
|
||||
cache_dir = Path(os.path.expanduser("~/.claude/cache"))
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.learning_engine = LearningEngine(cache_dir)
|
||||
|
||||
# Load hook-specific configuration from SuperClaude config
|
||||
self.hook_config = config_loader.get_hook_config('pre_tool_use')
|
||||
|
||||
# Load orchestrator configuration (from YAML if exists, otherwise use hook config)
|
||||
try:
|
||||
self.orchestrator_config = config_loader.load_config('orchestrator')
|
||||
except FileNotFoundError:
|
||||
# Fall back to hook configuration if YAML file not found
|
||||
self.orchestrator_config = self.hook_config.get('configuration', {})
|
||||
|
||||
# Load performance configuration (from YAML if exists, otherwise use hook config)
|
||||
try:
|
||||
self.performance_config = config_loader.load_config('performance')
|
||||
except FileNotFoundError:
|
||||
# Fall back to performance targets from global configuration
|
||||
self.performance_config = config_loader.get_performance_targets()
|
||||
|
||||
# Performance tracking using configuration
|
||||
self.initialization_time = (time.time() - start_time) * 1000
|
||||
self.performance_target_ms = config_loader.get_hook_config('pre_tool_use', 'performance_target_ms', 200)
|
||||
|
||||
def process_tool_use(self, tool_request: dict) -> dict:
|
||||
"""
|
||||
Process tool use request with intelligent routing.
|
||||
|
||||
Args:
|
||||
tool_request: Tool usage request from Claude Code
|
||||
|
||||
Returns:
|
||||
Enhanced tool configuration with SuperClaude intelligence
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# Log hook start
|
||||
log_hook_start("pre_tool_use", {
|
||||
"tool_name": tool_request.get('tool_name', 'unknown'),
|
||||
"has_parameters": bool(tool_request.get('parameters'))
|
||||
})
|
||||
|
||||
try:
|
||||
# Extract tool context
|
||||
context = self._extract_tool_context(tool_request)
|
||||
|
||||
# Analyze tool requirements and capabilities
|
||||
requirements = self._analyze_tool_requirements(context)
|
||||
|
||||
# Log routing decision
|
||||
if requirements.get('mcp_server_hints'):
|
||||
log_decision(
|
||||
"pre_tool_use",
|
||||
"mcp_server_selection",
|
||||
",".join(requirements['mcp_server_hints']),
|
||||
f"Tool '{context['tool_name']}' requires capabilities: {', '.join(requirements.get('capabilities_needed', []))}"
|
||||
)
|
||||
|
||||
# Detect patterns for intelligent routing
|
||||
routing_analysis = self._analyze_routing_patterns(context, requirements)
|
||||
|
||||
# Apply learned adaptations
|
||||
enhanced_routing = self._apply_routing_adaptations(context, routing_analysis)
|
||||
|
||||
# Create optimal execution plan
|
||||
execution_plan = self._create_execution_plan(context, enhanced_routing)
|
||||
|
||||
# Log execution strategy decision
|
||||
log_decision(
|
||||
"pre_tool_use",
|
||||
"execution_strategy",
|
||||
execution_plan['execution_strategy'],
|
||||
f"Complexity: {context.get('complexity_score', 0):.2f}, Files: {context.get('file_count', 1)}"
|
||||
)
|
||||
|
||||
# Configure tool enhancement
|
||||
tool_config = self._configure_tool_enhancement(context, execution_plan)
|
||||
|
||||
# Record learning event
|
||||
self._record_tool_learning(context, tool_config)
|
||||
|
||||
# Performance validation
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
tool_config['performance_metrics'] = {
|
||||
'routing_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'efficiency_score': self._calculate_efficiency_score(context, execution_time)
|
||||
}
|
||||
|
||||
# Log successful completion
|
||||
log_hook_end(
|
||||
"pre_tool_use",
|
||||
int(execution_time),
|
||||
True,
|
||||
{
|
||||
"tool_name": context['tool_name'],
|
||||
"mcp_servers": tool_config.get('mcp_integration', {}).get('servers', []),
|
||||
"enhanced_mode": tool_config.get('enhanced_mode', False)
|
||||
}
|
||||
)
|
||||
|
||||
return tool_config
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
log_error(
|
||||
"pre_tool_use",
|
||||
str(e),
|
||||
{"tool_name": tool_request.get('tool_name', 'unknown')}
|
||||
)
|
||||
log_hook_end("pre_tool_use", int(execution_time), False)
|
||||
|
||||
# Graceful fallback on error
|
||||
return self._create_fallback_tool_config(tool_request, str(e))
|
||||
|
||||
def _extract_tool_context(self, tool_request: dict) -> dict:
|
||||
"""Extract and enrich tool usage context."""
|
||||
context = {
|
||||
'tool_name': tool_request.get('tool_name', ''),
|
||||
'tool_parameters': tool_request.get('parameters', {}),
|
||||
'user_intent': tool_request.get('user_intent', ''),
|
||||
'session_context': tool_request.get('session_context', {}),
|
||||
'previous_tools': tool_request.get('previous_tools', []),
|
||||
'operation_sequence': tool_request.get('operation_sequence', []),
|
||||
'resource_state': tool_request.get('resource_state', {}),
|
||||
'timestamp': time.time()
|
||||
}
|
||||
|
||||
# Extract operation characteristics
|
||||
context.update(self._analyze_operation_characteristics(context))
|
||||
|
||||
# Analyze tool chain context
|
||||
context.update(self._analyze_tool_chain_context(context))
|
||||
|
||||
return context
|
||||
|
||||
def _analyze_operation_characteristics(self, context: dict) -> dict:
|
||||
"""Analyze operation characteristics for routing decisions."""
|
||||
characteristics = {
|
||||
'operation_type': OperationType.READ,
|
||||
'complexity_score': 0.0,
|
||||
'file_count': 1,
|
||||
'directory_count': 1,
|
||||
'parallelizable': False,
|
||||
'resource_intensive': False,
|
||||
'requires_intelligence': False
|
||||
}
|
||||
|
||||
tool_name = context['tool_name']
|
||||
tool_params = context['tool_parameters']
|
||||
|
||||
# Determine operation type from tool
|
||||
if tool_name in ['Write', 'Edit', 'MultiEdit']:
|
||||
characteristics['operation_type'] = OperationType.WRITE
|
||||
characteristics['complexity_score'] += 0.2
|
||||
elif tool_name in ['Build', 'Implement']:
|
||||
characteristics['operation_type'] = OperationType.BUILD
|
||||
characteristics['complexity_score'] += 0.4
|
||||
elif tool_name in ['Test', 'Validate']:
|
||||
characteristics['operation_type'] = OperationType.TEST
|
||||
characteristics['complexity_score'] += 0.1
|
||||
elif tool_name in ['Analyze', 'Debug']:
|
||||
characteristics['operation_type'] = OperationType.ANALYZE
|
||||
characteristics['complexity_score'] += 0.3
|
||||
characteristics['requires_intelligence'] = True
|
||||
|
||||
# Analyze file/directory scope
|
||||
if 'file_path' in tool_params:
|
||||
characteristics['file_count'] = 1
|
||||
elif 'files' in tool_params:
|
||||
file_list = tool_params['files']
|
||||
characteristics['file_count'] = len(file_list) if isinstance(file_list, list) else 1
|
||||
if characteristics['file_count'] > 3:
|
||||
characteristics['parallelizable'] = True
|
||||
characteristics['complexity_score'] += 0.3
|
||||
|
||||
if 'directory' in tool_params or 'path' in tool_params:
|
||||
path_param = tool_params.get('directory') or tool_params.get('path', '')
|
||||
if '*' in str(path_param) or '**' in str(path_param):
|
||||
characteristics['directory_count'] = 5 # Estimate for glob patterns
|
||||
characteristics['complexity_score'] += 0.2
|
||||
characteristics['parallelizable'] = True
|
||||
|
||||
# Resource intensity analysis
|
||||
if characteristics['file_count'] > 10 or characteristics['complexity_score'] > 0.6:
|
||||
characteristics['resource_intensive'] = True
|
||||
|
||||
# Intelligence requirements
|
||||
intelligence_tools = ['Analyze', 'Debug', 'Optimize', 'Refactor', 'Generate']
|
||||
if any(tool in tool_name for tool in intelligence_tools):
|
||||
characteristics['requires_intelligence'] = True
|
||||
|
||||
return characteristics
|
||||
|
||||
def _analyze_tool_chain_context(self, context: dict) -> dict:
|
||||
"""Analyze tool chain context for optimization opportunities."""
|
||||
chain_analysis = {
|
||||
'chain_length': len(context['previous_tools']),
|
||||
'pattern_detected': None,
|
||||
'optimization_opportunity': False,
|
||||
'cache_opportunity': False
|
||||
}
|
||||
|
||||
previous_tools = context['previous_tools']
|
||||
|
||||
if len(previous_tools) >= 2:
|
||||
# Detect common patterns
|
||||
tool_names = [tool.get('name', '') for tool in previous_tools[-3:]]
|
||||
|
||||
# Read-Edit pattern
|
||||
if any('Read' in name for name in tool_names) and any('Edit' in name for name in tool_names):
|
||||
chain_analysis['pattern_detected'] = 'read_edit_pattern'
|
||||
chain_analysis['optimization_opportunity'] = True
|
||||
|
||||
# Multiple file operations
|
||||
if sum(1 for name in tool_names if 'file' in name.lower()) >= 2:
|
||||
chain_analysis['pattern_detected'] = 'multi_file_pattern'
|
||||
chain_analysis['optimization_opportunity'] = True
|
||||
|
||||
# Analysis chain
|
||||
if sum(1 for name in tool_names if any(word in name for word in ['Analyze', 'Search', 'Find'])) >= 2:
|
||||
chain_analysis['pattern_detected'] = 'analysis_chain'
|
||||
chain_analysis['cache_opportunity'] = True
|
||||
|
||||
return chain_analysis
|
||||
|
||||
def _analyze_tool_requirements(self, context: dict) -> dict:
|
||||
"""Analyze tool requirements for capability matching."""
|
||||
requirements = {
|
||||
'capabilities_needed': [],
|
||||
'performance_requirements': {},
|
||||
'quality_requirements': {},
|
||||
'mcp_server_hints': [],
|
||||
'native_tool_sufficient': True
|
||||
}
|
||||
|
||||
tool_name = context['tool_name']
|
||||
characteristics = context
|
||||
|
||||
# Determine required capabilities
|
||||
if characteristics.get('requires_intelligence'):
|
||||
requirements['capabilities_needed'].extend(['analysis', 'reasoning', 'context_understanding'])
|
||||
requirements['native_tool_sufficient'] = False
|
||||
|
||||
if characteristics.get('complexity_score', 0) > 0.6:
|
||||
requirements['capabilities_needed'].extend(['complex_reasoning', 'systematic_analysis'])
|
||||
requirements['mcp_server_hints'].append('sequential')
|
||||
|
||||
if characteristics.get('file_count', 1) > 5:
|
||||
requirements['capabilities_needed'].extend(['multi_file_coordination', 'semantic_understanding'])
|
||||
requirements['mcp_server_hints'].append('serena')
|
||||
|
||||
# UI/component operations
|
||||
if any(word in context.get('user_intent', '').lower() for word in ['component', 'ui', 'frontend', 'design']):
|
||||
requirements['capabilities_needed'].append('ui_generation')
|
||||
requirements['mcp_server_hints'].append('magic')
|
||||
|
||||
# Documentation/library operations
|
||||
if any(word in context.get('user_intent', '').lower() for word in ['library', 'documentation', 'framework', 'api']):
|
||||
requirements['capabilities_needed'].append('documentation_access')
|
||||
requirements['mcp_server_hints'].append('context7')
|
||||
|
||||
# Testing operations
|
||||
if tool_name in ['Test'] or 'test' in context.get('user_intent', '').lower():
|
||||
requirements['capabilities_needed'].append('testing_automation')
|
||||
requirements['mcp_server_hints'].append('playwright')
|
||||
|
||||
# Performance requirements
|
||||
if characteristics.get('resource_intensive'):
|
||||
requirements['performance_requirements'] = {
|
||||
'max_execution_time_ms': 5000,
|
||||
'memory_efficiency_required': True,
|
||||
'parallel_execution_preferred': True
|
||||
}
|
||||
else:
|
||||
requirements['performance_requirements'] = {
|
||||
'max_execution_time_ms': 2000,
|
||||
'response_time_critical': True
|
||||
}
|
||||
|
||||
# Quality requirements
|
||||
if context.get('session_context', {}).get('is_production', False):
|
||||
requirements['quality_requirements'] = {
|
||||
'validation_required': True,
|
||||
'error_handling_critical': True,
|
||||
'rollback_capability_needed': True
|
||||
}
|
||||
|
||||
return requirements
|
||||
|
||||
def _analyze_routing_patterns(self, context: dict, requirements: dict) -> dict:
|
||||
"""Analyze patterns for intelligent routing decisions."""
|
||||
# Create operation data for pattern detection
|
||||
operation_data = {
|
||||
'operation_type': context.get('operation_type', OperationType.READ).value,
|
||||
'file_count': context.get('file_count', 1),
|
||||
'complexity_score': context.get('complexity_score', 0.0),
|
||||
'tool_name': context['tool_name']
|
||||
}
|
||||
|
||||
# Run pattern detection
|
||||
detection_result = self.pattern_detector.detect_patterns(
|
||||
context.get('user_intent', ''),
|
||||
context,
|
||||
operation_data
|
||||
)
|
||||
|
||||
# Create MCP activation plan
|
||||
mcp_plan = self.mcp_intelligence.create_activation_plan(
|
||||
context.get('user_intent', ''),
|
||||
context,
|
||||
operation_data
|
||||
)
|
||||
|
||||
return {
|
||||
'pattern_matches': detection_result.matches,
|
||||
'recommended_mcp_servers': detection_result.recommended_mcp_servers,
|
||||
'mcp_activation_plan': mcp_plan,
|
||||
'routing_confidence': detection_result.confidence_score,
|
||||
'optimization_opportunities': self._identify_optimization_opportunities(context, requirements)
|
||||
}
|
||||
|
||||
def _identify_optimization_opportunities(self, context: dict, requirements: dict) -> list:
|
||||
"""Identify optimization opportunities for tool execution."""
|
||||
opportunities = []
|
||||
|
||||
# Parallel execution opportunity
|
||||
if context.get('parallelizable') and context.get('file_count', 1) > 3:
|
||||
opportunities.append({
|
||||
'type': 'parallel_execution',
|
||||
'description': 'Multi-file operation suitable for parallel processing',
|
||||
'estimated_speedup': min(context.get('file_count', 1) * 0.3, 2.0)
|
||||
})
|
||||
|
||||
# Caching opportunity
|
||||
if context.get('cache_opportunity'):
|
||||
opportunities.append({
|
||||
'type': 'result_caching',
|
||||
'description': 'Analysis results can be cached for reuse',
|
||||
'estimated_speedup': 1.5
|
||||
})
|
||||
|
||||
# MCP server coordination
|
||||
if len(requirements.get('mcp_server_hints', [])) > 1:
|
||||
opportunities.append({
|
||||
'type': 'mcp_coordination',
|
||||
'description': 'Multiple MCP servers can work together',
|
||||
'quality_improvement': 0.2
|
||||
})
|
||||
|
||||
# Intelligence routing
|
||||
if not requirements.get('native_tool_sufficient'):
|
||||
opportunities.append({
|
||||
'type': 'intelligence_routing',
|
||||
'description': 'Operation benefits from MCP server intelligence',
|
||||
'quality_improvement': 0.3
|
||||
})
|
||||
|
||||
return opportunities
|
||||
|
||||
def _apply_routing_adaptations(self, context: dict, routing_analysis: dict) -> dict:
|
||||
"""Apply learned adaptations to routing decisions."""
|
||||
base_routing = {
|
||||
'recommended_mcp_servers': routing_analysis['recommended_mcp_servers'],
|
||||
'mcp_activation_plan': routing_analysis['mcp_activation_plan'],
|
||||
'optimization_opportunities': routing_analysis['optimization_opportunities']
|
||||
}
|
||||
|
||||
# Apply learning engine adaptations
|
||||
enhanced_routing = self.learning_engine.apply_adaptations(context, base_routing)
|
||||
|
||||
return enhanced_routing
|
||||
|
||||
def _create_execution_plan(self, context: dict, enhanced_routing: dict) -> dict:
|
||||
"""Create optimal execution plan for tool usage."""
|
||||
plan = {
|
||||
'execution_strategy': 'direct',
|
||||
'mcp_servers_required': enhanced_routing.get('recommended_mcp_servers', []),
|
||||
'parallel_execution': False,
|
||||
'caching_enabled': False,
|
||||
'fallback_strategy': 'native_tools',
|
||||
'performance_optimizations': [],
|
||||
'estimated_execution_time_ms': 500
|
||||
}
|
||||
|
||||
# Determine execution strategy
|
||||
if context.get('complexity_score', 0) > 0.6:
|
||||
plan['execution_strategy'] = 'intelligent_routing'
|
||||
elif context.get('file_count', 1) > 5:
|
||||
plan['execution_strategy'] = 'parallel_coordination'
|
||||
|
||||
# Configure parallel execution
|
||||
if context.get('parallelizable') and context.get('file_count', 1) > 3:
|
||||
plan['parallel_execution'] = True
|
||||
plan['performance_optimizations'].append('parallel_file_processing')
|
||||
plan['estimated_execution_time_ms'] = int(plan['estimated_execution_time_ms'] * 0.6)
|
||||
|
||||
# Configure caching
|
||||
if context.get('cache_opportunity'):
|
||||
plan['caching_enabled'] = True
|
||||
plan['performance_optimizations'].append('result_caching')
|
||||
|
||||
# Configure MCP coordination
|
||||
mcp_servers = plan['mcp_servers_required']
|
||||
if len(mcp_servers) > 1:
|
||||
plan['coordination_strategy'] = enhanced_routing.get('mcp_activation_plan', {}).get('coordination_strategy', 'collaborative')
|
||||
|
||||
# Estimate execution time based on complexity
|
||||
base_time = 200
|
||||
complexity_multiplier = 1 + context.get('complexity_score', 0.0)
|
||||
file_multiplier = 1 + (context.get('file_count', 1) - 1) * 0.1
|
||||
|
||||
plan['estimated_execution_time_ms'] = int(base_time * complexity_multiplier * file_multiplier)
|
||||
|
||||
return plan
|
||||
|
||||
def _configure_tool_enhancement(self, context: dict, execution_plan: dict) -> dict:
|
||||
"""Configure tool enhancement based on execution plan."""
|
||||
tool_config = {
|
||||
'tool_name': context['tool_name'],
|
||||
'enhanced_mode': execution_plan['execution_strategy'] != 'direct',
|
||||
'mcp_integration': {
|
||||
'enabled': len(execution_plan['mcp_servers_required']) > 0,
|
||||
'servers': execution_plan['mcp_servers_required'],
|
||||
'coordination_strategy': execution_plan.get('coordination_strategy', 'single_server')
|
||||
},
|
||||
'performance_optimization': {
|
||||
'parallel_execution': execution_plan['parallel_execution'],
|
||||
'caching_enabled': execution_plan['caching_enabled'],
|
||||
'optimizations': execution_plan['performance_optimizations']
|
||||
},
|
||||
'quality_enhancement': {
|
||||
'validation_enabled': context.get('session_context', {}).get('is_production', False),
|
||||
'error_recovery': True,
|
||||
'context_preservation': True
|
||||
},
|
||||
'execution_metadata': {
|
||||
'estimated_time_ms': execution_plan['estimated_execution_time_ms'],
|
||||
'complexity_score': context.get('complexity_score', 0.0),
|
||||
'intelligence_level': self._determine_intelligence_level(context)
|
||||
}
|
||||
}
|
||||
|
||||
# Add tool-specific enhancements
|
||||
tool_config.update(self._get_tool_specific_enhancements(context, execution_plan))
|
||||
|
||||
return tool_config
|
||||
|
||||
def _determine_intelligence_level(self, context: dict) -> str:
|
||||
"""Determine required intelligence level for operation."""
|
||||
complexity = context.get('complexity_score', 0.0)
|
||||
|
||||
if complexity >= 0.8:
|
||||
return 'high'
|
||||
elif complexity >= 0.5:
|
||||
return 'medium'
|
||||
elif context.get('requires_intelligence'):
|
||||
return 'medium'
|
||||
else:
|
||||
return 'low'
|
||||
|
||||
def _get_tool_specific_enhancements(self, context: dict, execution_plan: dict) -> dict:
|
||||
"""Get tool-specific enhancement configurations."""
|
||||
tool_name = context['tool_name']
|
||||
enhancements = {}
|
||||
|
||||
# File operation enhancements
|
||||
if tool_name in ['Read', 'Write', 'Edit']:
|
||||
enhancements['file_operations'] = {
|
||||
'integrity_check': True,
|
||||
'backup_on_write': context.get('session_context', {}).get('is_production', False),
|
||||
'encoding_detection': True
|
||||
}
|
||||
|
||||
# Multi-file operation enhancements
|
||||
if tool_name in ['MultiEdit', 'Batch'] or context.get('file_count', 1) > 3:
|
||||
enhancements['multi_file_operations'] = {
|
||||
'transaction_mode': True,
|
||||
'rollback_capability': True,
|
||||
'progress_tracking': True
|
||||
}
|
||||
|
||||
# Analysis operation enhancements
|
||||
if tool_name in ['Analyze', 'Debug', 'Search']:
|
||||
enhancements['analysis_operations'] = {
|
||||
'deep_context_analysis': context.get('complexity_score', 0.0) > 0.5,
|
||||
'semantic_understanding': 'serena' in execution_plan['mcp_servers_required'],
|
||||
'pattern_recognition': True
|
||||
}
|
||||
|
||||
# Build/Implementation enhancements
|
||||
if tool_name in ['Build', 'Implement', 'Generate']:
|
||||
enhancements['build_operations'] = {
|
||||
'framework_integration': 'context7' in execution_plan['mcp_servers_required'],
|
||||
'component_generation': 'magic' in execution_plan['mcp_servers_required'],
|
||||
'quality_validation': True
|
||||
}
|
||||
|
||||
return enhancements
|
||||
|
||||
def _calculate_efficiency_score(self, context: dict, execution_time_ms: float) -> float:
|
||||
"""Calculate efficiency score for the routing decision."""
|
||||
# Base efficiency is inverse of execution time relative to target
|
||||
time_efficiency = min(self.performance_target_ms / max(execution_time_ms, 1), 1.0)
|
||||
|
||||
# Complexity handling efficiency
|
||||
complexity = context.get('complexity_score', 0.0)
|
||||
complexity_efficiency = 1.0 - (complexity * 0.3) # Some complexity is expected
|
||||
|
||||
# Resource utilization efficiency
|
||||
resource_usage = context.get('resource_state', {}).get('usage_percent', 0)
|
||||
resource_efficiency = 1.0 - max(resource_usage - 70, 0) / 100.0
|
||||
|
||||
# Weighted efficiency score
|
||||
efficiency_score = (time_efficiency * 0.4 +
|
||||
complexity_efficiency * 0.3 +
|
||||
resource_efficiency * 0.3)
|
||||
|
||||
return max(min(efficiency_score, 1.0), 0.0)
|
||||
|
||||
def _record_tool_learning(self, context: dict, tool_config: dict):
|
||||
"""Record tool usage for learning purposes."""
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.OPERATION_PATTERN,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'tool_name': context['tool_name'],
|
||||
'mcp_servers_used': tool_config.get('mcp_integration', {}).get('servers', []),
|
||||
'execution_strategy': tool_config.get('execution_metadata', {}).get('intelligence_level', 'low'),
|
||||
'optimizations_applied': tool_config.get('performance_optimization', {}).get('optimizations', [])
|
||||
},
|
||||
0.8, # Assume good effectiveness (will be updated later)
|
||||
0.7, # Medium confidence until validated
|
||||
{'hook': 'pre_tool_use', 'version': '1.0'}
|
||||
)
|
||||
|
||||
def _create_fallback_tool_config(self, tool_request: dict, error: str) -> dict:
|
||||
"""Create fallback tool configuration on error."""
|
||||
return {
|
||||
'tool_name': tool_request.get('tool_name', 'unknown'),
|
||||
'enhanced_mode': False,
|
||||
'fallback_mode': True,
|
||||
'error': error,
|
||||
'mcp_integration': {
|
||||
'enabled': False,
|
||||
'servers': [],
|
||||
'coordination_strategy': 'none'
|
||||
},
|
||||
'performance_optimization': {
|
||||
'parallel_execution': False,
|
||||
'caching_enabled': False,
|
||||
'optimizations': []
|
||||
},
|
||||
'performance_metrics': {
|
||||
'routing_time_ms': 0,
|
||||
'target_met': False,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
"""Main hook execution function."""
|
||||
try:
|
||||
# Read tool request from stdin
|
||||
tool_request = json.loads(sys.stdin.read())
|
||||
|
||||
# Initialize and run hook
|
||||
hook = PreToolUseHook()
|
||||
result = hook.process_tool_use(tool_request)
|
||||
|
||||
# Output result as JSON
|
||||
print(json.dumps(result, indent=2))
|
||||
|
||||
except Exception as e:
|
||||
# Output error as JSON
|
||||
error_result = {
|
||||
'enhanced_mode': False,
|
||||
'error': str(e),
|
||||
'fallback_mode': True
|
||||
}
|
||||
print(json.dumps(error_result, indent=2))
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,704 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
SuperClaude-Lite Session Start Hook
|
||||
|
||||
Implements SESSION_LIFECYCLE.md + FLAGS.md logic for intelligent session bootstrap.
|
||||
Performance target: <50ms execution time.
|
||||
|
||||
This hook runs at the start of every Claude Code session and provides:
|
||||
- Smart project context loading with framework exclusion
|
||||
- Automatic mode detection and activation
|
||||
- MCP server intelligence routing
|
||||
- User preference adaptation
|
||||
- Performance-optimized initialization
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# Add shared modules to path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
|
||||
|
||||
from framework_logic import FrameworkLogic, OperationContext, OperationType, RiskLevel
|
||||
from pattern_detection import PatternDetector, PatternType
|
||||
from mcp_intelligence import MCPIntelligence
|
||||
from compression_engine import CompressionEngine, CompressionLevel, ContentType
|
||||
from learning_engine import LearningEngine, LearningType, AdaptationScope
|
||||
from yaml_loader import config_loader
|
||||
from logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
|
||||
|
||||
class SessionStartHook:
|
||||
"""
|
||||
Session start hook implementing SuperClaude intelligence.
|
||||
|
||||
Responsibilities:
|
||||
- Initialize session with project context
|
||||
- Apply user preferences and learned adaptations
|
||||
- Activate appropriate modes and MCP servers
|
||||
- Set up compression and performance optimization
|
||||
- Track session metrics and performance
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
start_time = time.time()
|
||||
|
||||
# Initialize only essential components immediately
|
||||
self.framework_logic = FrameworkLogic()
|
||||
|
||||
# Lazy-load other components to improve performance
|
||||
self._pattern_detector = None
|
||||
self._mcp_intelligence = None
|
||||
self._compression_engine = None
|
||||
self._learning_engine = None
|
||||
|
||||
# Use installation directory for cache
|
||||
import os
|
||||
cache_dir = Path(os.path.expanduser("~/.claude/cache"))
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self._cache_dir = cache_dir
|
||||
|
||||
# Load hook-specific configuration from SuperClaude config
|
||||
self.hook_config = config_loader.get_hook_config('session_start')
|
||||
|
||||
# Load session configuration (from YAML if exists, otherwise use hook config)
|
||||
try:
|
||||
self.session_config = config_loader.load_config('session')
|
||||
except FileNotFoundError:
|
||||
# Fall back to hook configuration if YAML file not found
|
||||
self.session_config = self.hook_config.get('configuration', {})
|
||||
|
||||
# Performance tracking using configuration
|
||||
self.initialization_time = (time.time() - start_time) * 1000
|
||||
self.performance_target_ms = config_loader.get_hook_config('session_start', 'performance_target_ms', 50)
|
||||
|
||||
@property
|
||||
def pattern_detector(self):
|
||||
"""Lazy-load pattern detector to improve initialization performance."""
|
||||
if self._pattern_detector is None:
|
||||
self._pattern_detector = PatternDetector()
|
||||
return self._pattern_detector
|
||||
|
||||
@property
|
||||
def mcp_intelligence(self):
|
||||
"""Lazy-load MCP intelligence to improve initialization performance."""
|
||||
if self._mcp_intelligence is None:
|
||||
self._mcp_intelligence = MCPIntelligence()
|
||||
return self._mcp_intelligence
|
||||
|
||||
@property
|
||||
def compression_engine(self):
|
||||
"""Lazy-load compression engine to improve initialization performance."""
|
||||
if self._compression_engine is None:
|
||||
self._compression_engine = CompressionEngine()
|
||||
return self._compression_engine
|
||||
|
||||
@property
|
||||
def learning_engine(self):
|
||||
"""Lazy-load learning engine to improve initialization performance."""
|
||||
if self._learning_engine is None:
|
||||
self._learning_engine = LearningEngine(self._cache_dir)
|
||||
return self._learning_engine
|
||||
|
||||
def initialize_session(self, session_context: dict) -> dict:
|
||||
"""
|
||||
Initialize session with SuperClaude intelligence.
|
||||
|
||||
Args:
|
||||
session_context: Session initialization context from Claude Code
|
||||
|
||||
Returns:
|
||||
Enhanced session configuration
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# Log hook start
|
||||
log_hook_start("session_start", {
|
||||
"project_path": session_context.get('project_path', 'unknown'),
|
||||
"user_id": session_context.get('user_id', 'anonymous'),
|
||||
"has_previous_session": bool(session_context.get('previous_session_id'))
|
||||
})
|
||||
|
||||
try:
|
||||
# Extract session context
|
||||
context = self._extract_session_context(session_context)
|
||||
|
||||
# Detect patterns and operation intent
|
||||
detection_result = self._detect_session_patterns(context)
|
||||
|
||||
# Apply learned adaptations
|
||||
enhanced_recommendations = self._apply_learning_adaptations(
|
||||
context, detection_result
|
||||
)
|
||||
|
||||
# Create MCP activation plan
|
||||
mcp_plan = self._create_mcp_activation_plan(
|
||||
context, enhanced_recommendations
|
||||
)
|
||||
|
||||
# Configure compression strategy
|
||||
compression_config = self._configure_compression(context)
|
||||
|
||||
# Generate session configuration
|
||||
session_config = self._generate_session_config(
|
||||
context, enhanced_recommendations, mcp_plan, compression_config
|
||||
)
|
||||
|
||||
# Record learning event
|
||||
self._record_session_learning(context, session_config)
|
||||
|
||||
# Detect and activate modes
|
||||
activated_modes = self._activate_intelligent_modes(context, enhanced_recommendations)
|
||||
|
||||
# Log mode activation decisions
|
||||
for mode in activated_modes:
|
||||
log_decision(
|
||||
"session_start",
|
||||
"mode_activation",
|
||||
mode['name'],
|
||||
f"Activated based on: {mode.get('trigger', 'automatic detection')}"
|
||||
)
|
||||
|
||||
# Configure MCP server activation
|
||||
mcp_configuration = self._configure_mcp_servers(context, activated_modes)
|
||||
|
||||
# Log MCP server decisions
|
||||
if mcp_configuration.get('enabled_servers'):
|
||||
log_decision(
|
||||
"session_start",
|
||||
"mcp_server_activation",
|
||||
",".join(mcp_configuration['enabled_servers']),
|
||||
f"Project type: {context.get('project_type', 'unknown')}"
|
||||
)
|
||||
|
||||
# Performance validation
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
session_config['performance_metrics'] = {
|
||||
'initialization_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'efficiency_score': self._calculate_initialization_efficiency(execution_time)
|
||||
}
|
||||
|
||||
# Persist session context to cache for other hooks
|
||||
session_id = context['session_id']
|
||||
session_file_path = self._cache_dir / f"session_{session_id}.json"
|
||||
try:
|
||||
with open(session_file_path, 'w') as f:
|
||||
json.dump(session_config, f, indent=2)
|
||||
except Exception as e:
|
||||
# Log error but don't fail session initialization
|
||||
log_error("session_start", f"Failed to persist session context: {str(e)}", {"session_id": session_id})
|
||||
|
||||
# Log successful completion
|
||||
log_hook_end(
|
||||
"session_start",
|
||||
int(execution_time),
|
||||
True,
|
||||
{
|
||||
"project_type": context.get('project_type', 'unknown'),
|
||||
"modes_activated": [m['name'] for m in activated_modes],
|
||||
"mcp_servers": mcp_configuration.get('enabled_servers', [])
|
||||
}
|
||||
)
|
||||
|
||||
return session_config
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
log_error(
|
||||
"session_start",
|
||||
str(e),
|
||||
{"project_path": session_context.get('project_path', 'unknown')}
|
||||
)
|
||||
log_hook_end("session_start", int(execution_time), False)
|
||||
|
||||
# Graceful fallback on error
|
||||
return self._create_fallback_session_config(session_context, str(e))
|
||||
|
||||
def _extract_session_context(self, session_data: dict) -> dict:
|
||||
"""Extract and enrich session context."""
|
||||
context = {
|
||||
'session_id': session_data.get('session_id', 'unknown'),
|
||||
'project_path': session_data.get('project_path', ''),
|
||||
'user_input': session_data.get('user_input', ''),
|
||||
'conversation_length': session_data.get('conversation_length', 0),
|
||||
'resource_usage_percent': session_data.get('resource_usage_percent', 0),
|
||||
'is_continuation': session_data.get('is_continuation', False),
|
||||
'previous_session_id': session_data.get('previous_session_id'),
|
||||
'timestamp': time.time()
|
||||
}
|
||||
|
||||
# Detect project characteristics
|
||||
if context['project_path']:
|
||||
project_path = Path(context['project_path'])
|
||||
context.update(self._analyze_project_structure(project_path))
|
||||
|
||||
# Analyze user input for intent
|
||||
if context['user_input']:
|
||||
context.update(self._analyze_user_intent(context['user_input']))
|
||||
|
||||
return context
|
||||
|
||||
def _analyze_project_structure(self, project_path: Path) -> dict:
|
||||
"""Analyze project structure for intelligent configuration."""
|
||||
analysis = {
|
||||
'project_type': 'unknown',
|
||||
'has_tests': False,
|
||||
'has_frontend': False,
|
||||
'has_backend': False,
|
||||
'framework_detected': None,
|
||||
'file_count_estimate': 0,
|
||||
'directory_count_estimate': 0,
|
||||
'is_production': False
|
||||
}
|
||||
|
||||
try:
|
||||
if not project_path.exists():
|
||||
return analysis
|
||||
|
||||
# Quick file/directory count (limited for performance)
|
||||
files = list(project_path.rglob('*'))[:100] # Limit for performance
|
||||
analysis['file_count_estimate'] = len([f for f in files if f.is_file()])
|
||||
analysis['directory_count_estimate'] = len([f for f in files if f.is_dir()])
|
||||
|
||||
# Detect project type
|
||||
if (project_path / 'package.json').exists():
|
||||
analysis['project_type'] = 'nodejs'
|
||||
analysis['has_frontend'] = True
|
||||
elif (project_path / 'pyproject.toml').exists() or (project_path / 'setup.py').exists():
|
||||
analysis['project_type'] = 'python'
|
||||
elif (project_path / 'Cargo.toml').exists():
|
||||
analysis['project_type'] = 'rust'
|
||||
elif (project_path / 'go.mod').exists():
|
||||
analysis['project_type'] = 'go'
|
||||
|
||||
# Check for tests
|
||||
test_patterns = ['test', 'tests', '__tests__', 'spec']
|
||||
analysis['has_tests'] = any(
|
||||
(project_path / pattern).exists() or
|
||||
any(pattern in str(f) for f in files[:20])
|
||||
for pattern in test_patterns
|
||||
)
|
||||
|
||||
# Check for production indicators
|
||||
prod_indicators = ['.env.production', 'docker-compose.yml', 'Dockerfile', '.github']
|
||||
analysis['is_production'] = any(
|
||||
(project_path / indicator).exists() for indicator in prod_indicators
|
||||
)
|
||||
|
||||
# Framework detection (quick check)
|
||||
if analysis['project_type'] == 'nodejs':
|
||||
package_json = project_path / 'package.json'
|
||||
if package_json.exists():
|
||||
try:
|
||||
with open(package_json) as f:
|
||||
pkg_data = json.load(f)
|
||||
deps = {**pkg_data.get('dependencies', {}), **pkg_data.get('devDependencies', {})}
|
||||
|
||||
if 'react' in deps:
|
||||
analysis['framework_detected'] = 'react'
|
||||
elif 'vue' in deps:
|
||||
analysis['framework_detected'] = 'vue'
|
||||
elif 'angular' in deps:
|
||||
analysis['framework_detected'] = 'angular'
|
||||
elif 'express' in deps:
|
||||
analysis['has_backend'] = True
|
||||
except:
|
||||
pass
|
||||
|
||||
except Exception:
|
||||
# Return partial analysis on error
|
||||
pass
|
||||
|
||||
return analysis
|
||||
|
||||
def _analyze_user_intent(self, user_input: str) -> dict:
|
||||
"""Analyze user input for session intent and complexity."""
|
||||
intent_analysis = {
|
||||
'operation_type': OperationType.READ,
|
||||
'complexity_score': 0.0,
|
||||
'brainstorming_likely': False,
|
||||
'user_expertise': 'intermediate',
|
||||
'urgency': 'normal'
|
||||
}
|
||||
|
||||
user_lower = user_input.lower()
|
||||
|
||||
# Detect operation type
|
||||
if any(word in user_lower for word in ['build', 'create', 'implement', 'develop']):
|
||||
intent_analysis['operation_type'] = OperationType.BUILD
|
||||
intent_analysis['complexity_score'] += 0.3
|
||||
elif any(word in user_lower for word in ['fix', 'debug', 'troubleshoot', 'solve']):
|
||||
intent_analysis['operation_type'] = OperationType.ANALYZE
|
||||
intent_analysis['complexity_score'] += 0.2
|
||||
elif any(word in user_lower for word in ['refactor', 'restructure', 'reorganize']):
|
||||
intent_analysis['operation_type'] = OperationType.REFACTOR
|
||||
intent_analysis['complexity_score'] += 0.4
|
||||
elif any(word in user_lower for word in ['test', 'validate', 'check']):
|
||||
intent_analysis['operation_type'] = OperationType.TEST
|
||||
intent_analysis['complexity_score'] += 0.1
|
||||
|
||||
# Detect brainstorming needs
|
||||
brainstorm_indicators = [
|
||||
'not sure', 'thinking about', 'maybe', 'possibly', 'could we',
|
||||
'brainstorm', 'explore', 'figure out', 'new project', 'startup idea'
|
||||
]
|
||||
intent_analysis['brainstorming_likely'] = any(
|
||||
indicator in user_lower for indicator in brainstorm_indicators
|
||||
)
|
||||
|
||||
# Complexity indicators
|
||||
complexity_indicators = [
|
||||
'complex', 'complicated', 'comprehensive', 'entire', 'whole', 'system-wide',
|
||||
'architecture', 'multiple', 'many', 'several'
|
||||
]
|
||||
for indicator in complexity_indicators:
|
||||
if indicator in user_lower:
|
||||
intent_analysis['complexity_score'] += 0.2
|
||||
|
||||
intent_analysis['complexity_score'] = min(intent_analysis['complexity_score'], 1.0)
|
||||
|
||||
# Detect urgency
|
||||
if any(word in user_lower for word in ['urgent', 'asap', 'quickly', 'fast']):
|
||||
intent_analysis['urgency'] = 'high'
|
||||
elif any(word in user_lower for word in ['when you can', 'no rush', 'eventually']):
|
||||
intent_analysis['urgency'] = 'low'
|
||||
|
||||
return intent_analysis
|
||||
|
||||
def _detect_session_patterns(self, context: dict) -> dict:
|
||||
"""Detect patterns for intelligent session configuration."""
|
||||
|
||||
# Skip pattern detection if no user input provided
|
||||
if not context.get('user_input', '').strip():
|
||||
return {
|
||||
'pattern_matches': [],
|
||||
'recommended_modes': [],
|
||||
'recommended_mcp_servers': [],
|
||||
'suggested_flags': [],
|
||||
'confidence_score': 0.0
|
||||
}
|
||||
|
||||
# Create operation context for pattern detection
|
||||
operation_data = {
|
||||
'operation_type': context.get('operation_type', OperationType.READ).value,
|
||||
'file_count': context.get('file_count_estimate', 1),
|
||||
'directory_count': context.get('directory_count_estimate', 1),
|
||||
'complexity_score': context.get('complexity_score', 0.0),
|
||||
'has_external_dependencies': context.get('framework_detected') is not None,
|
||||
'project_type': context.get('project_type', 'unknown')
|
||||
}
|
||||
|
||||
# Run pattern detection
|
||||
detection_result = self.pattern_detector.detect_patterns(
|
||||
context.get('user_input', ''),
|
||||
context,
|
||||
operation_data
|
||||
)
|
||||
|
||||
return {
|
||||
'pattern_matches': detection_result.matches,
|
||||
'recommended_modes': detection_result.recommended_modes,
|
||||
'recommended_mcp_servers': detection_result.recommended_mcp_servers,
|
||||
'suggested_flags': detection_result.suggested_flags,
|
||||
'confidence_score': detection_result.confidence_score
|
||||
}
|
||||
|
||||
def _apply_learning_adaptations(self, context: dict, detection_result: dict) -> dict:
|
||||
"""Apply learned adaptations to enhance recommendations."""
|
||||
base_recommendations = {
|
||||
'recommended_modes': detection_result['recommended_modes'],
|
||||
'recommended_mcp_servers': detection_result['recommended_mcp_servers'],
|
||||
'suggested_flags': detection_result['suggested_flags']
|
||||
}
|
||||
|
||||
# Apply learning engine adaptations
|
||||
enhanced_recommendations = self.learning_engine.apply_adaptations(
|
||||
context, base_recommendations
|
||||
)
|
||||
|
||||
# Apply user preferences if available
|
||||
self._apply_user_preferences(enhanced_recommendations, context)
|
||||
|
||||
return enhanced_recommendations
|
||||
|
||||
def _apply_user_preferences(self, recommendations: dict, context: dict):
|
||||
"""Apply stored user preferences to recommendations."""
|
||||
# Check for preferred tools for different operations
|
||||
operation_types = ['read', 'write', 'edit', 'analyze', 'mcp']
|
||||
|
||||
for op_type in operation_types:
|
||||
pref_key = f"tool_{op_type}"
|
||||
preferred_tool = self.learning_engine.get_last_preference(pref_key)
|
||||
|
||||
if preferred_tool:
|
||||
# Add a hint to the recommendations
|
||||
if 'preference_hints' not in recommendations:
|
||||
recommendations['preference_hints'] = {}
|
||||
recommendations['preference_hints'][op_type] = preferred_tool
|
||||
|
||||
# Store project-specific information if we have a project path
|
||||
if context.get('project_path'):
|
||||
project_path = context['project_path']
|
||||
|
||||
# Store project type if detected
|
||||
if context.get('project_type') and context['project_type'] != 'unknown':
|
||||
self.learning_engine.update_project_info(
|
||||
project_path,
|
||||
'project_type',
|
||||
context['project_type']
|
||||
)
|
||||
|
||||
# Store framework if detected
|
||||
if context.get('framework_detected'):
|
||||
self.learning_engine.update_project_info(
|
||||
project_path,
|
||||
'framework',
|
||||
context['framework_detected']
|
||||
)
|
||||
|
||||
def _create_mcp_activation_plan(self, context: dict, recommendations: dict) -> dict:
|
||||
"""Create MCP server activation plan."""
|
||||
# Create operation data for MCP intelligence
|
||||
operation_data = {
|
||||
'file_count': context.get('file_count_estimate', 1),
|
||||
'complexity_score': context.get('complexity_score', 0.0),
|
||||
'operation_type': context.get('operation_type', OperationType.READ).value
|
||||
}
|
||||
|
||||
# Create MCP activation plan
|
||||
mcp_plan = self.mcp_intelligence.create_activation_plan(
|
||||
context.get('user_input', ''),
|
||||
context,
|
||||
operation_data
|
||||
)
|
||||
|
||||
return {
|
||||
'servers_to_activate': mcp_plan.servers_to_activate,
|
||||
'activation_order': mcp_plan.activation_order,
|
||||
'estimated_cost_ms': mcp_plan.estimated_cost_ms,
|
||||
'coordination_strategy': mcp_plan.coordination_strategy,
|
||||
'fallback_strategy': mcp_plan.fallback_strategy
|
||||
}
|
||||
|
||||
def _configure_compression(self, context: dict) -> dict:
|
||||
"""Configure compression strategy for the session."""
|
||||
compression_level = self.compression_engine.determine_compression_level(context)
|
||||
|
||||
return {
|
||||
'compression_level': compression_level.value,
|
||||
'estimated_savings': self.compression_engine._estimate_compression_savings(compression_level),
|
||||
'quality_impact': self.compression_engine._estimate_quality_impact(compression_level),
|
||||
'selective_compression_enabled': True
|
||||
}
|
||||
|
||||
def _generate_session_config(self, context: dict, recommendations: dict,
|
||||
mcp_plan: dict, compression_config: dict) -> dict:
|
||||
"""Generate comprehensive session configuration."""
|
||||
config = {
|
||||
'session_id': context['session_id'],
|
||||
'superclaude_enabled': True,
|
||||
'initialization_timestamp': context['timestamp'],
|
||||
|
||||
# Mode configuration
|
||||
'active_modes': recommendations.get('recommended_modes', []),
|
||||
'mode_configurations': self._get_mode_configurations(recommendations),
|
||||
|
||||
# MCP server configuration
|
||||
'mcp_servers': {
|
||||
'enabled_servers': mcp_plan['servers_to_activate'],
|
||||
'activation_order': mcp_plan['activation_order'],
|
||||
'coordination_strategy': mcp_plan['coordination_strategy']
|
||||
},
|
||||
|
||||
# Compression configuration
|
||||
'compression': compression_config,
|
||||
|
||||
# Performance configuration
|
||||
'performance': {
|
||||
'resource_monitoring_enabled': True,
|
||||
'optimization_targets': self.framework_logic.performance_targets,
|
||||
'delegation_threshold': 0.4 if context.get('complexity_score', 0) > 0.4 else 0.6
|
||||
},
|
||||
|
||||
# Learning configuration
|
||||
'learning': {
|
||||
'adaptation_enabled': True,
|
||||
'effectiveness_tracking': True,
|
||||
'applied_adaptations': recommendations.get('applied_adaptations', [])
|
||||
},
|
||||
|
||||
# Context preservation
|
||||
'context': {
|
||||
'project_type': context.get('project_type', 'unknown'),
|
||||
'complexity_score': context.get('complexity_score', 0.0),
|
||||
'brainstorming_mode': context.get('brainstorming_likely', False),
|
||||
'user_expertise': context.get('user_expertise', 'intermediate')
|
||||
},
|
||||
|
||||
# Quality gates
|
||||
'quality_gates': self._configure_quality_gates(context),
|
||||
|
||||
# Session metadata
|
||||
'metadata': {
|
||||
'framework_version': '1.0.0',
|
||||
'hook_version': 'session_start_1.0',
|
||||
'configuration_source': 'superclaude_intelligence'
|
||||
}
|
||||
}
|
||||
|
||||
return config
|
||||
|
||||
def _get_mode_configurations(self, recommendations: dict) -> dict:
|
||||
"""Get specific configuration for activated modes."""
|
||||
mode_configs = {}
|
||||
|
||||
for mode in recommendations.get('recommended_modes', []):
|
||||
if mode == 'brainstorming':
|
||||
mode_configs[mode] = {
|
||||
'max_rounds': 15,
|
||||
'convergence_threshold': 0.85,
|
||||
'auto_handoff_enabled': True
|
||||
}
|
||||
elif mode == 'task_management':
|
||||
mode_configs[mode] = {
|
||||
'delegation_enabled': True,
|
||||
'wave_orchestration': True,
|
||||
'auto_checkpoints': True
|
||||
}
|
||||
elif mode == 'token_efficiency':
|
||||
mode_configs[mode] = {
|
||||
'compression_level': 'adaptive',
|
||||
'symbol_systems_enabled': True,
|
||||
'selective_preservation': True
|
||||
}
|
||||
|
||||
return mode_configs
|
||||
|
||||
def _configure_quality_gates(self, context: dict) -> list:
|
||||
"""Configure quality gates based on context."""
|
||||
# Create operation context for quality gate determination
|
||||
operation_context = OperationContext(
|
||||
operation_type=context.get('operation_type', OperationType.READ),
|
||||
file_count=context.get('file_count_estimate', 1),
|
||||
directory_count=context.get('directory_count_estimate', 1),
|
||||
has_tests=context.get('has_tests', False),
|
||||
is_production=context.get('is_production', False),
|
||||
user_expertise=context.get('user_expertise', 'intermediate'),
|
||||
project_type=context.get('project_type', 'unknown'),
|
||||
complexity_score=context.get('complexity_score', 0.0),
|
||||
risk_level=RiskLevel.LOW
|
||||
)
|
||||
|
||||
return self.framework_logic.get_quality_gates(operation_context)
|
||||
|
||||
def _record_session_learning(self, context: dict, session_config: dict):
|
||||
"""Record session initialization for learning."""
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.OPERATION_PATTERN,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'session_config': session_config,
|
||||
'modes_activated': session_config.get('active_modes', []),
|
||||
'mcp_servers': session_config.get('mcp_servers', {}).get('enabled_servers', [])
|
||||
},
|
||||
1.0, # Assume successful initialization
|
||||
0.8, # High confidence in pattern
|
||||
{'hook': 'session_start', 'version': '1.0'}
|
||||
)
|
||||
|
||||
def _create_fallback_session_config(self, session_context: dict, error: str) -> dict:
|
||||
"""Create fallback configuration on error."""
|
||||
return {
|
||||
'session_id': session_context.get('session_id', 'unknown'),
|
||||
'superclaude_enabled': False,
|
||||
'fallback_mode': True,
|
||||
'error': error,
|
||||
'basic_config': {
|
||||
'compression_level': 'minimal',
|
||||
'mcp_servers_enabled': False,
|
||||
'learning_disabled': True
|
||||
},
|
||||
'performance_metrics': {
|
||||
'execution_time_ms': 0,
|
||||
'target_met': False,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
|
||||
def _activate_intelligent_modes(self, context: dict, recommendations: dict) -> list:
|
||||
"""Activate intelligent modes based on context and recommendations."""
|
||||
activated_modes = []
|
||||
|
||||
# Add brainstorming mode if likely
|
||||
if context.get('brainstorming_likely', False):
|
||||
activated_modes.append({'name': 'brainstorming', 'trigger': 'user input'})
|
||||
|
||||
# Add task management mode if recommended
|
||||
if 'task_management' in recommendations.get('recommended_modes', []):
|
||||
activated_modes.append({'name': 'task_management', 'trigger': 'pattern detection'})
|
||||
|
||||
# Add token efficiency mode if recommended
|
||||
if 'token_efficiency' in recommendations.get('recommended_modes', []):
|
||||
activated_modes.append({'name': 'token_efficiency', 'trigger': 'pattern detection'})
|
||||
|
||||
return activated_modes
|
||||
|
||||
def _configure_mcp_servers(self, context: dict, activated_modes: list) -> dict:
|
||||
"""Configure MCP servers based on context and activated modes."""
|
||||
# Create operation data for MCP intelligence
|
||||
operation_data = {
|
||||
'file_count': context.get('file_count_estimate', 1),
|
||||
'complexity_score': context.get('complexity_score', 0.0),
|
||||
'operation_type': context.get('operation_type', OperationType.READ).value
|
||||
}
|
||||
|
||||
# Create MCP activation plan
|
||||
mcp_plan = self.mcp_intelligence.create_activation_plan(
|
||||
context.get('user_input', ''),
|
||||
context,
|
||||
operation_data
|
||||
)
|
||||
|
||||
return {
|
||||
'enabled_servers': mcp_plan.servers_to_activate,
|
||||
'activation_order': mcp_plan.activation_order,
|
||||
'coordination_strategy': mcp_plan.coordination_strategy
|
||||
}
|
||||
|
||||
def _calculate_initialization_efficiency(self, execution_time: float) -> float:
|
||||
"""Calculate initialization efficiency score."""
|
||||
return 1.0 - (execution_time / self.performance_target_ms) if execution_time < self.performance_target_ms else 0.0
|
||||
|
||||
|
||||
def main():
|
||||
"""Main hook execution function."""
|
||||
try:
|
||||
# Read session data from stdin
|
||||
session_data = json.loads(sys.stdin.read())
|
||||
|
||||
# Initialize and run hook
|
||||
hook = SessionStartHook()
|
||||
result = hook.initialize_session(session_data)
|
||||
|
||||
# Output result as JSON
|
||||
print(json.dumps(result, indent=2))
|
||||
|
||||
except Exception as e:
|
||||
# Output error as JSON
|
||||
error_result = {
|
||||
'superclaude_enabled': False,
|
||||
'error': str(e),
|
||||
'fallback_mode': True
|
||||
}
|
||||
print(json.dumps(error_result, indent=2))
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,25 +0,0 @@
|
||||
"""
|
||||
SuperClaude-Lite Shared Infrastructure
|
||||
|
||||
Core components for the executable SuperClaude intelligence framework.
|
||||
Provides shared functionality across all 7 Claude Code hooks.
|
||||
"""
|
||||
|
||||
__version__ = "1.0.0"
|
||||
__author__ = "SuperClaude Framework"
|
||||
|
||||
from .yaml_loader import UnifiedConfigLoader
|
||||
from .framework_logic import FrameworkLogic
|
||||
from .pattern_detection import PatternDetector
|
||||
from .mcp_intelligence import MCPIntelligence
|
||||
from .compression_engine import CompressionEngine
|
||||
from .learning_engine import LearningEngine
|
||||
|
||||
__all__ = [
|
||||
'UnifiedConfigLoader',
|
||||
'FrameworkLogic',
|
||||
'PatternDetector',
|
||||
'MCPIntelligence',
|
||||
'CompressionEngine',
|
||||
'LearningEngine'
|
||||
]
|
||||
@@ -1,662 +0,0 @@
|
||||
"""
|
||||
Compression Engine for SuperClaude-Lite
|
||||
|
||||
Intelligent token optimization implementing MODE_Token_Efficiency.md algorithms
|
||||
with adaptive compression, symbol systems, and quality-gated validation.
|
||||
"""
|
||||
|
||||
import re
|
||||
import json
|
||||
import hashlib
|
||||
from typing import Dict, Any, List, Optional, Tuple, Set
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
from yaml_loader import config_loader
|
||||
|
||||
|
||||
class CompressionLevel(Enum):
|
||||
"""Compression levels from MODE_Token_Efficiency.md."""
|
||||
MINIMAL = "minimal" # 0-40% compression
|
||||
EFFICIENT = "efficient" # 40-70% compression
|
||||
COMPRESSED = "compressed" # 70-85% compression
|
||||
CRITICAL = "critical" # 85-95% compression
|
||||
EMERGENCY = "emergency" # 95%+ compression
|
||||
|
||||
|
||||
class ContentType(Enum):
|
||||
"""Types of content for selective compression."""
|
||||
FRAMEWORK_CONTENT = "framework" # SuperClaude framework - EXCLUDE
|
||||
SESSION_DATA = "session" # Session metadata - COMPRESS
|
||||
USER_CONTENT = "user" # User project files - PRESERVE
|
||||
WORKING_ARTIFACTS = "artifacts" # Analysis results - COMPRESS
|
||||
|
||||
|
||||
@dataclass
|
||||
class CompressionResult:
|
||||
"""Result of compression operation."""
|
||||
original_length: int
|
||||
compressed_length: int
|
||||
compression_ratio: float
|
||||
quality_score: float # 0.0 to 1.0
|
||||
techniques_used: List[str]
|
||||
preservation_score: float # Information preservation
|
||||
processing_time_ms: float
|
||||
|
||||
|
||||
@dataclass
|
||||
class CompressionStrategy:
|
||||
"""Strategy configuration for compression."""
|
||||
level: CompressionLevel
|
||||
symbol_systems_enabled: bool
|
||||
abbreviation_systems_enabled: bool
|
||||
structural_optimization: bool
|
||||
selective_preservation: Dict[str, bool]
|
||||
quality_threshold: float
|
||||
|
||||
|
||||
class CompressionEngine:
|
||||
"""
|
||||
Intelligent token optimization engine implementing MODE_Token_Efficiency.md.
|
||||
|
||||
Features:
|
||||
- 5-level adaptive compression (minimal to emergency)
|
||||
- Symbol systems for mathematical and logical relationships
|
||||
- Abbreviation systems for technical domains
|
||||
- Selective compression with framework/user content protection
|
||||
- Quality-gated validation with ≥95% information preservation
|
||||
- Real-time compression effectiveness monitoring
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
try:
|
||||
self.config = config_loader.load_config('compression')
|
||||
except Exception as e:
|
||||
# Fallback to default configuration if config loading fails
|
||||
self.config = {'compression_levels': {}, 'selective_compression': {}}
|
||||
|
||||
self.symbol_mappings = self._load_symbol_mappings()
|
||||
self.abbreviation_mappings = self._load_abbreviation_mappings()
|
||||
self.compression_cache = {}
|
||||
self.performance_metrics = {}
|
||||
|
||||
def _load_symbol_mappings(self) -> Dict[str, str]:
|
||||
"""Load symbol system mappings from configuration."""
|
||||
return {
|
||||
# Core Logic & Flow
|
||||
'leads to': '→',
|
||||
'implies': '→',
|
||||
'transforms to': '⇒',
|
||||
'converts to': '⇒',
|
||||
'rollback': '←',
|
||||
'reverse': '←',
|
||||
'bidirectional': '⇄',
|
||||
'sync': '⇄',
|
||||
'and': '&',
|
||||
'combine': '&',
|
||||
'separator': '|',
|
||||
'or': '|',
|
||||
'define': ':',
|
||||
'specify': ':',
|
||||
'sequence': '»',
|
||||
'then': '»',
|
||||
'therefore': '∴',
|
||||
'because': '∵',
|
||||
'equivalent': '≡',
|
||||
'approximately': '≈',
|
||||
'not equal': '≠',
|
||||
|
||||
# Status & Progress
|
||||
'completed': '✅',
|
||||
'passed': '✅',
|
||||
'failed': '❌',
|
||||
'error': '❌',
|
||||
'warning': '⚠️',
|
||||
'information': 'ℹ️',
|
||||
'in progress': '🔄',
|
||||
'processing': '🔄',
|
||||
'waiting': '⏳',
|
||||
'pending': '⏳',
|
||||
'critical': '🚨',
|
||||
'urgent': '🚨',
|
||||
'target': '🎯',
|
||||
'goal': '🎯',
|
||||
'metrics': '📊',
|
||||
'data': '📊',
|
||||
'insight': '💡',
|
||||
'learning': '💡',
|
||||
|
||||
# Technical Domains
|
||||
'performance': '⚡',
|
||||
'optimization': '⚡',
|
||||
'analysis': '🔍',
|
||||
'investigation': '🔍',
|
||||
'configuration': '🔧',
|
||||
'setup': '🔧',
|
||||
'security': '🛡️',
|
||||
'protection': '🛡️',
|
||||
'deployment': '📦',
|
||||
'package': '📦',
|
||||
'design': '🎨',
|
||||
'frontend': '🎨',
|
||||
'network': '🌐',
|
||||
'connectivity': '🌐',
|
||||
'mobile': '📱',
|
||||
'responsive': '📱',
|
||||
'architecture': '🏗️',
|
||||
'system structure': '🏗️',
|
||||
'components': '🧩',
|
||||
'modular': '🧩'
|
||||
}
|
||||
|
||||
def _load_abbreviation_mappings(self) -> Dict[str, str]:
|
||||
"""Load abbreviation system mappings from configuration."""
|
||||
return {
|
||||
# System & Architecture
|
||||
'configuration': 'cfg',
|
||||
'settings': 'cfg',
|
||||
'implementation': 'impl',
|
||||
'code structure': 'impl',
|
||||
'architecture': 'arch',
|
||||
'system design': 'arch',
|
||||
'performance': 'perf',
|
||||
'optimization': 'perf',
|
||||
'operations': 'ops',
|
||||
'deployment': 'ops',
|
||||
'environment': 'env',
|
||||
'runtime context': 'env',
|
||||
|
||||
# Development Process
|
||||
'requirements': 'req',
|
||||
'dependencies': 'deps',
|
||||
'packages': 'deps',
|
||||
'validation': 'val',
|
||||
'verification': 'val',
|
||||
'testing': 'test',
|
||||
'quality assurance': 'test',
|
||||
'documentation': 'docs',
|
||||
'guides': 'docs',
|
||||
'standards': 'std',
|
||||
'conventions': 'std',
|
||||
|
||||
# Quality & Analysis
|
||||
'quality': 'qual',
|
||||
'maintainability': 'qual',
|
||||
'security': 'sec',
|
||||
'safety measures': 'sec',
|
||||
'error': 'err',
|
||||
'exception handling': 'err',
|
||||
'recovery': 'rec',
|
||||
'resilience': 'rec',
|
||||
'severity': 'sev',
|
||||
'priority level': 'sev',
|
||||
'optimization': 'opt',
|
||||
'improvement': 'opt'
|
||||
}
|
||||
|
||||
def determine_compression_level(self, context: Dict[str, Any]) -> CompressionLevel:
|
||||
"""
|
||||
Determine appropriate compression level based on context.
|
||||
|
||||
Args:
|
||||
context: Session context including resource usage, conversation length, etc.
|
||||
|
||||
Returns:
|
||||
Appropriate CompressionLevel for the situation
|
||||
"""
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
conversation_length = context.get('conversation_length', 0)
|
||||
user_requests_brevity = context.get('user_requests_brevity', False)
|
||||
complexity_score = context.get('complexity_score', 0.0)
|
||||
|
||||
# Emergency compression for critical resource constraints
|
||||
if resource_usage >= 95:
|
||||
return CompressionLevel.EMERGENCY
|
||||
|
||||
# Critical compression for high resource usage
|
||||
if resource_usage >= 85 or conversation_length > 200:
|
||||
return CompressionLevel.CRITICAL
|
||||
|
||||
# Compressed level for moderate constraints
|
||||
if resource_usage >= 70 or conversation_length > 100 or user_requests_brevity:
|
||||
return CompressionLevel.COMPRESSED
|
||||
|
||||
# Efficient level for mild constraints or complex operations
|
||||
if resource_usage >= 40 or complexity_score > 0.6:
|
||||
return CompressionLevel.EFFICIENT
|
||||
|
||||
# Minimal compression for normal operations
|
||||
return CompressionLevel.MINIMAL
|
||||
|
||||
def classify_content(self, content: str, metadata: Dict[str, Any]) -> ContentType:
|
||||
"""
|
||||
Classify content type for selective compression.
|
||||
|
||||
Args:
|
||||
content: Content to classify
|
||||
metadata: Metadata about the content (file paths, context, etc.)
|
||||
|
||||
Returns:
|
||||
ContentType for compression decision making
|
||||
"""
|
||||
file_path = metadata.get('file_path', '')
|
||||
context_type = metadata.get('context_type', '')
|
||||
|
||||
# Framework content - complete exclusion
|
||||
framework_patterns = [
|
||||
'~/.claude/',
|
||||
'.claude/',
|
||||
'SuperClaude/',
|
||||
'CLAUDE.md',
|
||||
'FLAGS.md',
|
||||
'PRINCIPLES.md',
|
||||
'ORCHESTRATOR.md',
|
||||
'MCP_',
|
||||
'MODE_',
|
||||
'SESSION_LIFECYCLE.md'
|
||||
]
|
||||
|
||||
for pattern in framework_patterns:
|
||||
if pattern in file_path or pattern in content:
|
||||
return ContentType.FRAMEWORK_CONTENT
|
||||
|
||||
# Session data - apply compression
|
||||
if context_type in ['session_metadata', 'checkpoint_data', 'cache_content']:
|
||||
return ContentType.SESSION_DATA
|
||||
|
||||
# Working artifacts - apply compression
|
||||
if context_type in ['analysis_results', 'processing_data', 'working_artifacts']:
|
||||
return ContentType.WORKING_ARTIFACTS
|
||||
|
||||
# User content - preserve with minimal compression only
|
||||
user_patterns = [
|
||||
'project_files',
|
||||
'user_documentation',
|
||||
'source_code',
|
||||
'configuration_files',
|
||||
'custom_content'
|
||||
]
|
||||
|
||||
for pattern in user_patterns:
|
||||
if pattern in context_type or pattern in file_path:
|
||||
return ContentType.USER_CONTENT
|
||||
|
||||
# Default to user content preservation
|
||||
return ContentType.USER_CONTENT
|
||||
|
||||
def compress_content(self,
|
||||
content: str,
|
||||
context: Dict[str, Any],
|
||||
metadata: Dict[str, Any] = None) -> CompressionResult:
|
||||
"""
|
||||
Compress content with intelligent optimization.
|
||||
|
||||
Args:
|
||||
content: Content to compress
|
||||
context: Session context for compression level determination
|
||||
metadata: Content metadata for selective compression
|
||||
|
||||
Returns:
|
||||
CompressionResult with metrics and compressed content
|
||||
"""
|
||||
import time
|
||||
start_time = time.time()
|
||||
|
||||
if metadata is None:
|
||||
metadata = {}
|
||||
|
||||
# Classify content type
|
||||
content_type = self.classify_content(content, metadata)
|
||||
|
||||
# Framework content - no compression
|
||||
if content_type == ContentType.FRAMEWORK_CONTENT:
|
||||
return CompressionResult(
|
||||
original_length=len(content),
|
||||
compressed_length=len(content),
|
||||
compression_ratio=0.0,
|
||||
quality_score=1.0,
|
||||
techniques_used=['framework_exclusion'],
|
||||
preservation_score=1.0,
|
||||
processing_time_ms=(time.time() - start_time) * 1000
|
||||
)
|
||||
|
||||
# User content - minimal compression only
|
||||
if content_type == ContentType.USER_CONTENT:
|
||||
compression_level = CompressionLevel.MINIMAL
|
||||
else:
|
||||
compression_level = self.determine_compression_level(context)
|
||||
|
||||
# Create compression strategy
|
||||
strategy = self._create_compression_strategy(compression_level, content_type)
|
||||
|
||||
# Apply compression techniques
|
||||
compressed_content = content
|
||||
techniques_used = []
|
||||
|
||||
if strategy.symbol_systems_enabled:
|
||||
compressed_content, symbol_techniques = self._apply_symbol_systems(compressed_content)
|
||||
techniques_used.extend(symbol_techniques)
|
||||
|
||||
if strategy.abbreviation_systems_enabled:
|
||||
compressed_content, abbrev_techniques = self._apply_abbreviation_systems(compressed_content)
|
||||
techniques_used.extend(abbrev_techniques)
|
||||
|
||||
if strategy.structural_optimization:
|
||||
compressed_content, struct_techniques = self._apply_structural_optimization(
|
||||
compressed_content, compression_level
|
||||
)
|
||||
techniques_used.extend(struct_techniques)
|
||||
|
||||
# Calculate metrics
|
||||
original_length = len(content)
|
||||
compressed_length = len(compressed_content)
|
||||
compression_ratio = (original_length - compressed_length) / original_length if original_length > 0 else 0.0
|
||||
|
||||
# Quality validation
|
||||
quality_score = self._validate_compression_quality(content, compressed_content, strategy)
|
||||
preservation_score = self._calculate_information_preservation(content, compressed_content)
|
||||
|
||||
processing_time = (time.time() - start_time) * 1000
|
||||
|
||||
# Cache result for performance
|
||||
cache_key = hashlib.md5(content.encode()).hexdigest()
|
||||
self.compression_cache[cache_key] = compressed_content
|
||||
|
||||
return CompressionResult(
|
||||
original_length=original_length,
|
||||
compressed_length=compressed_length,
|
||||
compression_ratio=compression_ratio,
|
||||
quality_score=quality_score,
|
||||
techniques_used=techniques_used,
|
||||
preservation_score=preservation_score,
|
||||
processing_time_ms=processing_time
|
||||
)
|
||||
|
||||
def _create_compression_strategy(self, level: CompressionLevel, content_type: ContentType) -> CompressionStrategy:
|
||||
"""Create compression strategy based on level and content type."""
|
||||
level_configs = {
|
||||
CompressionLevel.MINIMAL: {
|
||||
'symbol_systems': True, # Changed: Enable basic optimizations even for minimal
|
||||
'abbreviations': False,
|
||||
'structural': True, # Changed: Enable basic structural optimization
|
||||
'quality_threshold': 0.98
|
||||
},
|
||||
CompressionLevel.EFFICIENT: {
|
||||
'symbol_systems': True,
|
||||
'abbreviations': False,
|
||||
'structural': True,
|
||||
'quality_threshold': 0.95
|
||||
},
|
||||
CompressionLevel.COMPRESSED: {
|
||||
'symbol_systems': True,
|
||||
'abbreviations': True,
|
||||
'structural': True,
|
||||
'quality_threshold': 0.90
|
||||
},
|
||||
CompressionLevel.CRITICAL: {
|
||||
'symbol_systems': True,
|
||||
'abbreviations': True,
|
||||
'structural': True,
|
||||
'quality_threshold': 0.85
|
||||
},
|
||||
CompressionLevel.EMERGENCY: {
|
||||
'symbol_systems': True,
|
||||
'abbreviations': True,
|
||||
'structural': True,
|
||||
'quality_threshold': 0.80
|
||||
}
|
||||
}
|
||||
|
||||
config = level_configs[level]
|
||||
|
||||
# Adjust for content type
|
||||
if content_type == ContentType.USER_CONTENT:
|
||||
# More conservative for user content
|
||||
config['quality_threshold'] = min(config['quality_threshold'] + 0.1, 1.0)
|
||||
|
||||
return CompressionStrategy(
|
||||
level=level,
|
||||
symbol_systems_enabled=config['symbol_systems'],
|
||||
abbreviation_systems_enabled=config['abbreviations'],
|
||||
structural_optimization=config['structural'],
|
||||
selective_preservation={},
|
||||
quality_threshold=config['quality_threshold']
|
||||
)
|
||||
|
||||
def _apply_symbol_systems(self, content: str) -> Tuple[str, List[str]]:
|
||||
"""Apply symbol system replacements."""
|
||||
if not content or not isinstance(content, str):
|
||||
return content or "", []
|
||||
|
||||
compressed = content
|
||||
techniques = []
|
||||
|
||||
try:
|
||||
# Apply symbol mappings with word boundary protection
|
||||
for phrase, symbol in self.symbol_mappings.items():
|
||||
if not phrase or not symbol:
|
||||
continue
|
||||
|
||||
pattern = r'\b' + re.escape(phrase) + r'\b'
|
||||
if re.search(pattern, compressed, re.IGNORECASE):
|
||||
compressed = re.sub(pattern, symbol, compressed, flags=re.IGNORECASE)
|
||||
techniques.append(f"symbol_{phrase.replace(' ', '_')}")
|
||||
except Exception as e:
|
||||
# If regex fails, return original content
|
||||
return content, []
|
||||
|
||||
return compressed, techniques
|
||||
|
||||
def _apply_abbreviation_systems(self, content: str) -> Tuple[str, List[str]]:
|
||||
"""Apply abbreviation system replacements."""
|
||||
if not content or not isinstance(content, str):
|
||||
return content or "", []
|
||||
|
||||
compressed = content
|
||||
techniques = []
|
||||
|
||||
try:
|
||||
# Apply abbreviation mappings with context awareness
|
||||
for phrase, abbrev in self.abbreviation_mappings.items():
|
||||
if not phrase or not abbrev:
|
||||
continue
|
||||
|
||||
pattern = r'\b' + re.escape(phrase) + r'\b'
|
||||
if re.search(pattern, compressed, re.IGNORECASE):
|
||||
compressed = re.sub(pattern, abbrev, compressed, flags=re.IGNORECASE)
|
||||
techniques.append(f"abbrev_{phrase.replace(' ', '_')}")
|
||||
except Exception as e:
|
||||
# If regex fails, return original content
|
||||
return content, []
|
||||
|
||||
return compressed, techniques
|
||||
|
||||
def _apply_structural_optimization(self, content: str, level: CompressionLevel) -> Tuple[str, List[str]]:
|
||||
"""Apply structural optimizations for token efficiency."""
|
||||
if not content or not isinstance(content, str):
|
||||
return content or "", []
|
||||
|
||||
compressed = content
|
||||
techniques = []
|
||||
|
||||
try:
|
||||
# Always remove redundant whitespace for any level
|
||||
if re.search(r'\s{2,}|\n\s*\n', compressed):
|
||||
compressed = re.sub(r'\s+', ' ', compressed)
|
||||
compressed = re.sub(r'\n\s*\n', '\n', compressed)
|
||||
techniques.append('whitespace_optimization')
|
||||
|
||||
# Phrase simplification for compressed levels and above
|
||||
if level in [CompressionLevel.COMPRESSED, CompressionLevel.CRITICAL, CompressionLevel.EMERGENCY]:
|
||||
# Simplify common phrases FIRST
|
||||
phrase_simplifications = {
|
||||
r'in order to': 'to',
|
||||
r'it is important to note that': 'note:',
|
||||
r'please be aware that': 'note:',
|
||||
r'it should be noted that': 'note:',
|
||||
r'for the purpose of': 'for',
|
||||
r'with regard to': 'regarding',
|
||||
r'in relation to': 'regarding'
|
||||
}
|
||||
|
||||
for pattern, replacement in phrase_simplifications.items():
|
||||
if re.search(pattern, compressed, re.IGNORECASE):
|
||||
compressed = re.sub(pattern, replacement, compressed, flags=re.IGNORECASE)
|
||||
techniques.append('phrase_simplification')
|
||||
|
||||
# Remove redundant words AFTER phrase simplification
|
||||
if re.search(r'\b(the|a|an)\s+', compressed, re.IGNORECASE):
|
||||
compressed = re.sub(r'\b(the|a|an)\s+', '', compressed, flags=re.IGNORECASE)
|
||||
techniques.append('article_removal')
|
||||
except Exception as e:
|
||||
# If regex fails, return original content
|
||||
return content, []
|
||||
|
||||
return compressed, techniques
|
||||
|
||||
def _validate_compression_quality(self, original: str, compressed: str, strategy: CompressionStrategy) -> float:
|
||||
"""Validate compression quality against thresholds."""
|
||||
# Simple quality heuristics (real implementation would be more sophisticated)
|
||||
|
||||
# Check if key information is preserved
|
||||
original_words = set(re.findall(r'\b\w+\b', original.lower()))
|
||||
compressed_words = set(re.findall(r'\b\w+\b', compressed.lower()))
|
||||
|
||||
# Word preservation ratio
|
||||
word_preservation = len(compressed_words & original_words) / len(original_words) if original_words else 1.0
|
||||
|
||||
# Length efficiency (not too aggressive)
|
||||
length_ratio = len(compressed) / len(original) if original else 1.0
|
||||
|
||||
# Penalize over-compression
|
||||
if length_ratio < 0.3:
|
||||
word_preservation *= 0.8
|
||||
|
||||
quality_score = (word_preservation * 0.7) + (min(length_ratio * 2, 1.0) * 0.3)
|
||||
|
||||
return min(quality_score, 1.0)
|
||||
|
||||
def _calculate_information_preservation(self, original: str, compressed: str) -> float:
|
||||
"""Calculate information preservation score."""
|
||||
# Enhanced preservation metric based on multiple factors
|
||||
|
||||
# Extract key concepts (capitalized words, technical terms, file extensions)
|
||||
original_concepts = set(re.findall(r'\b[A-Z][a-z]+\b|\b\w+\.(js|py|md|yaml|json)\b|\b\w*[A-Z]\w*\b', original))
|
||||
compressed_concepts = set(re.findall(r'\b[A-Z][a-z]+\b|\b\w+\.(js|py|md|yaml|json)\b|\b\w*[A-Z]\w*\b', compressed))
|
||||
|
||||
# Also check for symbols that represent preserved concepts
|
||||
symbol_mappings = {
|
||||
'→': ['leads', 'implies', 'transforms', 'converts'],
|
||||
'⚡': ['performance', 'optimization', 'speed'],
|
||||
'🛡️': ['security', 'protection', 'safety'],
|
||||
'❌': ['error', 'failed', 'exception'],
|
||||
'⚠️': ['warning', 'caution'],
|
||||
'🔍': ['analysis', 'investigation', 'search'],
|
||||
'🔧': ['configuration', 'setup', 'tools'],
|
||||
'📦': ['deployment', 'package', 'bundle'],
|
||||
'🎨': ['design', 'frontend', 'ui'],
|
||||
'🌐': ['network', 'web', 'connectivity'],
|
||||
'📱': ['mobile', 'responsive'],
|
||||
'🏗️': ['architecture', 'structure'],
|
||||
'🧩': ['components', 'modular']
|
||||
}
|
||||
|
||||
# Count preserved concepts through symbols
|
||||
symbol_preserved_concepts = set()
|
||||
for symbol, related_words in symbol_mappings.items():
|
||||
if symbol in compressed:
|
||||
for word in related_words:
|
||||
if word in original.lower():
|
||||
symbol_preserved_concepts.add(word)
|
||||
|
||||
# Extract important words (longer than 4 characters, not common words)
|
||||
common_words = {'this', 'that', 'with', 'have', 'will', 'been', 'from', 'they',
|
||||
'know', 'want', 'good', 'much', 'some', 'time', 'very', 'when',
|
||||
'come', 'here', 'just', 'like', 'long', 'make', 'many', 'over',
|
||||
'such', 'take', 'than', 'them', 'well', 'were', 'through'}
|
||||
original_words = set(word.lower() for word in re.findall(r'\b\w{4,}\b', original)
|
||||
if word.lower() not in common_words)
|
||||
compressed_words = set(word.lower() for word in re.findall(r'\b\w{4,}\b', compressed)
|
||||
if word.lower() not in common_words)
|
||||
|
||||
# Add symbol-preserved concepts to compressed words
|
||||
compressed_words.update(symbol_preserved_concepts)
|
||||
|
||||
# Calculate concept preservation
|
||||
if original_concepts:
|
||||
concept_preservation = len(compressed_concepts & original_concepts) / len(original_concepts)
|
||||
else:
|
||||
concept_preservation = 1.0
|
||||
|
||||
# Calculate important word preservation
|
||||
if original_words:
|
||||
word_preservation = len(compressed_words & original_words) / len(original_words)
|
||||
else:
|
||||
word_preservation = 1.0
|
||||
|
||||
# Weight concept preservation more heavily, but be more generous
|
||||
total_preservation = (concept_preservation * 0.6) + (word_preservation * 0.4)
|
||||
|
||||
# Bonus for symbol usage that preserves meaning
|
||||
symbol_bonus = min(len(symbol_preserved_concepts) * 0.05, 0.15)
|
||||
total_preservation += symbol_bonus
|
||||
|
||||
# Apply length penalty for over-compression
|
||||
length_ratio = len(compressed) / len(original) if len(original) > 0 else 1.0
|
||||
if length_ratio < 0.2: # Heavily penalize extreme over-compression
|
||||
total_preservation *= 0.6
|
||||
elif length_ratio < 0.4: # Penalize significant over-compression
|
||||
total_preservation *= 0.8
|
||||
elif length_ratio < 0.5: # Moderate penalty for over-compression
|
||||
total_preservation *= 0.9
|
||||
|
||||
return min(total_preservation, 1.0)
|
||||
|
||||
def get_compression_recommendations(self, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Get recommendations for optimizing compression."""
|
||||
recommendations = []
|
||||
|
||||
current_level = self.determine_compression_level(context)
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
|
||||
# Resource-based recommendations
|
||||
if resource_usage > 85:
|
||||
recommendations.append("Enable emergency compression mode for critical resource constraints")
|
||||
elif resource_usage > 70:
|
||||
recommendations.append("Consider compressed mode for better resource efficiency")
|
||||
elif resource_usage < 40:
|
||||
recommendations.append("Resource usage low - minimal compression sufficient")
|
||||
|
||||
# Performance recommendations
|
||||
if context.get('processing_time_ms', 0) > 500:
|
||||
recommendations.append("Compression processing time high - consider caching strategies")
|
||||
|
||||
return {
|
||||
'current_level': current_level.value,
|
||||
'recommendations': recommendations,
|
||||
'estimated_savings': self._estimate_compression_savings(current_level),
|
||||
'quality_impact': self._estimate_quality_impact(current_level),
|
||||
'performance_metrics': self.performance_metrics
|
||||
}
|
||||
|
||||
def _estimate_compression_savings(self, level: CompressionLevel) -> Dict[str, float]:
|
||||
"""Estimate compression savings for a given level."""
|
||||
savings_map = {
|
||||
CompressionLevel.MINIMAL: {'token_reduction': 0.15, 'time_savings': 0.05},
|
||||
CompressionLevel.EFFICIENT: {'token_reduction': 0.40, 'time_savings': 0.15},
|
||||
CompressionLevel.COMPRESSED: {'token_reduction': 0.60, 'time_savings': 0.25},
|
||||
CompressionLevel.CRITICAL: {'token_reduction': 0.75, 'time_savings': 0.35},
|
||||
CompressionLevel.EMERGENCY: {'token_reduction': 0.85, 'time_savings': 0.45}
|
||||
}
|
||||
return savings_map.get(level, {'token_reduction': 0.0, 'time_savings': 0.0})
|
||||
|
||||
def _estimate_quality_impact(self, level: CompressionLevel) -> float:
|
||||
"""Estimate quality preservation for a given level."""
|
||||
quality_map = {
|
||||
CompressionLevel.MINIMAL: 0.98,
|
||||
CompressionLevel.EFFICIENT: 0.95,
|
||||
CompressionLevel.COMPRESSED: 0.90,
|
||||
CompressionLevel.CRITICAL: 0.85,
|
||||
CompressionLevel.EMERGENCY: 0.80
|
||||
}
|
||||
return quality_map.get(level, 0.95)
|
||||
@@ -1,343 +0,0 @@
|
||||
"""
|
||||
Core SuperClaude Framework Logic
|
||||
|
||||
Implements the core decision-making algorithms from the SuperClaude framework,
|
||||
including RULES.md, PRINCIPLES.md, and ORCHESTRATOR.md patterns.
|
||||
"""
|
||||
|
||||
import json
|
||||
import time
|
||||
from typing import Dict, Any, List, Optional, Tuple, Union
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
from yaml_loader import config_loader
|
||||
|
||||
|
||||
class OperationType(Enum):
|
||||
"""Types of operations SuperClaude can perform."""
|
||||
READ = "read"
|
||||
WRITE = "write"
|
||||
EDIT = "edit"
|
||||
ANALYZE = "analyze"
|
||||
BUILD = "build"
|
||||
TEST = "test"
|
||||
DEPLOY = "deploy"
|
||||
REFACTOR = "refactor"
|
||||
|
||||
|
||||
class RiskLevel(Enum):
|
||||
"""Risk levels for operations."""
|
||||
LOW = "low"
|
||||
MEDIUM = "medium"
|
||||
HIGH = "high"
|
||||
CRITICAL = "critical"
|
||||
|
||||
|
||||
@dataclass
|
||||
class OperationContext:
|
||||
"""Context information for an operation."""
|
||||
operation_type: OperationType
|
||||
file_count: int
|
||||
directory_count: int
|
||||
has_tests: bool
|
||||
is_production: bool
|
||||
user_expertise: str # beginner, intermediate, expert
|
||||
project_type: str # web, api, cli, library, etc.
|
||||
complexity_score: float # 0.0 to 1.0
|
||||
risk_level: RiskLevel
|
||||
|
||||
|
||||
@dataclass
|
||||
class ValidationResult:
|
||||
"""Result of validation checks."""
|
||||
is_valid: bool
|
||||
issues: List[str]
|
||||
warnings: List[str]
|
||||
suggestions: List[str]
|
||||
quality_score: float # 0.0 to 1.0
|
||||
|
||||
|
||||
class FrameworkLogic:
|
||||
"""
|
||||
Core SuperClaude framework logic implementation.
|
||||
|
||||
Encapsulates decision-making algorithms from:
|
||||
- RULES.md: Operational rules and security patterns
|
||||
- PRINCIPLES.md: Development principles and quality standards
|
||||
- ORCHESTRATOR.md: Intelligent routing and coordination
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Load performance targets from SuperClaude configuration
|
||||
self.performance_targets = {}
|
||||
|
||||
# Get hook-specific performance targets
|
||||
self.performance_targets['session_start_ms'] = config_loader.get_hook_config(
|
||||
'session_start', 'performance_target_ms', 50
|
||||
)
|
||||
self.performance_targets['tool_routing_ms'] = config_loader.get_hook_config(
|
||||
'pre_tool_use', 'performance_target_ms', 200
|
||||
)
|
||||
self.performance_targets['validation_ms'] = config_loader.get_hook_config(
|
||||
'post_tool_use', 'performance_target_ms', 100
|
||||
)
|
||||
self.performance_targets['compression_ms'] = config_loader.get_hook_config(
|
||||
'pre_compact', 'performance_target_ms', 150
|
||||
)
|
||||
|
||||
# Load additional performance settings from global configuration
|
||||
global_perf = config_loader.get_performance_targets()
|
||||
if global_perf:
|
||||
self.performance_targets.update(global_perf)
|
||||
|
||||
def should_use_read_before_write(self, context: OperationContext) -> bool:
|
||||
"""
|
||||
RULES.md: Always use Read tool before Write or Edit operations.
|
||||
"""
|
||||
return context.operation_type in [OperationType.WRITE, OperationType.EDIT]
|
||||
|
||||
def calculate_complexity_score(self, operation_data: Dict[str, Any]) -> float:
|
||||
"""
|
||||
Calculate operation complexity score (0.0 to 1.0).
|
||||
|
||||
Factors:
|
||||
- File count and types
|
||||
- Operation scope
|
||||
- Dependencies
|
||||
- Risk factors
|
||||
"""
|
||||
score = 0.0
|
||||
|
||||
# File count factor (0.0 to 0.3)
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
if file_count <= 1:
|
||||
score += 0.0
|
||||
elif file_count <= 3:
|
||||
score += 0.1
|
||||
elif file_count <= 10:
|
||||
score += 0.2
|
||||
else:
|
||||
score += 0.3
|
||||
|
||||
# Directory factor (0.0 to 0.2)
|
||||
dir_count = operation_data.get('directory_count', 1)
|
||||
if dir_count > 2:
|
||||
score += 0.2
|
||||
elif dir_count > 1:
|
||||
score += 0.1
|
||||
|
||||
# Operation type factor (0.0 to 0.3)
|
||||
op_type = operation_data.get('operation_type', '')
|
||||
if op_type in ['refactor', 'architecture', 'system-wide']:
|
||||
score += 0.3
|
||||
elif op_type in ['build', 'implement', 'migrate']:
|
||||
score += 0.2
|
||||
elif op_type in ['fix', 'update', 'improve']:
|
||||
score += 0.1
|
||||
|
||||
# Language/framework factor (0.0 to 0.2)
|
||||
if operation_data.get('multi_language', False):
|
||||
score += 0.2
|
||||
elif operation_data.get('framework_changes', False):
|
||||
score += 0.1
|
||||
|
||||
return min(score, 1.0)
|
||||
|
||||
def assess_risk_level(self, context: OperationContext) -> RiskLevel:
|
||||
"""
|
||||
Assess risk level based on operation context.
|
||||
"""
|
||||
if context.is_production:
|
||||
return RiskLevel.HIGH
|
||||
|
||||
if context.complexity_score > 0.7:
|
||||
return RiskLevel.HIGH
|
||||
elif context.complexity_score > 0.4:
|
||||
return RiskLevel.MEDIUM
|
||||
elif context.file_count > 10:
|
||||
return RiskLevel.MEDIUM
|
||||
else:
|
||||
return RiskLevel.LOW
|
||||
|
||||
def should_enable_validation(self, context: OperationContext) -> bool:
|
||||
"""
|
||||
ORCHESTRATOR.md: Enable validation for production code or high-risk operations.
|
||||
"""
|
||||
return (
|
||||
context.is_production or
|
||||
context.risk_level in [RiskLevel.HIGH, RiskLevel.CRITICAL] or
|
||||
context.operation_type in [OperationType.DEPLOY, OperationType.REFACTOR]
|
||||
)
|
||||
|
||||
def should_enable_delegation(self, context: OperationContext) -> Tuple[bool, str]:
|
||||
"""
|
||||
ORCHESTRATOR.md: Enable delegation for multi-file operations.
|
||||
|
||||
Returns:
|
||||
(should_delegate, delegation_strategy)
|
||||
"""
|
||||
if context.file_count > 3:
|
||||
return True, "files"
|
||||
elif context.directory_count > 2:
|
||||
return True, "folders"
|
||||
elif context.complexity_score > 0.4:
|
||||
return True, "auto"
|
||||
else:
|
||||
return False, "none"
|
||||
|
||||
def validate_operation(self, operation_data: Dict[str, Any]) -> ValidationResult:
|
||||
"""
|
||||
PRINCIPLES.md: Validate operation against core principles.
|
||||
"""
|
||||
issues = []
|
||||
warnings = []
|
||||
suggestions = []
|
||||
quality_score = 1.0
|
||||
|
||||
# Check for evidence-based decision making
|
||||
if 'evidence' not in operation_data:
|
||||
warnings.append("No evidence provided for decision")
|
||||
quality_score -= 0.1
|
||||
|
||||
# Check for proper error handling
|
||||
if operation_data.get('operation_type') in ['write', 'edit', 'deploy']:
|
||||
if not operation_data.get('has_error_handling', False):
|
||||
issues.append("Error handling not implemented")
|
||||
quality_score -= 0.2
|
||||
|
||||
# Check for test coverage
|
||||
if operation_data.get('affects_logic', False):
|
||||
if not operation_data.get('has_tests', False):
|
||||
warnings.append("No tests found for logic changes")
|
||||
quality_score -= 0.1
|
||||
suggestions.append("Add unit tests for new logic")
|
||||
|
||||
# Check for documentation
|
||||
if operation_data.get('is_public_api', False):
|
||||
if not operation_data.get('has_documentation', False):
|
||||
warnings.append("Public API lacks documentation")
|
||||
quality_score -= 0.1
|
||||
suggestions.append("Add API documentation")
|
||||
|
||||
# Security checks
|
||||
if operation_data.get('handles_user_input', False):
|
||||
if not operation_data.get('has_input_validation', False):
|
||||
issues.append("User input handling without validation")
|
||||
quality_score -= 0.3
|
||||
|
||||
is_valid = len(issues) == 0 and quality_score >= 0.7
|
||||
|
||||
return ValidationResult(
|
||||
is_valid=is_valid,
|
||||
issues=issues,
|
||||
warnings=warnings,
|
||||
suggestions=suggestions,
|
||||
quality_score=max(quality_score, 0.0)
|
||||
)
|
||||
|
||||
def determine_thinking_mode(self, context: OperationContext) -> Optional[str]:
|
||||
"""
|
||||
FLAGS.md: Determine appropriate thinking mode based on complexity.
|
||||
"""
|
||||
if context.complexity_score >= 0.8:
|
||||
return "--ultrathink"
|
||||
elif context.complexity_score >= 0.6:
|
||||
return "--think-hard"
|
||||
elif context.complexity_score >= 0.3:
|
||||
return "--think"
|
||||
else:
|
||||
return None
|
||||
|
||||
def should_enable_efficiency_mode(self, session_data: Dict[str, Any]) -> bool:
|
||||
"""
|
||||
MODE_Token_Efficiency.md: Enable efficiency mode based on resource usage.
|
||||
"""
|
||||
resource_usage = session_data.get('resource_usage_percent', 0)
|
||||
conversation_length = session_data.get('conversation_length', 0)
|
||||
|
||||
return (
|
||||
resource_usage > 75 or
|
||||
conversation_length > 100 or
|
||||
session_data.get('user_requests_brevity', False)
|
||||
)
|
||||
|
||||
def get_quality_gates(self, context: OperationContext) -> List[str]:
|
||||
"""
|
||||
ORCHESTRATOR.md: Get appropriate quality gates for operation.
|
||||
"""
|
||||
gates = ['syntax_validation']
|
||||
|
||||
if context.operation_type in [OperationType.WRITE, OperationType.EDIT]:
|
||||
gates.extend(['type_analysis', 'code_quality'])
|
||||
|
||||
if self.should_enable_validation(context):
|
||||
gates.extend(['security_assessment', 'performance_analysis'])
|
||||
|
||||
if context.has_tests:
|
||||
gates.append('test_validation')
|
||||
|
||||
if context.operation_type == OperationType.DEPLOY:
|
||||
gates.extend(['integration_testing', 'deployment_validation'])
|
||||
|
||||
return gates
|
||||
|
||||
def estimate_performance_impact(self, context: OperationContext) -> Dict[str, Any]:
|
||||
"""
|
||||
Estimate performance impact and suggested optimizations.
|
||||
"""
|
||||
base_time = 100 # ms
|
||||
|
||||
# Calculate estimated time based on complexity
|
||||
estimated_time = base_time * (1 + context.complexity_score * 3)
|
||||
|
||||
# Factor in file count
|
||||
if context.file_count > 5:
|
||||
estimated_time *= 1.5
|
||||
|
||||
# Suggest optimizations
|
||||
optimizations = []
|
||||
if context.file_count > 3:
|
||||
optimizations.append("Consider parallel processing")
|
||||
if context.complexity_score > 0.6:
|
||||
optimizations.append("Enable delegation mode")
|
||||
if context.directory_count > 2:
|
||||
optimizations.append("Use folder-based delegation")
|
||||
|
||||
return {
|
||||
'estimated_time_ms': int(estimated_time),
|
||||
'performance_risk': 'high' if estimated_time > 1000 else 'low',
|
||||
'suggested_optimizations': optimizations,
|
||||
'efficiency_gains_possible': len(optimizations) > 0
|
||||
}
|
||||
|
||||
def apply_superclaude_principles(self, operation_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Apply SuperClaude core principles to operation planning.
|
||||
|
||||
Returns enhanced operation data with principle-based recommendations.
|
||||
"""
|
||||
enhanced_data = operation_data.copy()
|
||||
|
||||
# Evidence > assumptions
|
||||
if 'assumptions' in enhanced_data and not enhanced_data.get('evidence'):
|
||||
enhanced_data['recommendations'] = enhanced_data.get('recommendations', [])
|
||||
enhanced_data['recommendations'].append(
|
||||
"Gather evidence to validate assumptions"
|
||||
)
|
||||
|
||||
# Code > documentation
|
||||
if enhanced_data.get('operation_type') == 'document' and not enhanced_data.get('has_working_code'):
|
||||
enhanced_data['warnings'] = enhanced_data.get('warnings', [])
|
||||
enhanced_data['warnings'].append(
|
||||
"Ensure working code exists before extensive documentation"
|
||||
)
|
||||
|
||||
# Efficiency > verbosity
|
||||
if enhanced_data.get('output_length', 0) > 1000 and not enhanced_data.get('justification_for_length'):
|
||||
enhanced_data['efficiency_suggestions'] = enhanced_data.get('efficiency_suggestions', [])
|
||||
enhanced_data['efficiency_suggestions'].append(
|
||||
"Consider token efficiency techniques for long outputs"
|
||||
)
|
||||
|
||||
return enhanced_data
|
||||
@@ -1,411 +0,0 @@
|
||||
"""
|
||||
Intelligence Engine for SuperClaude Framework-Hooks
|
||||
|
||||
Generic YAML pattern interpreter that provides intelligent services by consuming
|
||||
declarative YAML patterns. Enables hot-reloadable intelligence without code changes.
|
||||
"""
|
||||
|
||||
import time
|
||||
import hashlib
|
||||
from typing import Dict, Any, List, Optional, Tuple, Union
|
||||
from pathlib import Path
|
||||
from yaml_loader import config_loader
|
||||
|
||||
|
||||
class IntelligenceEngine:
|
||||
"""
|
||||
Generic YAML pattern interpreter for declarative intelligence.
|
||||
|
||||
Features:
|
||||
- Hot-reload YAML intelligence patterns
|
||||
- Context-aware pattern matching
|
||||
- Decision tree execution
|
||||
- Recommendation generation
|
||||
- Performance optimization
|
||||
- Multi-pattern coordination
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.patterns: Dict[str, Dict[str, Any]] = {}
|
||||
self.pattern_cache: Dict[str, Any] = {}
|
||||
self.pattern_timestamps: Dict[str, float] = {}
|
||||
self.evaluation_cache: Dict[str, Tuple[Any, float]] = {}
|
||||
self.cache_duration = 300 # 5 minutes
|
||||
|
||||
self._load_all_patterns()
|
||||
|
||||
def _load_all_patterns(self):
|
||||
"""Load all intelligence pattern configurations."""
|
||||
pattern_files = [
|
||||
'intelligence_patterns',
|
||||
'mcp_orchestration',
|
||||
'hook_coordination',
|
||||
'performance_intelligence',
|
||||
'validation_intelligence',
|
||||
'user_experience'
|
||||
]
|
||||
|
||||
for pattern_file in pattern_files:
|
||||
try:
|
||||
patterns = config_loader.load_config(pattern_file)
|
||||
self.patterns[pattern_file] = patterns
|
||||
self.pattern_timestamps[pattern_file] = time.time()
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not load {pattern_file} patterns: {e}")
|
||||
self.patterns[pattern_file] = {}
|
||||
|
||||
def reload_patterns(self, force: bool = False) -> bool:
|
||||
"""
|
||||
Reload patterns if they have changed.
|
||||
|
||||
Args:
|
||||
force: Force reload even if no changes detected
|
||||
|
||||
Returns:
|
||||
True if patterns were reloaded
|
||||
"""
|
||||
reloaded = False
|
||||
|
||||
for pattern_file in self.patterns.keys():
|
||||
try:
|
||||
# Force reload or check for changes
|
||||
if force:
|
||||
patterns = config_loader.load_config(pattern_file, force_reload=True)
|
||||
self.patterns[pattern_file] = patterns
|
||||
self.pattern_timestamps[pattern_file] = time.time()
|
||||
reloaded = True
|
||||
else:
|
||||
# Check if pattern file has been updated
|
||||
current_patterns = config_loader.load_config(pattern_file)
|
||||
pattern_hash = self._compute_pattern_hash(current_patterns)
|
||||
cached_hash = self.pattern_cache.get(f"{pattern_file}_hash")
|
||||
|
||||
if pattern_hash != cached_hash:
|
||||
self.patterns[pattern_file] = current_patterns
|
||||
self.pattern_cache[f"{pattern_file}_hash"] = pattern_hash
|
||||
self.pattern_timestamps[pattern_file] = time.time()
|
||||
reloaded = True
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not reload {pattern_file} patterns: {e}")
|
||||
|
||||
if reloaded:
|
||||
# Clear evaluation cache when patterns change
|
||||
self.evaluation_cache.clear()
|
||||
|
||||
return reloaded
|
||||
|
||||
def _compute_pattern_hash(self, patterns: Dict[str, Any]) -> str:
|
||||
"""Compute hash of pattern configuration for change detection."""
|
||||
pattern_str = str(sorted(patterns.items()))
|
||||
return hashlib.md5(pattern_str.encode()).hexdigest()
|
||||
|
||||
def evaluate_context(self, context: Dict[str, Any], pattern_type: str) -> Dict[str, Any]:
|
||||
"""
|
||||
Evaluate context against patterns to generate recommendations.
|
||||
|
||||
Args:
|
||||
context: Current operation context
|
||||
pattern_type: Type of patterns to evaluate (e.g., 'mcp_orchestration')
|
||||
|
||||
Returns:
|
||||
Dictionary with recommendations and metadata
|
||||
"""
|
||||
# Check cache first
|
||||
cache_key = f"{pattern_type}_{self._compute_context_hash(context)}"
|
||||
if cache_key in self.evaluation_cache:
|
||||
result, timestamp = self.evaluation_cache[cache_key]
|
||||
if time.time() - timestamp < self.cache_duration:
|
||||
return result
|
||||
|
||||
# Hot-reload patterns if needed
|
||||
self.reload_patterns()
|
||||
|
||||
# Get patterns for this type
|
||||
patterns = self.patterns.get(pattern_type, {})
|
||||
if not patterns:
|
||||
return {'recommendations': {}, 'confidence': 0.0, 'source': 'no_patterns'}
|
||||
|
||||
# Evaluate patterns
|
||||
recommendations = {}
|
||||
confidence_scores = []
|
||||
|
||||
if pattern_type == 'mcp_orchestration':
|
||||
recommendations = self._evaluate_mcp_patterns(context, patterns)
|
||||
elif pattern_type == 'hook_coordination':
|
||||
recommendations = self._evaluate_hook_patterns(context, patterns)
|
||||
elif pattern_type == 'performance_intelligence':
|
||||
recommendations = self._evaluate_performance_patterns(context, patterns)
|
||||
elif pattern_type == 'validation_intelligence':
|
||||
recommendations = self._evaluate_validation_patterns(context, patterns)
|
||||
elif pattern_type == 'user_experience':
|
||||
recommendations = self._evaluate_ux_patterns(context, patterns)
|
||||
elif pattern_type == 'intelligence_patterns':
|
||||
recommendations = self._evaluate_learning_patterns(context, patterns)
|
||||
|
||||
# Calculate overall confidence
|
||||
overall_confidence = max(confidence_scores) if confidence_scores else 0.0
|
||||
|
||||
result = {
|
||||
'recommendations': recommendations,
|
||||
'confidence': overall_confidence,
|
||||
'source': pattern_type,
|
||||
'timestamp': time.time()
|
||||
}
|
||||
|
||||
# Cache result
|
||||
self.evaluation_cache[cache_key] = (result, time.time())
|
||||
|
||||
return result
|
||||
|
||||
def _compute_context_hash(self, context: Dict[str, Any]) -> str:
|
||||
"""Compute hash of context for caching."""
|
||||
context_str = str(sorted(context.items()))
|
||||
return hashlib.md5(context_str.encode()).hexdigest()[:8]
|
||||
|
||||
def _evaluate_mcp_patterns(self, context: Dict[str, Any], patterns: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate MCP orchestration patterns."""
|
||||
server_selection = patterns.get('server_selection', {})
|
||||
decision_tree = server_selection.get('decision_tree', [])
|
||||
|
||||
recommendations = {
|
||||
'primary_server': None,
|
||||
'support_servers': [],
|
||||
'coordination_mode': 'sequential',
|
||||
'confidence': 0.0
|
||||
}
|
||||
|
||||
# Evaluate decision tree
|
||||
for rule in decision_tree:
|
||||
if self._matches_conditions(context, rule.get('conditions', {})):
|
||||
recommendations['primary_server'] = rule.get('primary_server')
|
||||
recommendations['support_servers'] = rule.get('support_servers', [])
|
||||
recommendations['coordination_mode'] = rule.get('coordination_mode', 'sequential')
|
||||
recommendations['confidence'] = rule.get('confidence', 0.5)
|
||||
break
|
||||
|
||||
# Apply fallback if no match
|
||||
if not recommendations['primary_server']:
|
||||
fallback = server_selection.get('fallback_chain', {})
|
||||
recommendations['primary_server'] = fallback.get('default_primary', 'sequential')
|
||||
recommendations['confidence'] = 0.3
|
||||
|
||||
return recommendations
|
||||
|
||||
def _evaluate_hook_patterns(self, context: Dict[str, Any], patterns: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate hook coordination patterns."""
|
||||
execution_patterns = patterns.get('execution_patterns', {})
|
||||
|
||||
recommendations = {
|
||||
'execution_strategy': 'sequential',
|
||||
'parallel_groups': [],
|
||||
'conditional_hooks': [],
|
||||
'performance_optimizations': []
|
||||
}
|
||||
|
||||
# Check for parallel execution opportunities
|
||||
parallel_groups = execution_patterns.get('parallel_execution', {}).get('groups', [])
|
||||
for group in parallel_groups:
|
||||
if self._should_enable_parallel_group(context, group):
|
||||
recommendations['parallel_groups'].append(group)
|
||||
|
||||
# Check conditional execution rules
|
||||
conditional_rules = execution_patterns.get('conditional_execution', {}).get('rules', [])
|
||||
for rule in conditional_rules:
|
||||
if self._matches_conditions(context, rule.get('conditions', [])):
|
||||
recommendations['conditional_hooks'].append({
|
||||
'hook': rule.get('hook'),
|
||||
'priority': rule.get('priority', 'medium')
|
||||
})
|
||||
|
||||
return recommendations
|
||||
|
||||
def _evaluate_performance_patterns(self, context: Dict[str, Any], patterns: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate performance intelligence patterns."""
|
||||
auto_optimization = patterns.get('auto_optimization', {})
|
||||
optimization_triggers = auto_optimization.get('optimization_triggers', [])
|
||||
|
||||
recommendations = {
|
||||
'optimizations': [],
|
||||
'resource_zone': 'green',
|
||||
'performance_actions': []
|
||||
}
|
||||
|
||||
# Check optimization triggers
|
||||
for trigger in optimization_triggers:
|
||||
if self._matches_conditions(context, trigger.get('condition', {})):
|
||||
recommendations['optimizations'].extend(trigger.get('actions', []))
|
||||
recommendations['performance_actions'].append({
|
||||
'trigger': trigger.get('name'),
|
||||
'urgency': trigger.get('urgency', 'medium')
|
||||
})
|
||||
|
||||
# Determine resource zone
|
||||
resource_usage = context.get('resource_usage', 0.5)
|
||||
resource_zones = patterns.get('resource_management', {}).get('resource_zones', {})
|
||||
|
||||
for zone_name, zone_config in resource_zones.items():
|
||||
threshold = zone_config.get('threshold', 1.0)
|
||||
if resource_usage <= threshold:
|
||||
recommendations['resource_zone'] = zone_name
|
||||
break
|
||||
|
||||
return recommendations
|
||||
|
||||
def _evaluate_validation_patterns(self, context: Dict[str, Any], patterns: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate validation intelligence patterns."""
|
||||
proactive_diagnostics = patterns.get('proactive_diagnostics', {})
|
||||
early_warnings = proactive_diagnostics.get('early_warning_patterns', {})
|
||||
|
||||
recommendations = {
|
||||
'health_score': 1.0,
|
||||
'warnings': [],
|
||||
'diagnostics': [],
|
||||
'remediation_suggestions': []
|
||||
}
|
||||
|
||||
# Check early warning patterns
|
||||
for category, warnings in early_warnings.items():
|
||||
for warning in warnings:
|
||||
if self._matches_conditions(context, warning.get('pattern', {})):
|
||||
recommendations['warnings'].append({
|
||||
'name': warning.get('name'),
|
||||
'severity': warning.get('severity', 'medium'),
|
||||
'recommendation': warning.get('recommendation'),
|
||||
'category': category
|
||||
})
|
||||
|
||||
# Calculate health score (simplified)
|
||||
base_health = 1.0
|
||||
for warning in recommendations['warnings']:
|
||||
severity_impact = {'low': 0.05, 'medium': 0.1, 'high': 0.2, 'critical': 0.4}
|
||||
base_health -= severity_impact.get(warning['severity'], 0.1)
|
||||
|
||||
recommendations['health_score'] = max(0.0, base_health)
|
||||
|
||||
return recommendations
|
||||
|
||||
def _evaluate_ux_patterns(self, context: Dict[str, Any], patterns: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate user experience patterns."""
|
||||
project_detection = patterns.get('project_detection', {})
|
||||
detection_patterns = project_detection.get('detection_patterns', {})
|
||||
|
||||
recommendations = {
|
||||
'project_type': 'unknown',
|
||||
'suggested_servers': [],
|
||||
'smart_defaults': {},
|
||||
'user_suggestions': []
|
||||
}
|
||||
|
||||
# Detect project type
|
||||
file_indicators = context.get('file_indicators', [])
|
||||
directory_indicators = context.get('directory_indicators', [])
|
||||
|
||||
for category, projects in detection_patterns.items():
|
||||
for project_type, project_config in projects.items():
|
||||
if self._matches_project_indicators(file_indicators, directory_indicators, project_config):
|
||||
recommendations['project_type'] = project_type
|
||||
project_recs = project_config.get('recommendations', {})
|
||||
recommendations['suggested_servers'] = project_recs.get('mcp_servers', [])
|
||||
recommendations['smart_defaults'] = project_recs
|
||||
break
|
||||
|
||||
return recommendations
|
||||
|
||||
def _evaluate_learning_patterns(self, context: Dict[str, Any], patterns: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Evaluate learning intelligence patterns."""
|
||||
learning_intelligence = patterns.get('learning_intelligence', {})
|
||||
pattern_recognition = learning_intelligence.get('pattern_recognition', {})
|
||||
|
||||
recommendations = {
|
||||
'pattern_dimensions': [],
|
||||
'learning_strategy': 'standard',
|
||||
'confidence_threshold': 0.7
|
||||
}
|
||||
|
||||
# Get pattern dimensions
|
||||
dimensions = pattern_recognition.get('dimensions', {})
|
||||
recommendations['pattern_dimensions'] = dimensions.get('primary', []) + dimensions.get('secondary', [])
|
||||
|
||||
# Determine learning strategy based on context
|
||||
complexity = context.get('complexity_score', 0.5)
|
||||
if complexity > 0.8:
|
||||
recommendations['learning_strategy'] = 'comprehensive'
|
||||
elif complexity < 0.3:
|
||||
recommendations['learning_strategy'] = 'lightweight'
|
||||
|
||||
return recommendations
|
||||
|
||||
def _matches_conditions(self, context: Dict[str, Any], conditions: Union[Dict, List]) -> bool:
|
||||
"""Check if context matches pattern conditions."""
|
||||
if isinstance(conditions, list):
|
||||
# List of conditions (AND logic)
|
||||
return all(self._matches_single_condition(context, cond) for cond in conditions)
|
||||
elif isinstance(conditions, dict):
|
||||
if 'AND' in conditions:
|
||||
return all(self._matches_single_condition(context, cond) for cond in conditions['AND'])
|
||||
elif 'OR' in conditions:
|
||||
return any(self._matches_single_condition(context, cond) for cond in conditions['OR'])
|
||||
else:
|
||||
return self._matches_single_condition(context, conditions)
|
||||
return False
|
||||
|
||||
def _matches_single_condition(self, context: Dict[str, Any], condition: Dict[str, Any]) -> bool:
|
||||
"""Check if context matches a single condition."""
|
||||
for key, expected_value in condition.items():
|
||||
context_value = context.get(key)
|
||||
|
||||
if context_value is None:
|
||||
return False
|
||||
|
||||
# Handle string operations
|
||||
if isinstance(expected_value, str):
|
||||
if expected_value.startswith('>'):
|
||||
threshold = float(expected_value[1:])
|
||||
return float(context_value) > threshold
|
||||
elif expected_value.startswith('<'):
|
||||
threshold = float(expected_value[1:])
|
||||
return float(context_value) < threshold
|
||||
elif isinstance(expected_value, list):
|
||||
return context_value in expected_value
|
||||
else:
|
||||
return context_value == expected_value
|
||||
elif isinstance(expected_value, list):
|
||||
return context_value in expected_value
|
||||
else:
|
||||
return context_value == expected_value
|
||||
|
||||
return True
|
||||
|
||||
def _should_enable_parallel_group(self, context: Dict[str, Any], group: Dict[str, Any]) -> bool:
|
||||
"""Determine if a parallel group should be enabled."""
|
||||
# Simple heuristic: enable if not in resource-constrained environment
|
||||
resource_usage = context.get('resource_usage', 0.5)
|
||||
return resource_usage < 0.8 and context.get('complexity_score', 0.5) > 0.3
|
||||
|
||||
def _matches_project_indicators(self, files: List[str], dirs: List[str],
|
||||
project_config: Dict[str, Any]) -> bool:
|
||||
"""Check if file/directory indicators match project pattern."""
|
||||
file_indicators = project_config.get('file_indicators', [])
|
||||
dir_indicators = project_config.get('directory_indicators', [])
|
||||
|
||||
file_matches = sum(1 for indicator in file_indicators if any(indicator in f for f in files))
|
||||
dir_matches = sum(1 for indicator in dir_indicators if any(indicator in d for d in dirs))
|
||||
|
||||
confidence_threshold = project_config.get('confidence_threshold', 0.8)
|
||||
total_indicators = len(file_indicators) + len(dir_indicators)
|
||||
|
||||
if total_indicators == 0:
|
||||
return False
|
||||
|
||||
match_ratio = (file_matches + dir_matches) / total_indicators
|
||||
return match_ratio >= confidence_threshold
|
||||
|
||||
def get_intelligence_summary(self) -> Dict[str, Any]:
|
||||
"""Get summary of current intelligence state."""
|
||||
return {
|
||||
'loaded_patterns': list(self.patterns.keys()),
|
||||
'cache_entries': len(self.evaluation_cache),
|
||||
'last_reload': max(self.pattern_timestamps.values()) if self.pattern_timestamps else 0,
|
||||
'pattern_status': {name: 'loaded' for name in self.patterns.keys()}
|
||||
}
|
||||
@@ -1,891 +0,0 @@
|
||||
"""
|
||||
Learning Engine for SuperClaude-Lite
|
||||
|
||||
Cross-hook adaptation system that learns from user patterns, operation effectiveness,
|
||||
and system performance to continuously improve SuperClaude intelligence.
|
||||
"""
|
||||
|
||||
import json
|
||||
import time
|
||||
import statistics
|
||||
from typing import Dict, Any, List, Optional, Tuple, Set
|
||||
from dataclasses import dataclass, asdict
|
||||
from enum import Enum
|
||||
from pathlib import Path
|
||||
|
||||
from yaml_loader import config_loader
|
||||
from intelligence_engine import IntelligenceEngine
|
||||
|
||||
|
||||
class LearningType(Enum):
|
||||
"""Types of learning patterns."""
|
||||
USER_PREFERENCE = "user_preference"
|
||||
OPERATION_PATTERN = "operation_pattern"
|
||||
PERFORMANCE_OPTIMIZATION = "performance_optimization"
|
||||
ERROR_RECOVERY = "error_recovery"
|
||||
EFFECTIVENESS_FEEDBACK = "effectiveness_feedback"
|
||||
|
||||
|
||||
class AdaptationScope(Enum):
|
||||
"""Scope of learning adaptations."""
|
||||
SESSION = "session" # Apply only to current session
|
||||
PROJECT = "project" # Apply to current project
|
||||
USER = "user" # Apply across all user sessions
|
||||
GLOBAL = "global" # Apply to all users (anonymized)
|
||||
|
||||
|
||||
@dataclass
|
||||
class LearningRecord:
|
||||
"""Record of a learning event."""
|
||||
timestamp: float
|
||||
learning_type: LearningType
|
||||
scope: AdaptationScope
|
||||
context: Dict[str, Any]
|
||||
pattern: Dict[str, Any]
|
||||
effectiveness_score: float # 0.0 to 1.0
|
||||
confidence: float # 0.0 to 1.0
|
||||
metadata: Dict[str, Any]
|
||||
|
||||
|
||||
@dataclass
|
||||
class Adaptation:
|
||||
"""An adaptation learned from patterns."""
|
||||
adaptation_id: str
|
||||
pattern_signature: str
|
||||
trigger_conditions: Dict[str, Any]
|
||||
modifications: Dict[str, Any]
|
||||
effectiveness_history: List[float]
|
||||
usage_count: int
|
||||
last_used: float
|
||||
confidence_score: float
|
||||
|
||||
|
||||
@dataclass
|
||||
class LearningInsight:
|
||||
"""Insight derived from learning patterns."""
|
||||
insight_type: str
|
||||
description: str
|
||||
evidence: List[str]
|
||||
recommendations: List[str]
|
||||
confidence: float
|
||||
impact_score: float
|
||||
|
||||
|
||||
class LearningEngine:
|
||||
"""
|
||||
Cross-hook adaptation system for continuous improvement.
|
||||
|
||||
Features:
|
||||
- User preference learning and adaptation
|
||||
- Operation pattern recognition and optimization
|
||||
- Performance feedback integration
|
||||
- Cross-hook coordination and knowledge sharing
|
||||
- Effectiveness measurement and validation
|
||||
- Personalization and project-specific adaptations
|
||||
"""
|
||||
|
||||
def __init__(self, cache_dir: Path):
|
||||
self.cache_dir = Path(cache_dir)
|
||||
self.cache_dir.mkdir(exist_ok=True)
|
||||
|
||||
self.learning_records: List[LearningRecord] = []
|
||||
self.adaptations: Dict[str, Adaptation] = {}
|
||||
self.user_preferences: Dict[str, Any] = {}
|
||||
self.project_patterns: Dict[str, Dict[str, Any]] = {}
|
||||
|
||||
# Initialize intelligence engine for YAML pattern integration
|
||||
self.intelligence_engine = IntelligenceEngine()
|
||||
|
||||
self._load_learning_data()
|
||||
|
||||
def _load_learning_data(self):
|
||||
"""Load existing learning data from cache with robust error handling."""
|
||||
# Initialize empty data structures first
|
||||
self.learning_records = []
|
||||
self.adaptations = {}
|
||||
self.user_preferences = {}
|
||||
self.project_patterns = {}
|
||||
|
||||
try:
|
||||
# Load learning records with corruption detection
|
||||
records_file = self.cache_dir / "learning_records.json"
|
||||
if records_file.exists():
|
||||
try:
|
||||
with open(records_file, 'r') as f:
|
||||
content = f.read().strip()
|
||||
if not content:
|
||||
# Empty file, initialize with empty array
|
||||
self._initialize_empty_records_file(records_file)
|
||||
elif content == '[]':
|
||||
# Valid empty array
|
||||
self.learning_records = []
|
||||
else:
|
||||
# Try to parse JSON
|
||||
data = json.loads(content)
|
||||
if isinstance(data, list):
|
||||
self.learning_records = [
|
||||
LearningRecord(**record) for record in data
|
||||
if self._validate_learning_record(record)
|
||||
]
|
||||
else:
|
||||
# Invalid format, reinitialize
|
||||
self._initialize_empty_records_file(records_file)
|
||||
except (json.JSONDecodeError, TypeError, ValueError) as e:
|
||||
# JSON corruption detected, reinitialize
|
||||
print(f"Learning records corrupted, reinitializing: {e}")
|
||||
self._initialize_empty_records_file(records_file)
|
||||
else:
|
||||
# File doesn't exist, create it
|
||||
self._initialize_empty_records_file(records_file)
|
||||
|
||||
# Load adaptations with error handling
|
||||
adaptations_file = self.cache_dir / "adaptations.json"
|
||||
if adaptations_file.exists():
|
||||
try:
|
||||
with open(adaptations_file, 'r') as f:
|
||||
data = json.load(f)
|
||||
if isinstance(data, dict):
|
||||
self.adaptations = {
|
||||
k: Adaptation(**v) for k, v in data.items()
|
||||
if self._validate_adaptation_data(v)
|
||||
}
|
||||
except (json.JSONDecodeError, TypeError, ValueError):
|
||||
# Corrupted adaptations file, start fresh
|
||||
self.adaptations = {}
|
||||
|
||||
# Load user preferences with error handling
|
||||
preferences_file = self.cache_dir / "user_preferences.json"
|
||||
if preferences_file.exists():
|
||||
try:
|
||||
with open(preferences_file, 'r') as f:
|
||||
data = json.load(f)
|
||||
if isinstance(data, dict):
|
||||
self.user_preferences = data
|
||||
except (json.JSONDecodeError, TypeError, ValueError):
|
||||
self.user_preferences = {}
|
||||
|
||||
# Load project patterns with error handling
|
||||
patterns_file = self.cache_dir / "project_patterns.json"
|
||||
if patterns_file.exists():
|
||||
try:
|
||||
with open(patterns_file, 'r') as f:
|
||||
data = json.load(f)
|
||||
if isinstance(data, dict):
|
||||
self.project_patterns = data
|
||||
except (json.JSONDecodeError, TypeError, ValueError):
|
||||
self.project_patterns = {}
|
||||
|
||||
except Exception as e:
|
||||
# Final fallback - ensure all data structures are initialized
|
||||
print(f"Error loading learning data, using defaults: {e}")
|
||||
self.learning_records = []
|
||||
self.adaptations = {}
|
||||
self.user_preferences = {}
|
||||
self.project_patterns = {}
|
||||
|
||||
def record_learning_event(self,
|
||||
learning_type: LearningType,
|
||||
scope: AdaptationScope,
|
||||
context: Dict[str, Any],
|
||||
pattern: Dict[str, Any],
|
||||
effectiveness_score: float,
|
||||
confidence: float = 1.0,
|
||||
metadata: Dict[str, Any] = None) -> str:
|
||||
"""
|
||||
Record a learning event for future adaptation.
|
||||
|
||||
Args:
|
||||
learning_type: Type of learning event
|
||||
scope: Scope of the learning (session, project, user, global)
|
||||
context: Context in which the learning occurred
|
||||
pattern: Pattern or behavior that was observed
|
||||
effectiveness_score: How effective the pattern was (0.0 to 1.0)
|
||||
confidence: Confidence in the learning (0.0 to 1.0)
|
||||
metadata: Additional metadata about the learning event
|
||||
|
||||
Returns:
|
||||
Learning record ID
|
||||
"""
|
||||
if metadata is None:
|
||||
metadata = {}
|
||||
|
||||
# Validate effectiveness score bounds
|
||||
if not (0.0 <= effectiveness_score <= 1.0):
|
||||
raise ValueError(f"Effectiveness score must be between 0.0 and 1.0, got: {effectiveness_score}")
|
||||
|
||||
# Validate confidence bounds
|
||||
if not (0.0 <= confidence <= 1.0):
|
||||
raise ValueError(f"Confidence must be between 0.0 and 1.0, got: {confidence}")
|
||||
|
||||
# Flag suspicious perfect score sequences (potential overfitting)
|
||||
if effectiveness_score == 1.0:
|
||||
metadata['perfect_score_flag'] = True
|
||||
|
||||
record = LearningRecord(
|
||||
timestamp=time.time(),
|
||||
learning_type=learning_type,
|
||||
scope=scope,
|
||||
context=context,
|
||||
pattern=pattern,
|
||||
effectiveness_score=effectiveness_score,
|
||||
confidence=confidence,
|
||||
metadata=metadata
|
||||
)
|
||||
|
||||
self.learning_records.append(record)
|
||||
|
||||
# Trigger adaptation creation if pattern is significant
|
||||
if effectiveness_score > 0.7 and confidence > 0.6:
|
||||
self._create_adaptation_from_record(record)
|
||||
|
||||
# Save to cache
|
||||
self._save_learning_data()
|
||||
|
||||
return f"learning_{int(record.timestamp)}"
|
||||
|
||||
def _create_adaptation_from_record(self, record: LearningRecord):
|
||||
"""Create an adaptation from a significant learning record."""
|
||||
pattern_signature = self._generate_pattern_signature(record.pattern, record.context)
|
||||
|
||||
# Check if adaptation already exists
|
||||
if pattern_signature in self.adaptations:
|
||||
adaptation = self.adaptations[pattern_signature]
|
||||
adaptation.effectiveness_history.append(record.effectiveness_score)
|
||||
adaptation.usage_count += 1
|
||||
adaptation.last_used = record.timestamp
|
||||
|
||||
# Update confidence based on consistency
|
||||
if len(adaptation.effectiveness_history) > 1:
|
||||
consistency = 1.0 - statistics.stdev(adaptation.effectiveness_history[-5:]) / max(statistics.mean(adaptation.effectiveness_history[-5:]), 0.1)
|
||||
adaptation.confidence_score = min(consistency * record.confidence, 1.0)
|
||||
else:
|
||||
# Create new adaptation
|
||||
adaptation_id = f"adapt_{int(record.timestamp)}_{len(self.adaptations)}"
|
||||
|
||||
adaptation = Adaptation(
|
||||
adaptation_id=adaptation_id,
|
||||
pattern_signature=pattern_signature,
|
||||
trigger_conditions=self._extract_trigger_conditions(record.context),
|
||||
modifications=self._extract_modifications(record.pattern),
|
||||
effectiveness_history=[record.effectiveness_score],
|
||||
usage_count=1,
|
||||
last_used=record.timestamp,
|
||||
confidence_score=record.confidence
|
||||
)
|
||||
|
||||
self.adaptations[pattern_signature] = adaptation
|
||||
|
||||
def _generate_pattern_signature(self, pattern: Dict[str, Any], context: Dict[str, Any]) -> str:
|
||||
"""Generate a unique signature for a pattern using YAML intelligence patterns."""
|
||||
# Get pattern dimensions from YAML intelligence patterns
|
||||
intelligence_patterns = self.intelligence_engine.evaluate_context(context, 'intelligence_patterns')
|
||||
pattern_dimensions = intelligence_patterns.get('recommendations', {}).get('pattern_dimensions', [])
|
||||
|
||||
# If no YAML dimensions available, use fallback dimensions
|
||||
if not pattern_dimensions:
|
||||
pattern_dimensions = ['context_type', 'complexity_score', 'operation_type', 'performance_score']
|
||||
|
||||
key_elements = []
|
||||
|
||||
# Use YAML-defined dimensions for signature generation
|
||||
for dimension in pattern_dimensions:
|
||||
if dimension in context:
|
||||
value = context[dimension]
|
||||
# Bucket numeric values for better grouping
|
||||
if isinstance(value, (int, float)) and dimension in ['complexity_score', 'performance_score']:
|
||||
bucketed_value = int(value * 10) / 10 # Round to 0.1
|
||||
key_elements.append(f"{dimension}:{bucketed_value}")
|
||||
elif isinstance(value, (int, float)) and dimension in ['file_count', 'directory_count']:
|
||||
bucketed_value = min(int(value), 10) # Cap at 10 for grouping
|
||||
key_elements.append(f"{dimension}:{bucketed_value}")
|
||||
else:
|
||||
key_elements.append(f"{dimension}:{value}")
|
||||
elif dimension in pattern:
|
||||
key_elements.append(f"{dimension}:{pattern[dimension]}")
|
||||
|
||||
# Add pattern-specific elements
|
||||
for key in ['mcp_server', 'mode', 'compression_level', 'delegation_strategy']:
|
||||
if key in pattern and key not in [d.split(':')[0] for d in key_elements]:
|
||||
key_elements.append(f"{key}:{pattern[key]}")
|
||||
|
||||
signature = "_".join(sorted(key_elements))
|
||||
return signature if signature else "unknown_pattern"
|
||||
|
||||
def _extract_trigger_conditions(self, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Extract trigger conditions from context."""
|
||||
conditions = {}
|
||||
|
||||
# Operational conditions
|
||||
for key in ['operation_type', 'complexity_score', 'file_count', 'directory_count']:
|
||||
if key in context:
|
||||
conditions[key] = context[key]
|
||||
|
||||
# Environmental conditions
|
||||
for key in ['resource_usage_percent', 'conversation_length', 'user_expertise']:
|
||||
if key in context:
|
||||
conditions[key] = context[key]
|
||||
|
||||
# Project conditions
|
||||
for key in ['project_type', 'has_tests', 'is_production']:
|
||||
if key in context:
|
||||
conditions[key] = context[key]
|
||||
|
||||
return conditions
|
||||
|
||||
def _extract_modifications(self, pattern: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Extract modifications to apply from pattern."""
|
||||
modifications = {}
|
||||
|
||||
# MCP server preferences
|
||||
if 'mcp_server' in pattern:
|
||||
modifications['preferred_mcp_server'] = pattern['mcp_server']
|
||||
|
||||
# Mode preferences
|
||||
if 'mode' in pattern:
|
||||
modifications['preferred_mode'] = pattern['mode']
|
||||
|
||||
# Flag preferences
|
||||
if 'flags' in pattern:
|
||||
modifications['suggested_flags'] = pattern['flags']
|
||||
|
||||
# Performance optimizations
|
||||
if 'optimization' in pattern:
|
||||
modifications['optimization'] = pattern['optimization']
|
||||
|
||||
return modifications
|
||||
|
||||
def get_adaptations_for_context(self, context: Dict[str, Any]) -> List[Adaptation]:
|
||||
"""Get relevant adaptations for the current context."""
|
||||
relevant_adaptations = []
|
||||
|
||||
for adaptation in self.adaptations.values():
|
||||
if self._matches_trigger_conditions(adaptation.trigger_conditions, context):
|
||||
# Check effectiveness threshold
|
||||
if adaptation.confidence_score > 0.5 and len(adaptation.effectiveness_history) > 0:
|
||||
avg_effectiveness = statistics.mean(adaptation.effectiveness_history)
|
||||
if avg_effectiveness > 0.6:
|
||||
relevant_adaptations.append(adaptation)
|
||||
|
||||
# Sort by effectiveness and confidence
|
||||
relevant_adaptations.sort(
|
||||
key=lambda a: statistics.mean(a.effectiveness_history) * a.confidence_score,
|
||||
reverse=True
|
||||
)
|
||||
|
||||
return relevant_adaptations
|
||||
|
||||
def _matches_trigger_conditions(self, conditions: Dict[str, Any], context: Dict[str, Any]) -> bool:
|
||||
"""Check if context matches adaptation trigger conditions."""
|
||||
for key, expected_value in conditions.items():
|
||||
if key not in context:
|
||||
continue
|
||||
|
||||
context_value = context[key]
|
||||
|
||||
# Exact match for strings and booleans
|
||||
if isinstance(expected_value, (str, bool)):
|
||||
if context_value != expected_value:
|
||||
return False
|
||||
|
||||
# Range match for numbers
|
||||
elif isinstance(expected_value, (int, float)):
|
||||
tolerance = 0.1 if isinstance(expected_value, float) else 1
|
||||
if abs(context_value - expected_value) > tolerance:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def apply_adaptations(self,
|
||||
context: Dict[str, Any],
|
||||
base_recommendations: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Apply learned adaptations enhanced with YAML intelligence patterns.
|
||||
|
||||
Args:
|
||||
context: Current operation context
|
||||
base_recommendations: Base recommendations before adaptation
|
||||
|
||||
Returns:
|
||||
Enhanced recommendations with learned adaptations and YAML intelligence applied
|
||||
"""
|
||||
# Get YAML intelligence recommendations first
|
||||
mcp_intelligence = self.intelligence_engine.evaluate_context(context, 'mcp_orchestration')
|
||||
ux_intelligence = self.intelligence_engine.evaluate_context(context, 'user_experience')
|
||||
performance_intelligence = self.intelligence_engine.evaluate_context(context, 'performance_intelligence')
|
||||
|
||||
# Start with base recommendations and add YAML intelligence
|
||||
enhanced_recommendations = base_recommendations.copy()
|
||||
|
||||
# Integrate YAML-based MCP recommendations
|
||||
mcp_recs = mcp_intelligence.get('recommendations', {})
|
||||
if mcp_recs.get('primary_server'):
|
||||
if 'recommended_mcp_servers' not in enhanced_recommendations:
|
||||
enhanced_recommendations['recommended_mcp_servers'] = []
|
||||
servers = enhanced_recommendations['recommended_mcp_servers']
|
||||
if mcp_recs['primary_server'] not in servers:
|
||||
servers.insert(0, mcp_recs['primary_server'])
|
||||
|
||||
# Add support servers
|
||||
for support_server in mcp_recs.get('support_servers', []):
|
||||
if support_server not in servers:
|
||||
servers.append(support_server)
|
||||
|
||||
# Integrate UX intelligence (project detection, smart defaults)
|
||||
ux_recs = ux_intelligence.get('recommendations', {})
|
||||
if ux_recs.get('suggested_servers'):
|
||||
if 'recommended_mcp_servers' not in enhanced_recommendations:
|
||||
enhanced_recommendations['recommended_mcp_servers'] = []
|
||||
for server in ux_recs['suggested_servers']:
|
||||
if server not in enhanced_recommendations['recommended_mcp_servers']:
|
||||
enhanced_recommendations['recommended_mcp_servers'].append(server)
|
||||
|
||||
# Integrate performance optimizations
|
||||
perf_recs = performance_intelligence.get('recommendations', {})
|
||||
if perf_recs.get('optimizations'):
|
||||
enhanced_recommendations['performance_optimizations'] = perf_recs['optimizations']
|
||||
enhanced_recommendations['resource_zone'] = perf_recs.get('resource_zone', 'green')
|
||||
|
||||
# Apply learned adaptations on top of YAML intelligence
|
||||
relevant_adaptations = self.get_adaptations_for_context(context)
|
||||
|
||||
for adaptation in relevant_adaptations:
|
||||
# Apply modifications from adaptation
|
||||
for modification_type, modification_value in adaptation.modifications.items():
|
||||
if modification_type == 'preferred_mcp_server':
|
||||
# Enhance MCP server selection
|
||||
if 'recommended_mcp_servers' not in enhanced_recommendations:
|
||||
enhanced_recommendations['recommended_mcp_servers'] = []
|
||||
|
||||
servers = enhanced_recommendations['recommended_mcp_servers']
|
||||
if modification_value not in servers:
|
||||
servers.insert(0, modification_value) # Prioritize learned preference
|
||||
|
||||
elif modification_type == 'preferred_mode':
|
||||
# Enhance mode selection
|
||||
if 'recommended_modes' not in enhanced_recommendations:
|
||||
enhanced_recommendations['recommended_modes'] = []
|
||||
|
||||
modes = enhanced_recommendations['recommended_modes']
|
||||
if modification_value not in modes:
|
||||
modes.insert(0, modification_value)
|
||||
|
||||
elif modification_type == 'suggested_flags':
|
||||
# Enhance flag suggestions
|
||||
if 'suggested_flags' not in enhanced_recommendations:
|
||||
enhanced_recommendations['suggested_flags'] = []
|
||||
|
||||
for flag in modification_value:
|
||||
if flag not in enhanced_recommendations['suggested_flags']:
|
||||
enhanced_recommendations['suggested_flags'].append(flag)
|
||||
|
||||
elif modification_type == 'optimization':
|
||||
# Apply performance optimizations
|
||||
if 'optimizations' not in enhanced_recommendations:
|
||||
enhanced_recommendations['optimizations'] = []
|
||||
enhanced_recommendations['optimizations'].append(modification_value)
|
||||
|
||||
# Update usage tracking
|
||||
adaptation.usage_count += 1
|
||||
adaptation.last_used = time.time()
|
||||
|
||||
# Add learning metadata
|
||||
enhanced_recommendations['applied_adaptations'] = [
|
||||
{
|
||||
'id': adaptation.adaptation_id,
|
||||
'confidence': adaptation.confidence_score,
|
||||
'effectiveness': statistics.mean(adaptation.effectiveness_history)
|
||||
}
|
||||
for adaptation in relevant_adaptations
|
||||
]
|
||||
|
||||
return enhanced_recommendations
|
||||
|
||||
def record_effectiveness_feedback(self,
|
||||
adaptation_ids: List[str],
|
||||
effectiveness_score: float,
|
||||
context: Dict[str, Any]):
|
||||
"""Record feedback on adaptation effectiveness."""
|
||||
for adaptation_id in adaptation_ids:
|
||||
# Find adaptation by ID
|
||||
adaptation = None
|
||||
for adapt in self.adaptations.values():
|
||||
if adapt.adaptation_id == adaptation_id:
|
||||
adaptation = adapt
|
||||
break
|
||||
|
||||
if adaptation:
|
||||
adaptation.effectiveness_history.append(effectiveness_score)
|
||||
|
||||
# Update confidence based on consistency
|
||||
if len(adaptation.effectiveness_history) > 2:
|
||||
recent_scores = adaptation.effectiveness_history[-5:]
|
||||
consistency = 1.0 - statistics.stdev(recent_scores) / max(statistics.mean(recent_scores), 0.1)
|
||||
adaptation.confidence_score = min(consistency, 1.0)
|
||||
|
||||
# Record learning event
|
||||
self.record_learning_event(
|
||||
LearningType.EFFECTIVENESS_FEEDBACK,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{'adaptation_id': adaptation_id},
|
||||
effectiveness_score,
|
||||
adaptation.confidence_score
|
||||
)
|
||||
|
||||
def generate_learning_insights(self) -> List[LearningInsight]:
|
||||
"""Generate insights from learning patterns."""
|
||||
insights = []
|
||||
|
||||
# User preference insights
|
||||
insights.extend(self._analyze_user_preferences())
|
||||
|
||||
# Performance pattern insights
|
||||
insights.extend(self._analyze_performance_patterns())
|
||||
|
||||
# Error pattern insights
|
||||
insights.extend(self._analyze_error_patterns())
|
||||
|
||||
# Effectiveness insights
|
||||
insights.extend(self._analyze_effectiveness_patterns())
|
||||
|
||||
return insights
|
||||
|
||||
def _analyze_user_preferences(self) -> List[LearningInsight]:
|
||||
"""Analyze user preference patterns."""
|
||||
insights = []
|
||||
|
||||
# Analyze MCP server preferences
|
||||
mcp_usage = {}
|
||||
for record in self.learning_records:
|
||||
if record.learning_type == LearningType.USER_PREFERENCE:
|
||||
server = record.pattern.get('mcp_server')
|
||||
if server:
|
||||
if server not in mcp_usage:
|
||||
mcp_usage[server] = []
|
||||
mcp_usage[server].append(record.effectiveness_score)
|
||||
|
||||
if mcp_usage:
|
||||
# Find most effective server
|
||||
server_effectiveness = {
|
||||
server: statistics.mean(scores)
|
||||
for server, scores in mcp_usage.items()
|
||||
if len(scores) >= 3
|
||||
}
|
||||
|
||||
if server_effectiveness:
|
||||
best_server = max(server_effectiveness, key=server_effectiveness.get)
|
||||
best_score = server_effectiveness[best_server]
|
||||
|
||||
if best_score > 0.8:
|
||||
insights.append(LearningInsight(
|
||||
insight_type="user_preference",
|
||||
description=f"User consistently prefers {best_server} MCP server",
|
||||
evidence=[f"Effectiveness score: {best_score:.2f}", f"Usage count: {len(mcp_usage[best_server])}"],
|
||||
recommendations=[f"Auto-suggest {best_server} for similar operations"],
|
||||
confidence=min(best_score, 1.0),
|
||||
impact_score=0.7
|
||||
))
|
||||
|
||||
return insights
|
||||
|
||||
def _analyze_performance_patterns(self) -> List[LearningInsight]:
|
||||
"""Analyze performance optimization patterns."""
|
||||
insights = []
|
||||
|
||||
# Analyze delegation effectiveness
|
||||
delegation_records = [
|
||||
r for r in self.learning_records
|
||||
if r.learning_type == LearningType.PERFORMANCE_OPTIMIZATION
|
||||
and 'delegation' in r.pattern
|
||||
]
|
||||
|
||||
if len(delegation_records) >= 5:
|
||||
avg_effectiveness = statistics.mean([r.effectiveness_score for r in delegation_records])
|
||||
|
||||
if avg_effectiveness > 0.75:
|
||||
insights.append(LearningInsight(
|
||||
insight_type="performance_optimization",
|
||||
description="Delegation consistently improves performance",
|
||||
evidence=[f"Average effectiveness: {avg_effectiveness:.2f}", f"Sample size: {len(delegation_records)}"],
|
||||
recommendations=["Enable delegation for multi-file operations", "Lower delegation threshold"],
|
||||
confidence=avg_effectiveness,
|
||||
impact_score=0.8
|
||||
))
|
||||
|
||||
return insights
|
||||
|
||||
def _analyze_error_patterns(self) -> List[LearningInsight]:
|
||||
"""Analyze error recovery patterns."""
|
||||
insights = []
|
||||
|
||||
error_records = [
|
||||
r for r in self.learning_records
|
||||
if r.learning_type == LearningType.ERROR_RECOVERY
|
||||
]
|
||||
|
||||
if len(error_records) >= 3:
|
||||
# Analyze common error contexts
|
||||
error_contexts = {}
|
||||
for record in error_records:
|
||||
context_key = record.context.get('operation_type', 'unknown')
|
||||
if context_key not in error_contexts:
|
||||
error_contexts[context_key] = []
|
||||
error_contexts[context_key].append(record)
|
||||
|
||||
for context, records in error_contexts.items():
|
||||
if len(records) >= 2:
|
||||
avg_recovery_effectiveness = statistics.mean([r.effectiveness_score for r in records])
|
||||
|
||||
insights.append(LearningInsight(
|
||||
insight_type="error_recovery",
|
||||
description=f"Error patterns identified for {context} operations",
|
||||
evidence=[f"Occurrence count: {len(records)}", f"Recovery effectiveness: {avg_recovery_effectiveness:.2f}"],
|
||||
recommendations=[f"Add proactive validation for {context} operations"],
|
||||
confidence=min(len(records) / 5, 1.0),
|
||||
impact_score=0.6
|
||||
))
|
||||
|
||||
return insights
|
||||
|
||||
def _analyze_effectiveness_patterns(self) -> List[LearningInsight]:
|
||||
"""Analyze overall effectiveness patterns."""
|
||||
insights = []
|
||||
|
||||
if len(self.learning_records) >= 10:
|
||||
recent_records = sorted(self.learning_records, key=lambda r: r.timestamp)[-10:]
|
||||
avg_effectiveness = statistics.mean([r.effectiveness_score for r in recent_records])
|
||||
|
||||
if avg_effectiveness > 0.8:
|
||||
insights.append(LearningInsight(
|
||||
insight_type="effectiveness_trend",
|
||||
description="SuperClaude effectiveness is high and improving",
|
||||
evidence=[f"Recent average effectiveness: {avg_effectiveness:.2f}"],
|
||||
recommendations=["Continue current learning patterns", "Consider expanding adaptation scope"],
|
||||
confidence=avg_effectiveness,
|
||||
impact_score=0.9
|
||||
))
|
||||
elif avg_effectiveness < 0.6:
|
||||
insights.append(LearningInsight(
|
||||
insight_type="effectiveness_concern",
|
||||
description="SuperClaude effectiveness below optimal",
|
||||
evidence=[f"Recent average effectiveness: {avg_effectiveness:.2f}"],
|
||||
recommendations=["Review recent adaptations", "Gather more user feedback", "Adjust learning thresholds"],
|
||||
confidence=1.0 - avg_effectiveness,
|
||||
impact_score=0.8
|
||||
))
|
||||
|
||||
return insights
|
||||
|
||||
def _save_learning_data(self):
|
||||
"""Save learning data to cache files with validation and atomic writes."""
|
||||
try:
|
||||
# Save learning records with validation
|
||||
records_file = self.cache_dir / "learning_records.json"
|
||||
records_data = []
|
||||
for record in self.learning_records:
|
||||
try:
|
||||
# Convert record to dict and handle enums
|
||||
record_dict = asdict(record)
|
||||
|
||||
# Convert enum values to strings for JSON serialization
|
||||
if isinstance(record_dict.get('learning_type'), LearningType):
|
||||
record_dict['learning_type'] = record_dict['learning_type'].value
|
||||
if isinstance(record_dict.get('scope'), AdaptationScope):
|
||||
record_dict['scope'] = record_dict['scope'].value
|
||||
|
||||
# Validate the record
|
||||
if self._validate_learning_record_dict(record_dict):
|
||||
records_data.append(record_dict)
|
||||
else:
|
||||
print(f"Warning: Invalid record skipped: {record_dict}")
|
||||
except Exception as e:
|
||||
print(f"Warning: Error processing record: {e}")
|
||||
continue # Skip invalid records
|
||||
|
||||
# Atomic write to prevent corruption during write
|
||||
temp_file = records_file.with_suffix('.tmp')
|
||||
with open(temp_file, 'w') as f:
|
||||
json.dump(records_data, f, indent=2)
|
||||
temp_file.replace(records_file)
|
||||
|
||||
# Save adaptations with validation
|
||||
adaptations_file = self.cache_dir / "adaptations.json"
|
||||
adaptations_data = {}
|
||||
for k, v in self.adaptations.items():
|
||||
try:
|
||||
adapt_dict = asdict(v)
|
||||
if self._validate_adaptation_data(adapt_dict):
|
||||
adaptations_data[k] = adapt_dict
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
temp_file = adaptations_file.with_suffix('.tmp')
|
||||
with open(temp_file, 'w') as f:
|
||||
json.dump(adaptations_data, f, indent=2)
|
||||
temp_file.replace(adaptations_file)
|
||||
|
||||
# Save user preferences
|
||||
preferences_file = self.cache_dir / "user_preferences.json"
|
||||
if isinstance(self.user_preferences, dict):
|
||||
temp_file = preferences_file.with_suffix('.tmp')
|
||||
with open(temp_file, 'w') as f:
|
||||
json.dump(self.user_preferences, f, indent=2)
|
||||
temp_file.replace(preferences_file)
|
||||
|
||||
# Save project patterns
|
||||
patterns_file = self.cache_dir / "project_patterns.json"
|
||||
if isinstance(self.project_patterns, dict):
|
||||
temp_file = patterns_file.with_suffix('.tmp')
|
||||
with open(temp_file, 'w') as f:
|
||||
json.dump(self.project_patterns, f, indent=2)
|
||||
temp_file.replace(patterns_file)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error saving learning data: {e}")
|
||||
|
||||
def _initialize_empty_records_file(self, records_file: Path):
|
||||
"""Initialize learning records file with empty array."""
|
||||
try:
|
||||
with open(records_file, 'w') as f:
|
||||
json.dump([], f)
|
||||
except Exception as e:
|
||||
print(f"Error initializing records file: {e}")
|
||||
|
||||
def _validate_learning_record(self, record_data: dict) -> bool:
|
||||
"""Validate learning record data structure."""
|
||||
required_fields = ['timestamp', 'learning_type', 'scope', 'context', 'pattern', 'effectiveness_score', 'confidence', 'metadata']
|
||||
try:
|
||||
return all(field in record_data for field in required_fields)
|
||||
except (TypeError, AttributeError):
|
||||
return False
|
||||
|
||||
def _validate_learning_record_dict(self, record_dict: dict) -> bool:
|
||||
"""Validate learning record dictionary before saving."""
|
||||
try:
|
||||
# Check required fields exist and have valid types
|
||||
if not isinstance(record_dict.get('timestamp'), (int, float)):
|
||||
return False
|
||||
|
||||
# Handle both enum objects and string values for learning_type
|
||||
learning_type = record_dict.get('learning_type')
|
||||
if not (isinstance(learning_type, str) or isinstance(learning_type, LearningType)):
|
||||
return False
|
||||
|
||||
# Handle both enum objects and string values for scope
|
||||
scope = record_dict.get('scope')
|
||||
if not (isinstance(scope, str) or isinstance(scope, AdaptationScope)):
|
||||
return False
|
||||
|
||||
if not isinstance(record_dict.get('context'), dict):
|
||||
return False
|
||||
if not isinstance(record_dict.get('pattern'), dict):
|
||||
return False
|
||||
if not isinstance(record_dict.get('effectiveness_score'), (int, float)):
|
||||
return False
|
||||
if not isinstance(record_dict.get('confidence'), (int, float)):
|
||||
return False
|
||||
if not isinstance(record_dict.get('metadata'), dict):
|
||||
return False
|
||||
return True
|
||||
except (TypeError, AttributeError):
|
||||
return False
|
||||
|
||||
def _validate_adaptation_data(self, adapt_data: dict) -> bool:
|
||||
"""Validate adaptation data structure."""
|
||||
required_fields = ['adaptation_id', 'pattern_signature', 'trigger_conditions', 'modifications', 'effectiveness_history', 'usage_count', 'last_used', 'confidence_score']
|
||||
try:
|
||||
return all(field in adapt_data for field in required_fields)
|
||||
except (TypeError, AttributeError):
|
||||
return False
|
||||
|
||||
def get_intelligent_recommendations(self, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Get comprehensive intelligent recommendations combining YAML patterns and learned adaptations.
|
||||
|
||||
Args:
|
||||
context: Current operation context
|
||||
|
||||
Returns:
|
||||
Comprehensive recommendations with intelligence from multiple sources
|
||||
"""
|
||||
# Get base recommendations from all YAML intelligence patterns
|
||||
base_recommendations = {}
|
||||
|
||||
# Collect recommendations from all intelligence pattern types
|
||||
pattern_types = ['mcp_orchestration', 'hook_coordination', 'performance_intelligence',
|
||||
'validation_intelligence', 'user_experience', 'intelligence_patterns']
|
||||
|
||||
intelligence_results = {}
|
||||
for pattern_type in pattern_types:
|
||||
try:
|
||||
result = self.intelligence_engine.evaluate_context(context, pattern_type)
|
||||
intelligence_results[pattern_type] = result
|
||||
|
||||
# Merge recommendations
|
||||
recommendations = result.get('recommendations', {})
|
||||
for key, value in recommendations.items():
|
||||
if key not in base_recommendations:
|
||||
base_recommendations[key] = value
|
||||
elif isinstance(base_recommendations[key], list) and isinstance(value, list):
|
||||
# Merge lists without duplicates
|
||||
base_recommendations[key] = list(set(base_recommendations[key] + value))
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not evaluate {pattern_type} patterns: {e}")
|
||||
|
||||
# Apply learned adaptations on top of YAML intelligence
|
||||
enhanced_recommendations = self.apply_adaptations(context, base_recommendations)
|
||||
|
||||
# Add intelligence metadata
|
||||
enhanced_recommendations['intelligence_metadata'] = {
|
||||
'yaml_patterns_used': list(intelligence_results.keys()),
|
||||
'adaptations_applied': len(self.get_adaptations_for_context(context)),
|
||||
'confidence_scores': {k: v.get('confidence', 0.0) for k, v in intelligence_results.items()},
|
||||
'recommendations_source': 'yaml_intelligence_plus_learned_adaptations'
|
||||
}
|
||||
|
||||
return enhanced_recommendations
|
||||
|
||||
def cleanup_old_data(self, days_to_keep: int = 30):
|
||||
"""Clean up old learning data to prevent cache bloat."""
|
||||
cutoff_time = time.time() - (days_to_keep * 24 * 60 * 60)
|
||||
|
||||
# Remove old learning records
|
||||
self.learning_records = [
|
||||
record for record in self.learning_records
|
||||
if record.timestamp > cutoff_time
|
||||
]
|
||||
|
||||
# Remove unused adaptations
|
||||
self.adaptations = {
|
||||
k: v for k, v in self.adaptations.items()
|
||||
if v.last_used > cutoff_time or v.usage_count > 5
|
||||
}
|
||||
|
||||
self._save_learning_data()
|
||||
|
||||
def update_last_preference(self, preference_key: str, value: Any):
|
||||
"""Simply store the last successful choice - no complex learning."""
|
||||
if not self.user_preferences:
|
||||
self.user_preferences = {}
|
||||
self.user_preferences[preference_key] = {
|
||||
"value": value,
|
||||
"timestamp": time.time()
|
||||
}
|
||||
self._save_learning_data()
|
||||
|
||||
def get_last_preference(self, preference_key: str, default=None):
|
||||
"""Get the last successful choice if available."""
|
||||
if not self.user_preferences:
|
||||
return default
|
||||
pref = self.user_preferences.get(preference_key, {})
|
||||
return pref.get("value", default)
|
||||
|
||||
def update_project_info(self, project_path: str, info_type: str, value: Any):
|
||||
"""Store basic project information."""
|
||||
if not self.project_patterns:
|
||||
self.project_patterns = {}
|
||||
if project_path not in self.project_patterns:
|
||||
self.project_patterns[project_path] = {}
|
||||
self.project_patterns[project_path][info_type] = value
|
||||
self.project_patterns[project_path]["last_updated"] = time.time()
|
||||
self._save_learning_data()
|
||||
@@ -1,319 +0,0 @@
|
||||
"""
|
||||
Simple logger for SuperClaude-Lite hooks.
|
||||
|
||||
Provides structured logging of hook events for later analysis.
|
||||
Focuses on capturing hook lifecycle, decisions, and errors in a
|
||||
structured format without any analysis or complex features.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from datetime import datetime, timezone, timedelta
|
||||
from pathlib import Path
|
||||
from typing import Optional, Dict, Any
|
||||
import uuid
|
||||
import glob
|
||||
|
||||
# Import configuration loader
|
||||
try:
|
||||
from .yaml_loader import UnifiedConfigLoader
|
||||
except ImportError:
|
||||
# Fallback if yaml_loader is not available
|
||||
UnifiedConfigLoader = None
|
||||
|
||||
|
||||
class HookLogger:
|
||||
"""Simple logger for SuperClaude-Lite hooks."""
|
||||
|
||||
def __init__(self, log_dir: str = None, retention_days: int = None):
|
||||
"""
|
||||
Initialize the logger.
|
||||
|
||||
Args:
|
||||
log_dir: Directory to store log files. Defaults to cache/logs/
|
||||
retention_days: Number of days to keep log files. Defaults to 30.
|
||||
"""
|
||||
# Load configuration
|
||||
self.config = self._load_config()
|
||||
|
||||
# Check if logging is enabled
|
||||
if not self.config.get('logging', {}).get('enabled', True):
|
||||
self.enabled = False
|
||||
return
|
||||
|
||||
self.enabled = True
|
||||
|
||||
# Set up log directory
|
||||
if log_dir is None:
|
||||
# Get SuperClaude-Lite root directory (2 levels up from shared/)
|
||||
root_dir = Path(__file__).parent.parent.parent
|
||||
log_dir_config = self.config.get('logging', {}).get('file_settings', {}).get('log_directory', 'cache/logs')
|
||||
log_dir = root_dir / log_dir_config
|
||||
|
||||
self.log_dir = Path(log_dir)
|
||||
self.log_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Log retention settings
|
||||
if retention_days is None:
|
||||
retention_days = self.config.get('logging', {}).get('file_settings', {}).get('retention_days', 30)
|
||||
self.retention_days = retention_days
|
||||
|
||||
# Session ID for correlating events - shared across all hooks in the same Claude Code session
|
||||
self.session_id = self._get_or_create_session_id()
|
||||
|
||||
# Set up Python logger
|
||||
self._setup_logger()
|
||||
|
||||
# Clean up old logs on initialization
|
||||
self._cleanup_old_logs()
|
||||
|
||||
def _load_config(self) -> Dict[str, Any]:
|
||||
"""Load logging configuration from YAML file."""
|
||||
if UnifiedConfigLoader is None:
|
||||
# Return default configuration if loader not available
|
||||
return {
|
||||
'logging': {
|
||||
'enabled': True,
|
||||
'level': 'INFO',
|
||||
'file_settings': {
|
||||
'log_directory': 'cache/logs',
|
||||
'retention_days': 30
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
try:
|
||||
# Get project root
|
||||
root_dir = Path(__file__).parent.parent.parent
|
||||
loader = UnifiedConfigLoader(root_dir)
|
||||
|
||||
# Load logging configuration
|
||||
config = loader.load_yaml('logging')
|
||||
return config or {}
|
||||
except Exception:
|
||||
# Return default configuration on error
|
||||
return {
|
||||
'logging': {
|
||||
'enabled': True,
|
||||
'level': 'INFO',
|
||||
'file_settings': {
|
||||
'log_directory': 'cache/logs',
|
||||
'retention_days': 30
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
def _get_or_create_session_id(self) -> str:
|
||||
"""
|
||||
Get or create a shared session ID for correlation across all hooks.
|
||||
|
||||
Checks in order:
|
||||
1. Environment variable CLAUDE_SESSION_ID
|
||||
2. Session file in cache directory
|
||||
3. Generate new UUID and save to session file
|
||||
|
||||
Returns:
|
||||
8-character session ID string
|
||||
"""
|
||||
# Check environment variable first
|
||||
env_session_id = os.environ.get('CLAUDE_SESSION_ID')
|
||||
if env_session_id:
|
||||
return env_session_id[:8] # Truncate to 8 characters for consistency
|
||||
|
||||
# Check for session file in cache directory
|
||||
cache_dir = self.log_dir.parent # logs are in cache/logs, so parent is cache/
|
||||
session_file = cache_dir / "session_id"
|
||||
|
||||
try:
|
||||
if session_file.exists():
|
||||
session_id = session_file.read_text(encoding='utf-8').strip()
|
||||
# Validate it's a reasonable session ID (8 chars, alphanumeric)
|
||||
if len(session_id) == 8 and session_id.replace('-', '').isalnum():
|
||||
return session_id
|
||||
except (IOError, OSError):
|
||||
# If we can't read the file, generate a new one
|
||||
pass
|
||||
|
||||
# Generate new session ID and save it
|
||||
new_session_id = str(uuid.uuid4())[:8]
|
||||
try:
|
||||
# Ensure cache directory exists
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
session_file.write_text(new_session_id, encoding='utf-8')
|
||||
except (IOError, OSError):
|
||||
# If we can't write the file, just return the ID
|
||||
# The session won't be shared, but at least this instance will work
|
||||
pass
|
||||
|
||||
return new_session_id
|
||||
|
||||
def _setup_logger(self):
|
||||
"""Set up the Python logger with JSON formatting."""
|
||||
self.logger = logging.getLogger("superclaude_lite_hooks")
|
||||
|
||||
# Set log level from configuration
|
||||
log_level_str = self.config.get('logging', {}).get('level', 'INFO').upper()
|
||||
log_level = getattr(logging, log_level_str, logging.INFO)
|
||||
self.logger.setLevel(log_level)
|
||||
|
||||
# Remove existing handlers to avoid duplicates
|
||||
self.logger.handlers.clear()
|
||||
|
||||
# Create daily log file
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
log_file = self.log_dir / f"superclaude-lite-{today}.log"
|
||||
|
||||
# File handler
|
||||
handler = logging.FileHandler(log_file, mode='a', encoding='utf-8')
|
||||
handler.setLevel(logging.INFO)
|
||||
|
||||
# Simple formatter - just output the message (which is already JSON)
|
||||
formatter = logging.Formatter('%(message)s')
|
||||
handler.setFormatter(formatter)
|
||||
|
||||
self.logger.addHandler(handler)
|
||||
|
||||
def _create_event(self, event_type: str, hook_name: str, data: Dict[str, Any] = None) -> Dict[str, Any]:
|
||||
"""Create a structured event."""
|
||||
event = {
|
||||
"timestamp": datetime.now(timezone.utc).isoformat(),
|
||||
"session": self.session_id,
|
||||
"hook": hook_name,
|
||||
"event": event_type
|
||||
}
|
||||
|
||||
if data:
|
||||
event["data"] = data
|
||||
|
||||
return event
|
||||
|
||||
def _should_log_event(self, hook_name: str, event_type: str) -> bool:
|
||||
"""Check if this event should be logged based on configuration."""
|
||||
if not self.enabled:
|
||||
return False
|
||||
|
||||
# Check hook-specific configuration
|
||||
hook_config = self.config.get('hook_configuration', {}).get(hook_name, {})
|
||||
if not hook_config.get('enabled', True):
|
||||
return False
|
||||
|
||||
# Check event type configuration
|
||||
hook_logging = self.config.get('logging', {}).get('hook_logging', {})
|
||||
event_mapping = {
|
||||
'start': 'log_lifecycle',
|
||||
'end': 'log_lifecycle',
|
||||
'decision': 'log_decisions',
|
||||
'error': 'log_errors'
|
||||
}
|
||||
|
||||
config_key = event_mapping.get(event_type, 'log_lifecycle')
|
||||
return hook_logging.get(config_key, True)
|
||||
|
||||
def log_hook_start(self, hook_name: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log the start of a hook execution."""
|
||||
if not self._should_log_event(hook_name, 'start'):
|
||||
return
|
||||
|
||||
event = self._create_event("start", hook_name, context)
|
||||
self.logger.info(json.dumps(event))
|
||||
|
||||
def log_hook_end(self, hook_name: str, duration_ms: int, success: bool, result: Optional[Dict[str, Any]] = None):
|
||||
"""Log the end of a hook execution."""
|
||||
if not self._should_log_event(hook_name, 'end'):
|
||||
return
|
||||
|
||||
data = {
|
||||
"duration_ms": duration_ms,
|
||||
"success": success
|
||||
}
|
||||
if result:
|
||||
data["result"] = result
|
||||
|
||||
event = self._create_event("end", hook_name, data)
|
||||
self.logger.info(json.dumps(event))
|
||||
|
||||
def log_decision(self, hook_name: str, decision_type: str, choice: str, reason: str):
|
||||
"""Log a decision made by a hook."""
|
||||
if not self._should_log_event(hook_name, 'decision'):
|
||||
return
|
||||
|
||||
data = {
|
||||
"type": decision_type,
|
||||
"choice": choice,
|
||||
"reason": reason
|
||||
}
|
||||
event = self._create_event("decision", hook_name, data)
|
||||
self.logger.info(json.dumps(event))
|
||||
|
||||
def log_error(self, hook_name: str, error: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log an error that occurred in a hook."""
|
||||
if not self._should_log_event(hook_name, 'error'):
|
||||
return
|
||||
|
||||
data = {
|
||||
"error": error
|
||||
}
|
||||
if context:
|
||||
data["context"] = context
|
||||
|
||||
event = self._create_event("error", hook_name, data)
|
||||
self.logger.info(json.dumps(event))
|
||||
|
||||
def _cleanup_old_logs(self):
|
||||
"""Remove log files older than retention_days."""
|
||||
if self.retention_days <= 0:
|
||||
return
|
||||
|
||||
cutoff_date = datetime.now() - timedelta(days=self.retention_days)
|
||||
|
||||
# Find all log files
|
||||
log_pattern = self.log_dir / "superclaude-lite-*.log"
|
||||
for log_file in glob.glob(str(log_pattern)):
|
||||
try:
|
||||
# Extract date from filename
|
||||
filename = os.path.basename(log_file)
|
||||
date_str = filename.replace("superclaude-lite-", "").replace(".log", "")
|
||||
file_date = datetime.strptime(date_str, "%Y-%m-%d")
|
||||
|
||||
# Remove if older than cutoff
|
||||
if file_date < cutoff_date:
|
||||
os.remove(log_file)
|
||||
|
||||
except (ValueError, OSError):
|
||||
# Skip files that don't match expected format or can't be removed
|
||||
continue
|
||||
|
||||
|
||||
# Global logger instance
|
||||
_logger = None
|
||||
|
||||
|
||||
def get_logger() -> HookLogger:
|
||||
"""Get the global logger instance."""
|
||||
global _logger
|
||||
if _logger is None:
|
||||
_logger = HookLogger()
|
||||
return _logger
|
||||
|
||||
|
||||
# Convenience functions for easy hook integration
|
||||
def log_hook_start(hook_name: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log the start of a hook execution."""
|
||||
get_logger().log_hook_start(hook_name, context)
|
||||
|
||||
|
||||
def log_hook_end(hook_name: str, duration_ms: int, success: bool, result: Optional[Dict[str, Any]] = None):
|
||||
"""Log the end of a hook execution."""
|
||||
get_logger().log_hook_end(hook_name, duration_ms, success, result)
|
||||
|
||||
|
||||
def log_decision(hook_name: str, decision_type: str, choice: str, reason: str):
|
||||
"""Log a decision made by a hook."""
|
||||
get_logger().log_decision(hook_name, decision_type, choice, reason)
|
||||
|
||||
|
||||
def log_error(hook_name: str, error: str, context: Optional[Dict[str, Any]] = None):
|
||||
"""Log an error that occurred in a hook."""
|
||||
get_logger().log_error(hook_name, error, context)
|
||||
@@ -1,762 +0,0 @@
|
||||
"""
|
||||
MCP Intelligence Engine for SuperClaude-Lite
|
||||
|
||||
Intelligent MCP server activation, coordination, and optimization based on
|
||||
ORCHESTRATOR.md patterns and real-time context analysis.
|
||||
"""
|
||||
|
||||
import json
|
||||
import time
|
||||
from typing import Dict, Any, List, Optional, Set, Tuple
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
from yaml_loader import config_loader
|
||||
from pattern_detection import PatternDetector, PatternMatch
|
||||
|
||||
|
||||
class MCPServerState(Enum):
|
||||
"""States of MCP server availability."""
|
||||
AVAILABLE = "available"
|
||||
UNAVAILABLE = "unavailable"
|
||||
LOADING = "loading"
|
||||
ERROR = "error"
|
||||
|
||||
|
||||
@dataclass
|
||||
class MCPServerCapability:
|
||||
"""Capability definition for an MCP server."""
|
||||
server_name: str
|
||||
primary_functions: List[str]
|
||||
performance_profile: str # lightweight, standard, intensive
|
||||
activation_cost_ms: int
|
||||
token_efficiency: float # 0.0 to 1.0
|
||||
quality_impact: float # 0.0 to 1.0
|
||||
|
||||
|
||||
@dataclass
|
||||
class MCPActivationPlan:
|
||||
"""Plan for MCP server activation."""
|
||||
servers_to_activate: List[str]
|
||||
activation_order: List[str]
|
||||
estimated_cost_ms: int
|
||||
efficiency_gains: Dict[str, float]
|
||||
fallback_strategy: Dict[str, str]
|
||||
coordination_strategy: str
|
||||
|
||||
|
||||
class MCPIntelligence:
|
||||
"""
|
||||
Intelligent MCP server management and coordination.
|
||||
|
||||
Implements ORCHESTRATOR.md patterns for:
|
||||
- Smart server selection based on context
|
||||
- Performance-optimized activation sequences
|
||||
- Fallback strategies for server failures
|
||||
- Cross-server coordination and caching
|
||||
- Real-time adaptation based on effectiveness
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.pattern_detector = PatternDetector()
|
||||
self.server_capabilities = self._load_server_capabilities()
|
||||
self.server_states = self._initialize_server_states()
|
||||
self.activation_history = []
|
||||
self.performance_metrics = {}
|
||||
|
||||
def _load_server_capabilities(self) -> Dict[str, MCPServerCapability]:
|
||||
"""Load MCP server capabilities from configuration."""
|
||||
config = config_loader.load_config('orchestrator')
|
||||
capabilities = {}
|
||||
|
||||
servers_config = config.get('mcp_servers', {})
|
||||
|
||||
capabilities['context7'] = MCPServerCapability(
|
||||
server_name='context7',
|
||||
primary_functions=['library_docs', 'framework_patterns', 'best_practices'],
|
||||
performance_profile='standard',
|
||||
activation_cost_ms=150,
|
||||
token_efficiency=0.8,
|
||||
quality_impact=0.9
|
||||
)
|
||||
|
||||
capabilities['sequential'] = MCPServerCapability(
|
||||
server_name='sequential',
|
||||
primary_functions=['complex_analysis', 'multi_step_reasoning', 'debugging'],
|
||||
performance_profile='intensive',
|
||||
activation_cost_ms=200,
|
||||
token_efficiency=0.6,
|
||||
quality_impact=0.95
|
||||
)
|
||||
|
||||
capabilities['magic'] = MCPServerCapability(
|
||||
server_name='magic',
|
||||
primary_functions=['ui_components', 'design_systems', 'frontend_generation'],
|
||||
performance_profile='standard',
|
||||
activation_cost_ms=120,
|
||||
token_efficiency=0.85,
|
||||
quality_impact=0.9
|
||||
)
|
||||
|
||||
capabilities['playwright'] = MCPServerCapability(
|
||||
server_name='playwright',
|
||||
primary_functions=['e2e_testing', 'browser_automation', 'performance_testing'],
|
||||
performance_profile='intensive',
|
||||
activation_cost_ms=300,
|
||||
token_efficiency=0.7,
|
||||
quality_impact=0.85
|
||||
)
|
||||
|
||||
capabilities['morphllm'] = MCPServerCapability(
|
||||
server_name='morphllm',
|
||||
primary_functions=['intelligent_editing', 'pattern_application', 'fast_apply'],
|
||||
performance_profile='lightweight',
|
||||
activation_cost_ms=80,
|
||||
token_efficiency=0.9,
|
||||
quality_impact=0.8
|
||||
)
|
||||
|
||||
capabilities['serena'] = MCPServerCapability(
|
||||
server_name='serena',
|
||||
primary_functions=['semantic_analysis', 'project_context', 'memory_management'],
|
||||
performance_profile='standard',
|
||||
activation_cost_ms=100,
|
||||
token_efficiency=0.75,
|
||||
quality_impact=0.95
|
||||
)
|
||||
|
||||
return capabilities
|
||||
|
||||
def _initialize_server_states(self) -> Dict[str, MCPServerState]:
|
||||
"""Initialize server state tracking."""
|
||||
return {
|
||||
server: MCPServerState.AVAILABLE
|
||||
for server in self.server_capabilities.keys()
|
||||
}
|
||||
|
||||
def create_activation_plan(self,
|
||||
user_input: str,
|
||||
context: Dict[str, Any],
|
||||
operation_data: Dict[str, Any]) -> MCPActivationPlan:
|
||||
"""
|
||||
Create intelligent MCP server activation plan.
|
||||
|
||||
Args:
|
||||
user_input: User's request or command
|
||||
context: Session and environment context
|
||||
operation_data: Information about the planned operation
|
||||
|
||||
Returns:
|
||||
MCPActivationPlan with optimized server selection and coordination
|
||||
"""
|
||||
# Detect patterns to determine server needs
|
||||
detection_result = self.pattern_detector.detect_patterns(
|
||||
user_input, context, operation_data
|
||||
)
|
||||
|
||||
# Extract recommended servers from pattern detection
|
||||
recommended_servers = detection_result.recommended_mcp_servers
|
||||
|
||||
# Apply intelligent selection based on context
|
||||
optimized_servers = self._optimize_server_selection(
|
||||
recommended_servers, context, operation_data
|
||||
)
|
||||
|
||||
# Determine activation order for optimal performance
|
||||
activation_order = self._calculate_activation_order(optimized_servers, context)
|
||||
|
||||
# Calculate estimated costs and gains
|
||||
estimated_cost = self._calculate_activation_cost(optimized_servers)
|
||||
efficiency_gains = self._calculate_efficiency_gains(optimized_servers, operation_data)
|
||||
|
||||
# Create fallback strategy
|
||||
fallback_strategy = self._create_fallback_strategy(optimized_servers)
|
||||
|
||||
# Determine coordination strategy
|
||||
coordination_strategy = self._determine_coordination_strategy(
|
||||
optimized_servers, operation_data
|
||||
)
|
||||
|
||||
return MCPActivationPlan(
|
||||
servers_to_activate=optimized_servers,
|
||||
activation_order=activation_order,
|
||||
estimated_cost_ms=estimated_cost,
|
||||
efficiency_gains=efficiency_gains,
|
||||
fallback_strategy=fallback_strategy,
|
||||
coordination_strategy=coordination_strategy
|
||||
)
|
||||
|
||||
def _optimize_server_selection(self,
|
||||
recommended_servers: List[str],
|
||||
context: Dict[str, Any],
|
||||
operation_data: Dict[str, Any]) -> List[str]:
|
||||
"""Apply intelligent optimization to server selection."""
|
||||
optimized = set(recommended_servers)
|
||||
|
||||
# Morphllm vs Serena intelligence selection
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
complexity_score = operation_data.get('complexity_score', 0.0)
|
||||
|
||||
if 'morphllm' in optimized and 'serena' in optimized:
|
||||
# Choose the more appropriate server based on complexity
|
||||
if file_count > 10 or complexity_score > 0.6:
|
||||
optimized.remove('morphllm') # Use Serena for complex operations
|
||||
else:
|
||||
optimized.remove('serena') # Use Morphllm for efficient operations
|
||||
elif file_count > 10 or complexity_score > 0.6:
|
||||
# Auto-add Serena for complex operations
|
||||
optimized.add('serena')
|
||||
optimized.discard('morphllm')
|
||||
elif file_count <= 10 and complexity_score <= 0.6:
|
||||
# Auto-add Morphllm for simple operations
|
||||
optimized.add('morphllm')
|
||||
optimized.discard('serena')
|
||||
|
||||
# Resource constraint optimization
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
if resource_usage > 85:
|
||||
# Remove intensive servers under resource constraints
|
||||
intensive_servers = {
|
||||
name for name, cap in self.server_capabilities.items()
|
||||
if cap.performance_profile == 'intensive'
|
||||
}
|
||||
optimized -= intensive_servers
|
||||
|
||||
# Performance optimization based on operation type
|
||||
operation_type = operation_data.get('operation_type', '')
|
||||
if operation_type in ['read', 'analyze'] and 'sequential' not in optimized:
|
||||
# Add Sequential for analysis operations
|
||||
optimized.add('sequential')
|
||||
|
||||
# Auto-add Context7 if external libraries detected
|
||||
if operation_data.get('has_external_dependencies', False):
|
||||
optimized.add('context7')
|
||||
|
||||
return list(optimized)
|
||||
|
||||
def _calculate_activation_order(self, servers: List[str], context: Dict[str, Any]) -> List[str]:
|
||||
"""Calculate optimal activation order for performance."""
|
||||
if not servers:
|
||||
return []
|
||||
|
||||
# Sort by activation cost (lightweight first)
|
||||
server_costs = [
|
||||
(server, self.server_capabilities[server].activation_cost_ms)
|
||||
for server in servers
|
||||
]
|
||||
server_costs.sort(key=lambda x: x[1])
|
||||
|
||||
# Special ordering rules
|
||||
ordered = []
|
||||
|
||||
# 1. Serena first if present (provides context for others)
|
||||
if 'serena' in servers:
|
||||
ordered.append('serena')
|
||||
servers = [s for s in servers if s != 'serena']
|
||||
|
||||
# 2. Context7 early for documentation context
|
||||
if 'context7' in servers:
|
||||
ordered.append('context7')
|
||||
servers = [s for s in servers if s != 'context7']
|
||||
|
||||
# 3. Remaining servers by cost
|
||||
remaining_costs = [
|
||||
(server, self.server_capabilities[server].activation_cost_ms)
|
||||
for server in servers
|
||||
]
|
||||
remaining_costs.sort(key=lambda x: x[1])
|
||||
ordered.extend([server for server, _ in remaining_costs])
|
||||
|
||||
return ordered
|
||||
|
||||
def _calculate_activation_cost(self, servers: List[str]) -> int:
|
||||
"""Calculate total activation cost in milliseconds."""
|
||||
return sum(
|
||||
self.server_capabilities[server].activation_cost_ms
|
||||
for server in servers
|
||||
if server in self.server_capabilities
|
||||
)
|
||||
|
||||
def _calculate_efficiency_gains(self, servers: List[str], operation_data: Dict[str, Any]) -> Dict[str, float]:
|
||||
"""Calculate expected efficiency gains from server activation."""
|
||||
gains = {}
|
||||
|
||||
for server in servers:
|
||||
if server not in self.server_capabilities:
|
||||
continue
|
||||
|
||||
capability = self.server_capabilities[server]
|
||||
|
||||
# Base efficiency gain
|
||||
base_gain = capability.token_efficiency * capability.quality_impact
|
||||
|
||||
# Context-specific adjustments
|
||||
if server == 'morphllm' and operation_data.get('file_count', 1) <= 5:
|
||||
gains[server] = base_gain * 1.2 # Extra efficient for small operations
|
||||
elif server == 'serena' and operation_data.get('complexity_score', 0) > 0.6:
|
||||
gains[server] = base_gain * 1.3 # Extra valuable for complex operations
|
||||
elif server == 'sequential' and 'debug' in operation_data.get('operation_type', ''):
|
||||
gains[server] = base_gain * 1.4 # Extra valuable for debugging
|
||||
else:
|
||||
gains[server] = base_gain
|
||||
|
||||
return gains
|
||||
|
||||
def _create_fallback_strategy(self, servers: List[str]) -> Dict[str, str]:
|
||||
"""Create fallback strategy for server failures."""
|
||||
fallbacks = {}
|
||||
|
||||
# Define fallback mappings
|
||||
fallback_map = {
|
||||
'morphllm': 'serena', # Serena can handle editing
|
||||
'serena': 'morphllm', # Morphllm can handle simple edits
|
||||
'sequential': 'context7', # Context7 for documentation-based analysis
|
||||
'context7': 'sequential', # Sequential for complex analysis
|
||||
'magic': 'morphllm', # Morphllm for component generation
|
||||
'playwright': 'sequential' # Sequential for test planning
|
||||
}
|
||||
|
||||
for server in servers:
|
||||
fallback = fallback_map.get(server)
|
||||
if fallback and fallback not in servers:
|
||||
fallbacks[server] = fallback
|
||||
else:
|
||||
fallbacks[server] = 'native_tools' # Fall back to native Claude tools
|
||||
|
||||
return fallbacks
|
||||
|
||||
def _determine_coordination_strategy(self, servers: List[str], operation_data: Dict[str, Any]) -> str:
|
||||
"""Determine how servers should coordinate."""
|
||||
if len(servers) <= 1:
|
||||
return 'single_server'
|
||||
|
||||
# Sequential coordination for complex analysis
|
||||
if 'sequential' in servers and operation_data.get('complexity_score', 0) > 0.6:
|
||||
return 'sequential_lead'
|
||||
|
||||
# Serena coordination for multi-file operations
|
||||
if 'serena' in servers and operation_data.get('file_count', 1) > 5:
|
||||
return 'serena_lead'
|
||||
|
||||
# Parallel coordination for independent operations
|
||||
if len(servers) >= 3:
|
||||
return 'parallel_with_sync'
|
||||
|
||||
return 'collaborative'
|
||||
|
||||
def execute_activation_plan(self, plan: MCPActivationPlan, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Execute MCP server activation plan with error handling and performance tracking.
|
||||
|
||||
Args:
|
||||
plan: MCPActivationPlan to execute
|
||||
context: Current session context
|
||||
|
||||
Returns:
|
||||
Execution results with performance metrics and activated servers
|
||||
"""
|
||||
start_time = time.time()
|
||||
activated_servers = []
|
||||
failed_servers = []
|
||||
fallback_activations = []
|
||||
|
||||
for server in plan.activation_order:
|
||||
try:
|
||||
# Check server availability
|
||||
if self.server_states.get(server) == MCPServerState.UNAVAILABLE:
|
||||
failed_servers.append(server)
|
||||
self._handle_server_fallback(server, plan, fallback_activations)
|
||||
continue
|
||||
|
||||
# Activate server (simulated - real implementation would call MCP)
|
||||
self.server_states[server] = MCPServerState.LOADING
|
||||
activation_start = time.time()
|
||||
|
||||
# Simulate activation time
|
||||
expected_cost = self.server_capabilities[server].activation_cost_ms
|
||||
actual_cost = expected_cost * (0.8 + 0.4 * hash(server) % 1000 / 1000) # Simulated variance
|
||||
|
||||
self.server_states[server] = MCPServerState.AVAILABLE
|
||||
activated_servers.append(server)
|
||||
|
||||
# Track performance
|
||||
activation_time = (time.time() - activation_start) * 1000
|
||||
self.performance_metrics[server] = {
|
||||
'last_activation_ms': activation_time,
|
||||
'expected_ms': expected_cost,
|
||||
'efficiency_ratio': expected_cost / max(activation_time, 1)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
failed_servers.append(server)
|
||||
self.server_states[server] = MCPServerState.ERROR
|
||||
self._handle_server_fallback(server, plan, fallback_activations)
|
||||
|
||||
total_time = (time.time() - start_time) * 1000
|
||||
|
||||
# Update activation history
|
||||
self.activation_history.append({
|
||||
'timestamp': time.time(),
|
||||
'plan': plan,
|
||||
'activated': activated_servers,
|
||||
'failed': failed_servers,
|
||||
'fallbacks': fallback_activations,
|
||||
'total_time_ms': total_time
|
||||
})
|
||||
|
||||
return {
|
||||
'activated_servers': activated_servers,
|
||||
'failed_servers': failed_servers,
|
||||
'fallback_activations': fallback_activations,
|
||||
'total_activation_time_ms': total_time,
|
||||
'coordination_strategy': plan.coordination_strategy,
|
||||
'performance_metrics': self.performance_metrics
|
||||
}
|
||||
|
||||
def _handle_server_fallback(self, failed_server: str, plan: MCPActivationPlan, fallback_activations: List[str]):
|
||||
"""Handle server activation failure with fallback strategy."""
|
||||
fallback = plan.fallback_strategy.get(failed_server)
|
||||
|
||||
if fallback and fallback != 'native_tools' and fallback not in plan.servers_to_activate:
|
||||
# Try to activate fallback server
|
||||
if self.server_states.get(fallback) == MCPServerState.AVAILABLE:
|
||||
fallback_activations.append(f"{failed_server}->{fallback}")
|
||||
# In real implementation, would activate fallback server
|
||||
|
||||
def get_optimization_recommendations(self, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Get recommendations for optimizing MCP server usage."""
|
||||
recommendations = []
|
||||
|
||||
# Analyze activation history for patterns
|
||||
if len(self.activation_history) >= 5:
|
||||
recent_activations = self.activation_history[-5:]
|
||||
|
||||
# Check for frequently failing servers
|
||||
failed_counts = {}
|
||||
for activation in recent_activations:
|
||||
for failed in activation['failed']:
|
||||
failed_counts[failed] = failed_counts.get(failed, 0) + 1
|
||||
|
||||
for server, count in failed_counts.items():
|
||||
if count >= 3:
|
||||
recommendations.append(f"Server {server} failing frequently - consider fallback strategy")
|
||||
|
||||
# Check for performance issues
|
||||
avg_times = {}
|
||||
for activation in recent_activations:
|
||||
total_time = activation['total_time_ms']
|
||||
server_count = len(activation['activated'])
|
||||
if server_count > 0:
|
||||
avg_time_per_server = total_time / server_count
|
||||
avg_times[len(activation['activated'])] = avg_time_per_server
|
||||
|
||||
if avg_times and max(avg_times.values()) > 500:
|
||||
recommendations.append("Consider reducing concurrent server activations for better performance")
|
||||
|
||||
# Resource usage recommendations
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
if resource_usage > 80:
|
||||
recommendations.append("High resource usage - consider lightweight servers only")
|
||||
|
||||
return {
|
||||
'recommendations': recommendations,
|
||||
'performance_metrics': self.performance_metrics,
|
||||
'server_states': {k: v.value for k, v in self.server_states.items()},
|
||||
'efficiency_score': self._calculate_overall_efficiency()
|
||||
}
|
||||
|
||||
def _calculate_overall_efficiency(self) -> float:
|
||||
"""Calculate overall MCP system efficiency."""
|
||||
if not self.performance_metrics:
|
||||
return 1.0
|
||||
|
||||
efficiency_scores = []
|
||||
for server, metrics in self.performance_metrics.items():
|
||||
efficiency_ratio = metrics.get('efficiency_ratio', 1.0)
|
||||
efficiency_scores.append(min(efficiency_ratio, 2.0)) # Cap at 200% efficiency
|
||||
|
||||
return sum(efficiency_scores) / len(efficiency_scores) if efficiency_scores else 1.0
|
||||
|
||||
def select_optimal_server(self, tool_name: str, context: Dict[str, Any]) -> str:
|
||||
"""
|
||||
Select the most appropriate MCP server for a given tool and context.
|
||||
|
||||
Enhanced with intelligent analysis of:
|
||||
- User intent keywords and patterns
|
||||
- Operation type classification
|
||||
- Test type specific routing
|
||||
- Multi-factor context analysis
|
||||
- Smart fallback logic
|
||||
|
||||
Args:
|
||||
tool_name: Name of the tool to be executed
|
||||
context: Context information for intelligent selection
|
||||
|
||||
Returns:
|
||||
Name of the optimal server for the tool
|
||||
"""
|
||||
# Extract context information
|
||||
user_intent = context.get('user_intent', '').lower()
|
||||
operation_type = context.get('operation_type', '').lower()
|
||||
test_type = context.get('test_type', '').lower()
|
||||
file_count = context.get('file_count', 1)
|
||||
complexity_score = context.get('complexity_score', 0.0)
|
||||
has_external_deps = context.get('has_external_dependencies', False)
|
||||
|
||||
# 1. KEYWORD-BASED INTENT ANALYSIS
|
||||
# UI/Frontend keywords → Magic
|
||||
ui_keywords = [
|
||||
'component', 'ui', 'frontend', 'react', 'vue', 'angular', 'button', 'form',
|
||||
'modal', 'layout', 'design', 'responsive', 'css', 'styling', 'theme',
|
||||
'navigation', 'menu', 'sidebar', 'dashboard', 'card', 'table', 'chart'
|
||||
]
|
||||
|
||||
# Testing keywords → Playwright
|
||||
test_keywords = [
|
||||
'test', 'testing', 'e2e', 'end-to-end', 'browser', 'automation',
|
||||
'selenium', 'cypress', 'performance', 'load test', 'visual test',
|
||||
'regression', 'cross-browser', 'integration test'
|
||||
]
|
||||
|
||||
# Documentation keywords → Context7
|
||||
doc_keywords = [
|
||||
'documentation', 'docs', 'library', 'framework', 'api', 'reference',
|
||||
'best practice', 'pattern', 'tutorial', 'guide', 'example', 'usage',
|
||||
'install', 'setup', 'configuration', 'migration'
|
||||
]
|
||||
|
||||
# Analysis/Debug keywords → Sequential
|
||||
analysis_keywords = [
|
||||
'analyze', 'debug', 'troubleshoot', 'investigate', 'complex', 'architecture',
|
||||
'system', 'performance', 'bottleneck', 'optimization', 'refactor',
|
||||
'review', 'audit', 'security', 'vulnerability'
|
||||
]
|
||||
|
||||
# Memory/Context keywords → Serena
|
||||
context_keywords = [
|
||||
'memory', 'context', 'semantic', 'symbol', 'reference', 'definition',
|
||||
'search', 'find', 'locate', 'navigate', 'project', 'codebase', 'workspace'
|
||||
]
|
||||
|
||||
# Editing keywords → Morphllm
|
||||
edit_keywords = [
|
||||
'edit', 'modify', 'change', 'update', 'fix', 'replace', 'rewrite',
|
||||
'format', 'style', 'cleanup', 'transform', 'apply', 'batch'
|
||||
]
|
||||
|
||||
# Check user intent against keyword categories
|
||||
intent_scores = {}
|
||||
|
||||
for keyword in ui_keywords:
|
||||
if keyword in user_intent:
|
||||
intent_scores['magic'] = intent_scores.get('magic', 0) + 1
|
||||
|
||||
for keyword in test_keywords:
|
||||
if keyword in user_intent:
|
||||
intent_scores['playwright'] = intent_scores.get('playwright', 0) + 1
|
||||
|
||||
for keyword in doc_keywords:
|
||||
if keyword in user_intent:
|
||||
intent_scores['context7'] = intent_scores.get('context7', 0) + 1
|
||||
|
||||
for keyword in analysis_keywords:
|
||||
if keyword in user_intent:
|
||||
intent_scores['sequential'] = intent_scores.get('sequential', 0) + 1
|
||||
|
||||
for keyword in context_keywords:
|
||||
if keyword in user_intent:
|
||||
intent_scores['serena'] = intent_scores.get('serena', 0) + 1
|
||||
|
||||
for keyword in edit_keywords:
|
||||
if keyword in user_intent:
|
||||
intent_scores['morphllm'] = intent_scores.get('morphllm', 0) + 1
|
||||
|
||||
# 2. OPERATION TYPE ANALYSIS
|
||||
operation_server_map = {
|
||||
'create': 'magic', # UI creation
|
||||
'build': 'magic', # Component building
|
||||
'implement': 'magic', # Feature implementation
|
||||
'test': 'playwright', # Testing operations
|
||||
'validate': 'playwright', # Validation testing
|
||||
'analyze': 'sequential', # Analysis operations
|
||||
'debug': 'sequential', # Debugging
|
||||
'troubleshoot': 'sequential', # Problem solving
|
||||
'document': 'context7', # Documentation
|
||||
'research': 'context7', # Research operations
|
||||
'edit': 'morphllm', # File editing
|
||||
'modify': 'morphllm', # Content modification
|
||||
'search': 'serena', # Code search
|
||||
'find': 'serena', # Finding operations
|
||||
'navigate': 'serena' # Navigation
|
||||
}
|
||||
|
||||
if operation_type in operation_server_map:
|
||||
server = operation_server_map[operation_type]
|
||||
intent_scores[server] = intent_scores.get(server, 0) + 2 # Higher weight
|
||||
|
||||
# 3. TEST TYPE SPECIFIC ROUTING
|
||||
test_type_map = {
|
||||
'e2e': 'playwright',
|
||||
'end-to-end': 'playwright',
|
||||
'integration': 'playwright',
|
||||
'browser': 'playwright',
|
||||
'visual': 'playwright',
|
||||
'performance': 'playwright',
|
||||
'load': 'playwright',
|
||||
'ui': 'playwright',
|
||||
'functional': 'playwright',
|
||||
'regression': 'playwright',
|
||||
'cross-browser': 'playwright',
|
||||
'unit': 'sequential', # Complex unit test analysis
|
||||
'security': 'sequential', # Security test analysis
|
||||
'api': 'sequential' # API test analysis
|
||||
}
|
||||
|
||||
if test_type and test_type in test_type_map:
|
||||
server = test_type_map[test_type]
|
||||
intent_scores[server] = intent_scores.get(server, 0) + 3 # Highest weight
|
||||
|
||||
# 4. TOOL-BASED MAPPING (Original logic enhanced)
|
||||
tool_server_mapping = {
|
||||
# File operations - context dependent
|
||||
'read_file': None, # Will be determined by context
|
||||
'write_file': None, # Will be determined by context
|
||||
'edit_file': None, # Will be determined by context
|
||||
|
||||
# Analysis operations
|
||||
'analyze_architecture': 'sequential',
|
||||
'complex_reasoning': 'sequential',
|
||||
'debug_analysis': 'sequential',
|
||||
'system_analysis': 'sequential',
|
||||
'performance_analysis': 'sequential',
|
||||
|
||||
# UI operations
|
||||
'create_component': 'magic',
|
||||
'ui_component': 'magic',
|
||||
'design_system': 'magic',
|
||||
'build_ui': 'magic',
|
||||
'frontend_generation': 'magic',
|
||||
|
||||
# Testing operations
|
||||
'browser_test': 'playwright',
|
||||
'e2e_test': 'playwright',
|
||||
'performance_test': 'playwright',
|
||||
'visual_test': 'playwright',
|
||||
'cross_browser_test': 'playwright',
|
||||
|
||||
# Documentation operations
|
||||
'get_documentation': 'context7',
|
||||
'library_docs': 'context7',
|
||||
'framework_patterns': 'context7',
|
||||
'api_reference': 'context7',
|
||||
'best_practices': 'context7',
|
||||
|
||||
# Semantic operations
|
||||
'semantic_analysis': 'serena',
|
||||
'project_context': 'serena',
|
||||
'memory_management': 'serena',
|
||||
'symbol_search': 'serena',
|
||||
'code_navigation': 'serena',
|
||||
|
||||
# Fast editing operations
|
||||
'fast_edit': 'morphllm',
|
||||
'pattern_application': 'morphllm',
|
||||
'batch_edit': 'morphllm',
|
||||
'text_transformation': 'morphllm'
|
||||
}
|
||||
|
||||
# Primary server selection based on tool
|
||||
primary_server = tool_server_mapping.get(tool_name)
|
||||
if primary_server:
|
||||
intent_scores[primary_server] = intent_scores.get(primary_server, 0) + 2
|
||||
|
||||
# 5. COMPLEXITY AND SCALE ANALYSIS
|
||||
|
||||
# High complexity → Sequential for analysis
|
||||
if complexity_score > 0.6:
|
||||
intent_scores['sequential'] = intent_scores.get('sequential', 0) + 2
|
||||
|
||||
# Large file count → Serena for project context
|
||||
if file_count > 10:
|
||||
intent_scores['serena'] = intent_scores.get('serena', 0) + 2
|
||||
elif file_count > 5:
|
||||
intent_scores['serena'] = intent_scores.get('serena', 0) + 1
|
||||
|
||||
# Small operations → Morphllm for efficiency
|
||||
if file_count <= 3 and complexity_score <= 0.4:
|
||||
intent_scores['morphllm'] = intent_scores.get('morphllm', 0) + 1
|
||||
|
||||
# External dependencies → Context7 for documentation
|
||||
if has_external_deps:
|
||||
intent_scores['context7'] = intent_scores.get('context7', 0) + 1
|
||||
|
||||
# 6. CONTEXTUAL FALLBACK LOGIC
|
||||
|
||||
# Check for file operation context-dependent routing
|
||||
if tool_name in ['read_file', 'write_file', 'edit_file']:
|
||||
# Route based on context
|
||||
if any(keyword in user_intent for keyword in ui_keywords):
|
||||
intent_scores['magic'] = intent_scores.get('magic', 0) + 2
|
||||
elif any(keyword in user_intent for keyword in test_keywords):
|
||||
intent_scores['playwright'] = intent_scores.get('playwright', 0) + 2
|
||||
elif complexity_score > 0.5 or file_count > 5:
|
||||
intent_scores['serena'] = intent_scores.get('serena', 0) + 2
|
||||
else:
|
||||
intent_scores['morphllm'] = intent_scores.get('morphllm', 0) + 2
|
||||
|
||||
# 7. SERVER SELECTION DECISION
|
||||
|
||||
# Return server with highest score
|
||||
if intent_scores:
|
||||
best_server = max(intent_scores.items(), key=lambda x: x[1])[0]
|
||||
|
||||
# Validate server availability
|
||||
if self.server_states.get(best_server) == MCPServerState.AVAILABLE:
|
||||
return best_server
|
||||
|
||||
# 8. INTELLIGENT FALLBACK CHAIN
|
||||
|
||||
# Fallback based on context characteristics
|
||||
if complexity_score > 0.7 or 'complex' in user_intent or 'analyze' in user_intent:
|
||||
return 'sequential'
|
||||
elif any(keyword in user_intent for keyword in ui_keywords) or operation_type in ['create', 'build']:
|
||||
return 'magic'
|
||||
elif any(keyword in user_intent for keyword in test_keywords) or 'test' in operation_type:
|
||||
return 'playwright'
|
||||
elif has_external_deps or any(keyword in user_intent for keyword in doc_keywords):
|
||||
return 'context7'
|
||||
elif file_count > 10 or any(keyword in user_intent for keyword in context_keywords):
|
||||
return 'serena'
|
||||
else:
|
||||
return 'morphllm' # Efficient default for simple operations
|
||||
|
||||
def get_fallback_server(self, tool_name: str, context: Dict[str, Any]) -> str:
|
||||
"""
|
||||
Get fallback server when primary server fails.
|
||||
|
||||
Args:
|
||||
tool_name: Name of the tool
|
||||
context: Context information
|
||||
|
||||
Returns:
|
||||
Name of the fallback server
|
||||
"""
|
||||
primary_server = self.select_optimal_server(tool_name, context)
|
||||
|
||||
# Define fallback chains
|
||||
fallback_chains = {
|
||||
'sequential': 'serena',
|
||||
'serena': 'morphllm',
|
||||
'morphllm': 'context7',
|
||||
'magic': 'morphllm',
|
||||
'playwright': 'sequential',
|
||||
'context7': 'morphllm'
|
||||
}
|
||||
|
||||
fallback = fallback_chains.get(primary_server, 'morphllm')
|
||||
|
||||
# Avoid circular fallback
|
||||
if fallback == primary_server:
|
||||
return 'morphllm'
|
||||
|
||||
return fallback
|
||||
@@ -1,985 +0,0 @@
|
||||
"""
|
||||
Pattern Detection Engine for SuperClaude-Lite
|
||||
|
||||
Intelligent pattern detection for automatic mode activation,
|
||||
MCP server selection, and operational optimization.
|
||||
"""
|
||||
|
||||
import re
|
||||
import json
|
||||
from typing import Dict, Any, List, Set, Optional, Tuple
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
from yaml_loader import config_loader
|
||||
|
||||
|
||||
class PatternType(Enum):
|
||||
"""Types of patterns we can detect."""
|
||||
MODE_TRIGGER = "mode_trigger"
|
||||
MCP_SERVER = "mcp_server"
|
||||
OPERATION_TYPE = "operation_type"
|
||||
COMPLEXITY_INDICATOR = "complexity_indicator"
|
||||
PERSONA_HINT = "persona_hint"
|
||||
PERFORMANCE_HINT = "performance_hint"
|
||||
|
||||
|
||||
@dataclass
|
||||
class PatternMatch:
|
||||
"""A detected pattern match."""
|
||||
pattern_type: PatternType
|
||||
pattern_name: str
|
||||
confidence: float # 0.0 to 1.0
|
||||
matched_text: str
|
||||
suggestions: List[str]
|
||||
metadata: Dict[str, Any]
|
||||
|
||||
|
||||
@dataclass
|
||||
class DetectionResult:
|
||||
"""Result of pattern detection analysis."""
|
||||
matches: List[PatternMatch]
|
||||
recommended_modes: List[str]
|
||||
recommended_mcp_servers: List[str]
|
||||
suggested_flags: List[str]
|
||||
complexity_score: float
|
||||
confidence_score: float
|
||||
|
||||
|
||||
class PatternDetector:
|
||||
"""
|
||||
Intelligent pattern detection system.
|
||||
|
||||
Analyzes user input, context, and operation patterns to determine:
|
||||
- Which SuperClaude modes should be activated
|
||||
- Which MCP servers are needed
|
||||
- What optimization flags to apply
|
||||
- Complexity and performance considerations
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize pattern detector with configuration loading and error handling."""
|
||||
try:
|
||||
self.patterns = config_loader.load_config('modes') or {}
|
||||
self.mcp_patterns = config_loader.load_config('orchestrator') or {}
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to load configuration: {e}")
|
||||
self.patterns = {}
|
||||
self.mcp_patterns = {}
|
||||
|
||||
self._compile_patterns()
|
||||
|
||||
def _compile_patterns(self):
|
||||
"""Compile regex patterns for efficient matching with proper error handling."""
|
||||
self.compiled_patterns = {}
|
||||
self.mode_configs = {}
|
||||
self.mcp_configs = {}
|
||||
|
||||
# Load mode detection patterns from the correct YAML structure
|
||||
mode_detection = self.patterns.get('mode_detection', {})
|
||||
for mode_name, mode_config in mode_detection.items():
|
||||
try:
|
||||
# Store mode configuration for threshold access
|
||||
self.mode_configs[mode_name] = mode_config
|
||||
|
||||
# Compile all trigger patterns from different categories
|
||||
all_patterns = []
|
||||
trigger_patterns = mode_config.get('trigger_patterns', {})
|
||||
|
||||
# Handle different YAML structures
|
||||
if isinstance(trigger_patterns, list):
|
||||
# Simple list of patterns
|
||||
all_patterns.extend(trigger_patterns)
|
||||
elif isinstance(trigger_patterns, dict):
|
||||
# Nested categories of patterns
|
||||
for category, patterns in trigger_patterns.items():
|
||||
if isinstance(patterns, list):
|
||||
all_patterns.extend(patterns)
|
||||
elif isinstance(patterns, str):
|
||||
all_patterns.append(patterns)
|
||||
elif isinstance(trigger_patterns, str):
|
||||
# Single pattern string
|
||||
all_patterns.append(trigger_patterns)
|
||||
|
||||
# Compile patterns with error handling
|
||||
compiled = []
|
||||
for pattern in all_patterns:
|
||||
try:
|
||||
compiled.append(re.compile(pattern, re.IGNORECASE))
|
||||
except re.error as e:
|
||||
print(f"Warning: Invalid regex pattern '{pattern}' for mode {mode_name}: {e}")
|
||||
|
||||
self.compiled_patterns[f"mode_{mode_name}"] = compiled
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Error compiling patterns for mode {mode_name}: {e}")
|
||||
self.compiled_patterns[f"mode_{mode_name}"] = []
|
||||
|
||||
# Load MCP server patterns from routing_patterns
|
||||
routing_patterns = self.mcp_patterns.get('routing_patterns', {})
|
||||
for server_name, server_config in routing_patterns.items():
|
||||
try:
|
||||
# Store server configuration
|
||||
self.mcp_configs[server_name] = server_config
|
||||
|
||||
triggers = server_config.get('triggers', [])
|
||||
compiled = []
|
||||
for trigger in triggers:
|
||||
try:
|
||||
compiled.append(re.compile(trigger, re.IGNORECASE))
|
||||
except re.error as e:
|
||||
print(f"Warning: Invalid regex pattern '{trigger}' for server {server_name}: {e}")
|
||||
|
||||
self.compiled_patterns[f"mcp_{server_name}"] = compiled
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Error compiling patterns for MCP server {server_name}: {e}")
|
||||
self.compiled_patterns[f"mcp_{server_name}"] = []
|
||||
|
||||
def detect_patterns(self,
|
||||
user_input: str,
|
||||
context: Dict[str, Any],
|
||||
operation_data: Dict[str, Any]) -> DetectionResult:
|
||||
"""
|
||||
Perform comprehensive pattern detection with input validation and error handling.
|
||||
|
||||
Args:
|
||||
user_input: User's request or command
|
||||
context: Session and environment context
|
||||
operation_data: Information about the planned operation
|
||||
|
||||
Returns:
|
||||
DetectionResult with all detected patterns and recommendations
|
||||
"""
|
||||
# Validate inputs
|
||||
is_valid, validation_message = self.validate_input(user_input, context, operation_data)
|
||||
if not is_valid:
|
||||
# Return empty result for invalid inputs
|
||||
return DetectionResult(
|
||||
matches=[],
|
||||
recommended_modes=[],
|
||||
recommended_mcp_servers=[],
|
||||
suggested_flags=[],
|
||||
complexity_score=0.0,
|
||||
confidence_score=0.0
|
||||
)
|
||||
|
||||
matches = []
|
||||
|
||||
try:
|
||||
# Detect mode triggers
|
||||
mode_matches = self._detect_mode_patterns(user_input, context)
|
||||
matches.extend(mode_matches)
|
||||
|
||||
# Detect MCP server needs
|
||||
mcp_matches = self._detect_mcp_patterns(user_input, context, operation_data)
|
||||
matches.extend(mcp_matches)
|
||||
|
||||
# Detect complexity indicators
|
||||
complexity_matches = self._detect_complexity_patterns(user_input, operation_data)
|
||||
matches.extend(complexity_matches)
|
||||
|
||||
# Detect persona hints
|
||||
persona_matches = self._detect_persona_patterns(user_input, context)
|
||||
matches.extend(persona_matches)
|
||||
|
||||
# Calculate overall scores
|
||||
complexity_score = self._calculate_complexity_score(matches, operation_data)
|
||||
confidence_score = self._calculate_confidence_score(matches)
|
||||
|
||||
# Generate recommendations
|
||||
recommended_modes = self._get_recommended_modes(matches, complexity_score)
|
||||
recommended_mcp_servers = self._get_recommended_mcp_servers(matches, context)
|
||||
suggested_flags = self._get_suggested_flags(matches, complexity_score, context)
|
||||
|
||||
return DetectionResult(
|
||||
matches=matches,
|
||||
recommended_modes=recommended_modes,
|
||||
recommended_mcp_servers=recommended_mcp_servers,
|
||||
suggested_flags=suggested_flags,
|
||||
complexity_score=complexity_score,
|
||||
confidence_score=confidence_score
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error during pattern detection: {e}")
|
||||
# Return partial results if available
|
||||
return DetectionResult(
|
||||
matches=matches, # Include any matches found before error
|
||||
recommended_modes=[],
|
||||
recommended_mcp_servers=[],
|
||||
suggested_flags=[],
|
||||
complexity_score=operation_data.get('complexity_score', 0.0),
|
||||
confidence_score=0.0
|
||||
)
|
||||
|
||||
def _detect_mode_patterns(self, user_input: str, context: Dict[str, Any]) -> List[PatternMatch]:
|
||||
"""Detect which SuperClaude modes should be activated using compiled patterns."""
|
||||
matches = []
|
||||
|
||||
# Iterate through all compiled mode patterns
|
||||
for pattern_key, compiled_patterns in self.compiled_patterns.items():
|
||||
if not pattern_key.startswith("mode_"):
|
||||
continue
|
||||
|
||||
mode_name = pattern_key[5:] # Remove "mode_" prefix
|
||||
mode_config = self.mode_configs.get(mode_name, {})
|
||||
confidence_threshold = mode_config.get('confidence_threshold', 0.7)
|
||||
|
||||
# Check if any pattern matches
|
||||
for pattern in compiled_patterns:
|
||||
try:
|
||||
match = pattern.search(user_input)
|
||||
if match:
|
||||
# Calculate confidence based on pattern type and context
|
||||
confidence = self._calculate_mode_confidence(mode_name, match, context)
|
||||
|
||||
# Only include if above threshold
|
||||
if confidence >= confidence_threshold:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.MODE_TRIGGER,
|
||||
pattern_name=mode_name,
|
||||
confidence=confidence,
|
||||
matched_text=match.group(),
|
||||
suggestions=[f"Enable {mode_name} mode based on detected patterns"],
|
||||
metadata={
|
||||
"mode": mode_name,
|
||||
"auto_activate": mode_config.get('activation_type') == 'automatic',
|
||||
"threshold_met": confidence >= confidence_threshold
|
||||
}
|
||||
))
|
||||
break # Stop after first match for this mode
|
||||
except Exception as e:
|
||||
print(f"Warning: Error matching pattern for mode {mode_name}: {e}")
|
||||
|
||||
# Check context-based triggers (resource usage, complexity, etc.)
|
||||
matches.extend(self._detect_context_mode_triggers(context))
|
||||
|
||||
return matches
|
||||
|
||||
def _calculate_mode_confidence(self, mode_name: str, match: re.Match, context: Dict[str, Any]) -> float:
|
||||
"""Calculate confidence score for mode activation based on match and context."""
|
||||
base_confidence = 0.7
|
||||
|
||||
# Mode-specific confidence adjustments
|
||||
mode_adjustments = {
|
||||
'brainstorming': {
|
||||
'uncertainty_words': ['maybe', 'not sure', 'thinking about', 'wondering'],
|
||||
'project_words': ['new project', 'startup', 'build something'],
|
||||
'exploration_words': ['brainstorm', 'explore', 'figure out']
|
||||
},
|
||||
'task_management': {
|
||||
'scope_words': ['multiple', 'many', 'complex', 'comprehensive'],
|
||||
'build_words': ['build', 'implement', 'create', 'develop']
|
||||
},
|
||||
'token_efficiency': {
|
||||
'efficiency_words': ['brief', 'concise', 'compressed', 'short'],
|
||||
'resource_words': ['token', 'resource', 'memory', 'optimization']
|
||||
}
|
||||
}
|
||||
|
||||
adjustments = mode_adjustments.get(mode_name, {})
|
||||
matched_text = match.group().lower()
|
||||
|
||||
# Boost confidence based on specific word categories
|
||||
confidence_boost = 0.0
|
||||
for category, words in adjustments.items():
|
||||
if any(word in matched_text for word in words):
|
||||
confidence_boost += 0.1
|
||||
|
||||
# Context-based adjustments
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
if mode_name == 'token_efficiency' and resource_usage > 75:
|
||||
confidence_boost += 0.2
|
||||
|
||||
file_count = context.get('file_count', 1)
|
||||
complexity_score = context.get('complexity_score', 0.0)
|
||||
if mode_name == 'task_management' and (file_count > 3 or complexity_score > 0.4):
|
||||
confidence_boost += 0.15
|
||||
|
||||
return min(base_confidence + confidence_boost, 1.0)
|
||||
|
||||
def _detect_context_mode_triggers(self, context: Dict[str, Any]) -> List[PatternMatch]:
|
||||
"""Detect mode triggers based on context alone (not user input)."""
|
||||
matches = []
|
||||
|
||||
# Resource-based token efficiency trigger
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
if resource_usage > 75:
|
||||
token_efficiency_config = self.mode_configs.get('token_efficiency', {})
|
||||
confidence_threshold = token_efficiency_config.get('confidence_threshold', 0.75)
|
||||
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.MODE_TRIGGER,
|
||||
pattern_name="token_efficiency",
|
||||
confidence=0.85,
|
||||
matched_text="high_resource_usage",
|
||||
suggestions=["Auto-enable token efficiency due to resource constraints"],
|
||||
metadata={
|
||||
"mode": "token_efficiency",
|
||||
"trigger": "resource_constraint",
|
||||
"resource_usage": resource_usage
|
||||
}
|
||||
))
|
||||
|
||||
# Complexity-based task management trigger
|
||||
file_count = context.get('file_count', 1)
|
||||
complexity_score = context.get('complexity_score', 0.0)
|
||||
task_mgmt_config = self.mode_configs.get('task_management', {})
|
||||
auto_thresholds = task_mgmt_config.get('auto_activation_thresholds', {})
|
||||
|
||||
file_threshold = auto_thresholds.get('file_count', 3)
|
||||
complexity_threshold = auto_thresholds.get('complexity_score', 0.4)
|
||||
|
||||
if file_count >= file_threshold or complexity_score >= complexity_threshold:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.MODE_TRIGGER,
|
||||
pattern_name="task_management",
|
||||
confidence=0.8,
|
||||
matched_text="complexity_threshold_met",
|
||||
suggestions=["Auto-enable task management for complex operations"],
|
||||
metadata={
|
||||
"mode": "task_management",
|
||||
"trigger": "complexity_threshold",
|
||||
"file_count": file_count,
|
||||
"complexity_score": complexity_score
|
||||
}
|
||||
))
|
||||
|
||||
return matches
|
||||
|
||||
def _detect_mcp_patterns(self, user_input: str, context: Dict[str, Any], operation_data: Dict[str, Any]) -> List[PatternMatch]:
|
||||
"""Detect which MCP servers should be activated using compiled patterns."""
|
||||
matches = []
|
||||
|
||||
# Iterate through all compiled MCP server patterns
|
||||
for pattern_key, compiled_patterns in self.compiled_patterns.items():
|
||||
if not pattern_key.startswith("mcp_"):
|
||||
continue
|
||||
|
||||
server_name = pattern_key[4:] # Remove "mcp_" prefix
|
||||
server_config = self.mcp_configs.get(server_name, {})
|
||||
confidence_threshold = server_config.get('confidence_threshold', 0.7)
|
||||
|
||||
# Check if any pattern matches
|
||||
for pattern in compiled_patterns:
|
||||
try:
|
||||
match = pattern.search(user_input)
|
||||
if match:
|
||||
# Calculate confidence based on server type and context
|
||||
confidence = self._calculate_mcp_confidence(server_name, match, context, operation_data)
|
||||
|
||||
# Only include if above threshold
|
||||
if confidence >= confidence_threshold:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.MCP_SERVER,
|
||||
pattern_name=server_name,
|
||||
confidence=confidence,
|
||||
matched_text=match.group(),
|
||||
suggestions=[f"Enable {server_name} server for {server_config.get('capabilities', ['general'])[0]} capabilities"],
|
||||
metadata={
|
||||
"mcp_server": server_name,
|
||||
"confidence_threshold": confidence_threshold,
|
||||
"server_config": server_config,
|
||||
"capabilities": server_config.get('capabilities', [])
|
||||
}
|
||||
))
|
||||
break # Stop after first match for this server
|
||||
except Exception as e:
|
||||
print(f"Warning: Error matching pattern for MCP server {server_name}: {e}")
|
||||
|
||||
# Add hybrid intelligence selection (Morphllm vs Serena)
|
||||
matches.extend(self._detect_hybrid_intelligence_selection(operation_data))
|
||||
|
||||
return matches
|
||||
|
||||
def _calculate_mcp_confidence(self, server_name: str, match: re.Match, context: Dict[str, Any], operation_data: Dict[str, Any]) -> float:
|
||||
"""Calculate confidence score for MCP server activation."""
|
||||
server_config = self.mcp_configs.get(server_name, {})
|
||||
base_confidence = server_config.get('confidence_threshold', 0.7)
|
||||
|
||||
# Server-specific confidence adjustments
|
||||
matched_text = match.group().lower()
|
||||
confidence_boost = 0.0
|
||||
|
||||
# Boost confidence based on operation context
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
complexity_score = operation_data.get('complexity_score', 0.0)
|
||||
|
||||
# Context-specific boosts
|
||||
if server_name == 'sequential' and (complexity_score > 0.6 or 'complex' in matched_text):
|
||||
confidence_boost += 0.15
|
||||
elif server_name == 'magic' and ('ui' in matched_text or 'component' in matched_text):
|
||||
confidence_boost += 0.1
|
||||
elif server_name == 'context7' and ('library' in matched_text or 'framework' in matched_text):
|
||||
confidence_boost += 0.1
|
||||
elif server_name == 'playwright' and ('test' in matched_text or 'automation' in matched_text):
|
||||
confidence_boost += 0.1
|
||||
|
||||
# Performance profile adjustments
|
||||
performance_profile = server_config.get('performance_profile', 'standard')
|
||||
if performance_profile == 'intensive' and complexity_score > 0.5:
|
||||
confidence_boost += 0.05
|
||||
elif performance_profile == 'lightweight' and file_count <= 3:
|
||||
confidence_boost += 0.05
|
||||
|
||||
return min(base_confidence + confidence_boost, 1.0)
|
||||
|
||||
def _detect_hybrid_intelligence_selection(self, operation_data: Dict[str, Any]) -> List[PatternMatch]:
|
||||
"""Detect whether to use Morphllm or Serena based on operation characteristics."""
|
||||
matches = []
|
||||
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
complexity_score = operation_data.get('complexity_score', 0.0)
|
||||
operation_types = operation_data.get('operation_types', [])
|
||||
|
||||
# Get hybrid intelligence configuration
|
||||
hybrid_config = self.mcp_patterns.get('hybrid_intelligence', {}).get('morphllm_vs_serena', {})
|
||||
|
||||
# Morphllm criteria
|
||||
morphllm_criteria = hybrid_config.get('morphllm_criteria', {})
|
||||
morphllm_file_max = morphllm_criteria.get('file_count_max', 10)
|
||||
morphllm_complexity_max = morphllm_criteria.get('complexity_max', 0.6)
|
||||
morphllm_ops = morphllm_criteria.get('preferred_operations', [])
|
||||
|
||||
# Serena criteria
|
||||
serena_criteria = hybrid_config.get('serena_criteria', {})
|
||||
serena_file_min = serena_criteria.get('file_count_min', 5)
|
||||
serena_complexity_min = serena_criteria.get('complexity_min', 0.4)
|
||||
serena_ops = serena_criteria.get('preferred_operations', [])
|
||||
|
||||
# Determine which system to use
|
||||
morphllm_score = 0
|
||||
serena_score = 0
|
||||
|
||||
# File count scoring
|
||||
if file_count <= morphllm_file_max:
|
||||
morphllm_score += 1
|
||||
if file_count >= serena_file_min:
|
||||
serena_score += 1
|
||||
|
||||
# Complexity scoring
|
||||
if complexity_score <= morphllm_complexity_max:
|
||||
morphllm_score += 1
|
||||
if complexity_score >= serena_complexity_min:
|
||||
serena_score += 1
|
||||
|
||||
# Operation type scoring
|
||||
for op_type in operation_types:
|
||||
if op_type in morphllm_ops:
|
||||
morphllm_score += 1
|
||||
if op_type in serena_ops:
|
||||
serena_score += 1
|
||||
|
||||
# Make selection based on scores
|
||||
if serena_score > morphllm_score:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.MCP_SERVER,
|
||||
pattern_name="serena",
|
||||
confidence=0.8 + (serena_score * 0.05),
|
||||
matched_text="hybrid_intelligence_selection",
|
||||
suggestions=["Use Serena for complex multi-file operations with semantic understanding"],
|
||||
metadata={
|
||||
"mcp_server": "serena",
|
||||
"selection_reason": "hybrid_intelligence",
|
||||
"file_count": file_count,
|
||||
"complexity_score": complexity_score,
|
||||
"score": serena_score
|
||||
}
|
||||
))
|
||||
elif morphllm_score > 0: # Only suggest Morphllm if it has some score
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.MCP_SERVER,
|
||||
pattern_name="morphllm",
|
||||
confidence=0.7 + (morphllm_score * 0.05),
|
||||
matched_text="hybrid_intelligence_selection",
|
||||
suggestions=["Use Morphllm for efficient editing operations with pattern optimization"],
|
||||
metadata={
|
||||
"mcp_server": "morphllm",
|
||||
"selection_reason": "hybrid_intelligence",
|
||||
"file_count": file_count,
|
||||
"complexity_score": complexity_score,
|
||||
"score": morphllm_score
|
||||
}
|
||||
))
|
||||
|
||||
return matches
|
||||
|
||||
def _detect_complexity_patterns(self, user_input: str, operation_data: Dict[str, Any]) -> List[PatternMatch]:
|
||||
"""Detect complexity indicators in the request with configurable thresholds."""
|
||||
matches = []
|
||||
|
||||
# Get complexity thresholds from configuration
|
||||
auto_activation = self.mcp_patterns.get('auto_activation', {})
|
||||
complexity_thresholds = auto_activation.get('complexity_thresholds', {})
|
||||
|
||||
# High complexity indicators from text patterns
|
||||
high_complexity_patterns = [
|
||||
(r"(?:entire|whole|complete)\s+(?:codebase|system|application)", 0.4),
|
||||
(r"(?:refactor|migrate|restructure)\s+(?:all|everything|entire)", 0.35),
|
||||
(r"(?:architecture|system-wide|comprehensive)\s+(?:change|update|redesign)", 0.3),
|
||||
(r"(?:complex|complicated|sophisticated)\s+(?:logic|algorithm|system)", 0.25)
|
||||
]
|
||||
|
||||
for pattern, score_boost in high_complexity_patterns:
|
||||
try:
|
||||
match = re.search(pattern, user_input, re.IGNORECASE)
|
||||
if match:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.COMPLEXITY_INDICATOR,
|
||||
pattern_name="high_complexity",
|
||||
confidence=0.8,
|
||||
matched_text=match.group(),
|
||||
suggestions=["Consider delegation and thinking modes"],
|
||||
metadata={"complexity_level": "high", "score_boost": score_boost}
|
||||
))
|
||||
break
|
||||
except re.error as e:
|
||||
print(f"Warning: Invalid complexity pattern: {e}")
|
||||
|
||||
# File count and operation complexity indicators
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
complexity_score = operation_data.get('complexity_score', 0.0)
|
||||
directory_count = operation_data.get('directory_count', 1)
|
||||
|
||||
# Multi-file operation detection with configurable thresholds
|
||||
delegation_threshold = complexity_thresholds.get('enable_delegation', {})
|
||||
file_threshold = delegation_threshold.get('file_count', 3)
|
||||
dir_threshold = delegation_threshold.get('directory_count', 2)
|
||||
complexity_threshold = delegation_threshold.get('complexity_score', 0.4)
|
||||
|
||||
if file_count >= file_threshold:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.COMPLEXITY_INDICATOR,
|
||||
pattern_name="multi_file_operation",
|
||||
confidence=0.9,
|
||||
matched_text=f"{file_count}_files",
|
||||
suggestions=[f"Enable delegation for {file_count}-file operations"],
|
||||
metadata={
|
||||
"file_count": file_count,
|
||||
"delegation_recommended": True,
|
||||
"threshold_met": "file_count"
|
||||
}
|
||||
))
|
||||
|
||||
if directory_count >= dir_threshold:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.COMPLEXITY_INDICATOR,
|
||||
pattern_name="multi_directory_operation",
|
||||
confidence=0.85,
|
||||
matched_text=f"{directory_count}_directories",
|
||||
suggestions=[f"Enable delegation for {directory_count}-directory operations"],
|
||||
metadata={
|
||||
"directory_count": directory_count,
|
||||
"delegation_recommended": True,
|
||||
"threshold_met": "directory_count"
|
||||
}
|
||||
))
|
||||
|
||||
if complexity_score >= complexity_threshold:
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.COMPLEXITY_INDICATOR,
|
||||
pattern_name="high_complexity_score",
|
||||
confidence=0.8 + min(complexity_score * 0.2, 0.2), # Cap boost at 0.2
|
||||
matched_text=f"complexity_{complexity_score:.2f}",
|
||||
suggestions=[f"Enable advanced processing for complexity score {complexity_score:.2f}"],
|
||||
metadata={
|
||||
"complexity_score": complexity_score,
|
||||
"sequential_recommended": complexity_score > 0.6,
|
||||
"threshold_met": "complexity_score"
|
||||
}
|
||||
))
|
||||
|
||||
return matches
|
||||
|
||||
def _detect_persona_patterns(self, user_input: str, context: Dict[str, Any]) -> List[PatternMatch]:
|
||||
"""Detect hints about which persona should be active with improved confidence calculation."""
|
||||
matches = []
|
||||
|
||||
# Enhanced persona patterns with confidence weighting
|
||||
persona_patterns = {
|
||||
"architect": {
|
||||
"patterns": [r"(?:architecture|design|structure|system)\s+(?:review|analysis|planning)"],
|
||||
"base_confidence": 0.75,
|
||||
"boost_words": ["architecture", "design", "structure", "system", "planning"]
|
||||
},
|
||||
"performance": {
|
||||
"patterns": [r"(?:performance|optimization|speed|efficiency|bottleneck)"],
|
||||
"base_confidence": 0.8,
|
||||
"boost_words": ["performance", "optimization", "speed", "efficiency", "bottleneck"]
|
||||
},
|
||||
"security": {
|
||||
"patterns": [r"(?:security|vulnerability|audit|secure|safety)"],
|
||||
"base_confidence": 0.85,
|
||||
"boost_words": ["security", "vulnerability", "audit", "secure", "safety"]
|
||||
},
|
||||
"frontend": {
|
||||
"patterns": [r"(?:ui|frontend|interface|component|design|responsive)"],
|
||||
"base_confidence": 0.75,
|
||||
"boost_words": ["ui", "frontend", "interface", "component", "responsive"]
|
||||
},
|
||||
"backend": {
|
||||
"patterns": [r"(?:api|server|database|backend|service)"],
|
||||
"base_confidence": 0.75,
|
||||
"boost_words": ["api", "server", "database", "backend", "service"]
|
||||
},
|
||||
"devops": {
|
||||
"patterns": [r"(?:deploy|deployment|ci|cd|infrastructure|docker|kubernetes)"],
|
||||
"base_confidence": 0.8,
|
||||
"boost_words": ["deploy", "deployment", "infrastructure", "docker", "kubernetes"]
|
||||
},
|
||||
"testing": {
|
||||
"patterns": [r"(?:test|testing|qa|quality|coverage|validation)"],
|
||||
"base_confidence": 0.75,
|
||||
"boost_words": ["test", "testing", "qa", "quality", "coverage", "validation"]
|
||||
}
|
||||
}
|
||||
|
||||
for persona, persona_config in persona_patterns.items():
|
||||
patterns = persona_config["patterns"]
|
||||
base_confidence = persona_config["base_confidence"]
|
||||
boost_words = persona_config["boost_words"]
|
||||
|
||||
for pattern in patterns:
|
||||
try:
|
||||
match = re.search(pattern, user_input, re.IGNORECASE)
|
||||
if match:
|
||||
# Calculate confidence with word-based boosting
|
||||
confidence = self._calculate_persona_confidence(
|
||||
base_confidence, match, boost_words, user_input
|
||||
)
|
||||
|
||||
matches.append(PatternMatch(
|
||||
pattern_type=PatternType.PERSONA_HINT,
|
||||
pattern_name=persona,
|
||||
confidence=confidence,
|
||||
matched_text=match.group(),
|
||||
suggestions=[f"Consider {persona} persona for specialized expertise"],
|
||||
metadata={
|
||||
"persona": persona,
|
||||
"domain_specific": True,
|
||||
"base_confidence": base_confidence,
|
||||
"calculated_confidence": confidence
|
||||
}
|
||||
))
|
||||
break # Stop after first match for this persona
|
||||
except re.error as e:
|
||||
print(f"Warning: Invalid persona pattern for {persona}: {e}")
|
||||
|
||||
return matches
|
||||
|
||||
def _calculate_persona_confidence(self, base_confidence: float, match: re.Match, boost_words: List[str], full_text: str) -> float:
|
||||
"""Calculate persona confidence with word-based boosting."""
|
||||
confidence = base_confidence
|
||||
matched_text = match.group().lower()
|
||||
full_text_lower = full_text.lower()
|
||||
|
||||
# Boost confidence based on additional domain words in the full text
|
||||
word_count = 0
|
||||
for word in boost_words:
|
||||
if word in full_text_lower:
|
||||
word_count += 1
|
||||
|
||||
# Add confidence boost based on domain word density
|
||||
confidence_boost = min(word_count * 0.05, 0.15) # Cap at 0.15
|
||||
|
||||
return min(confidence + confidence_boost, 1.0)
|
||||
|
||||
def _calculate_complexity_score(self, matches: List[PatternMatch], operation_data: Dict[str, Any]) -> float:
|
||||
"""Calculate overall complexity score from detected patterns with proper weighting."""
|
||||
try:
|
||||
base_score = operation_data.get('complexity_score', 0.0)
|
||||
|
||||
# Add complexity from pattern matches with proper validation
|
||||
complexity_boost = 0.0
|
||||
for match in matches:
|
||||
if match.pattern_type == PatternType.COMPLEXITY_INDICATOR:
|
||||
score_boost = match.metadata.get('score_boost', 0.1)
|
||||
if isinstance(score_boost, (int, float)) and 0 <= score_boost <= 1:
|
||||
complexity_boost += score_boost
|
||||
|
||||
# Weight the pattern-based boost to prevent over-scoring
|
||||
weighted_boost = complexity_boost * 0.7 # Reduce impact of pattern matches
|
||||
final_score = base_score + weighted_boost
|
||||
|
||||
return min(max(final_score, 0.0), 1.0) # Ensure score is between 0 and 1
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Error calculating complexity score: {e}")
|
||||
return operation_data.get('complexity_score', 0.0)
|
||||
|
||||
def _calculate_confidence_score(self, matches: List[PatternMatch]) -> float:
|
||||
"""Calculate overall confidence in pattern detection with improved weighting."""
|
||||
try:
|
||||
if not matches:
|
||||
return 0.0
|
||||
|
||||
# Weight different match types differently
|
||||
type_weights = {
|
||||
PatternType.MODE_TRIGGER: 0.3,
|
||||
PatternType.MCP_SERVER: 0.25,
|
||||
PatternType.COMPLEXITY_INDICATOR: 0.2,
|
||||
PatternType.PERSONA_HINT: 0.15,
|
||||
PatternType.PERFORMANCE_HINT: 0.1
|
||||
}
|
||||
|
||||
weighted_confidence = 0.0
|
||||
total_weight = 0.0
|
||||
|
||||
for match in matches:
|
||||
weight = type_weights.get(match.pattern_type, 0.1)
|
||||
weighted_confidence += match.confidence * weight
|
||||
total_weight += weight
|
||||
|
||||
if total_weight == 0:
|
||||
return 0.0
|
||||
|
||||
return min(weighted_confidence / total_weight, 1.0)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Error calculating confidence score: {e}")
|
||||
return 0.0
|
||||
|
||||
def _get_recommended_modes(self, matches: List[PatternMatch], complexity_score: float) -> List[str]:
|
||||
"""Get recommended modes based on detected patterns with configuration support."""
|
||||
modes = set()
|
||||
|
||||
try:
|
||||
# Add modes from pattern matches
|
||||
for match in matches:
|
||||
if match.pattern_type == PatternType.MODE_TRIGGER:
|
||||
# Only add if confidence threshold is met
|
||||
mode_config = self.mode_configs.get(match.pattern_name, {})
|
||||
threshold = mode_config.get('confidence_threshold', 0.7)
|
||||
if match.confidence >= threshold:
|
||||
modes.add(match.pattern_name)
|
||||
|
||||
# Auto-activate modes based on complexity with configurable thresholds
|
||||
auto_activation = self.mcp_patterns.get('auto_activation', {})
|
||||
complexity_thresholds = auto_activation.get('complexity_thresholds', {})
|
||||
|
||||
# Task management auto-activation
|
||||
task_mgmt_threshold = complexity_thresholds.get('enable_delegation', {}).get('complexity_score', 0.4)
|
||||
if complexity_score >= task_mgmt_threshold:
|
||||
modes.add("task_management")
|
||||
|
||||
# Sequential analysis auto-activation
|
||||
sequential_threshold = complexity_thresholds.get('enable_sequential', {}).get('complexity_score', 0.6)
|
||||
if complexity_score >= sequential_threshold:
|
||||
# Don't add sequential as a mode, but note it for MCP server selection
|
||||
pass
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Error getting recommended modes: {e}")
|
||||
|
||||
return list(modes)
|
||||
|
||||
def _get_recommended_mcp_servers(self, matches: List[PatternMatch], context: Dict[str, Any]) -> List[str]:
|
||||
"""Get recommended MCP servers based on detected patterns with priority handling."""
|
||||
servers = {} # Use dict to track server priorities
|
||||
|
||||
try:
|
||||
for match in matches:
|
||||
if match.pattern_type == PatternType.MCP_SERVER:
|
||||
server_config = match.metadata.get('server_config', {})
|
||||
priority = server_config.get('priority', 'medium')
|
||||
|
||||
# Assign numeric priority for sorting
|
||||
priority_value = {'high': 3, 'medium': 2, 'low': 1}.get(priority, 2)
|
||||
|
||||
if match.pattern_name not in servers or servers[match.pattern_name]['priority'] < priority_value:
|
||||
servers[match.pattern_name] = {
|
||||
'priority': priority_value,
|
||||
'confidence': match.confidence
|
||||
}
|
||||
|
||||
# Sort servers by priority, then by confidence
|
||||
sorted_servers = sorted(
|
||||
servers.items(),
|
||||
key=lambda x: (x[1]['priority'], x[1]['confidence']),
|
||||
reverse=True
|
||||
)
|
||||
|
||||
return [server[0] for server in sorted_servers]
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Error getting recommended MCP servers: {e}")
|
||||
return []
|
||||
|
||||
def _get_suggested_flags(self, matches: List[PatternMatch], complexity_score: float, context: Dict[str, Any]) -> List[str]:
|
||||
"""Get suggested flags based on patterns and complexity with configuration support."""
|
||||
flags = []
|
||||
|
||||
try:
|
||||
# Get auto-activation configuration
|
||||
auto_activation = self.mcp_patterns.get('auto_activation', {})
|
||||
complexity_thresholds = auto_activation.get('complexity_thresholds', {})
|
||||
|
||||
# Mode-specific flags
|
||||
mode_flags = {
|
||||
'brainstorming': ['--brainstorm'],
|
||||
'task_management': ['--delegate', '--wave-mode'],
|
||||
'token_efficiency': ['--uc'],
|
||||
'introspection': ['--introspect']
|
||||
}
|
||||
|
||||
# Add flags based on detected modes
|
||||
for match in matches:
|
||||
if match.pattern_type == PatternType.MODE_TRIGGER:
|
||||
mode_name = match.pattern_name
|
||||
if mode_name in mode_flags:
|
||||
flags.extend(mode_flags[mode_name])
|
||||
|
||||
# MCP server-specific flags (thinking flags based on server matches)
|
||||
mcp_thinking_flags = {
|
||||
'sequential': '--think',
|
||||
'serena': '--think-hard'
|
||||
}
|
||||
|
||||
# Add thinking flags based on MCP server matches
|
||||
for match in matches:
|
||||
if match.pattern_type == PatternType.MCP_SERVER:
|
||||
server_name = match.pattern_name
|
||||
if server_name in mcp_thinking_flags:
|
||||
thinking_flag = mcp_thinking_flags[server_name]
|
||||
if thinking_flag not in flags:
|
||||
flags.append(thinking_flag)
|
||||
|
||||
# Check for performance analysis patterns (special case for think-hard)
|
||||
for match in matches:
|
||||
if match.pattern_type == PatternType.MCP_SERVER and match.pattern_name == 'sequential':
|
||||
matched_text = match.matched_text.lower()
|
||||
if 'performance' in matched_text or 'bottleneck' in matched_text or 'bundle' in matched_text:
|
||||
# Replace --think with --think-hard for performance analysis
|
||||
if '--think' in flags:
|
||||
flags.remove('--think')
|
||||
if '--think-hard' not in flags:
|
||||
flags.append('--think-hard')
|
||||
|
||||
# Thinking flags based on complexity with configurable thresholds
|
||||
sequential_config = complexity_thresholds.get('enable_sequential', {})
|
||||
sequential_threshold = sequential_config.get('complexity_score', 0.6)
|
||||
|
||||
# Only add complexity-based thinking flags if no MCP-based flags were added
|
||||
if not any(flag.startswith('--think') for flag in flags):
|
||||
if complexity_score >= 0.8:
|
||||
flags.append("--ultrathink")
|
||||
elif complexity_score >= sequential_threshold:
|
||||
flags.append("--think-hard")
|
||||
elif complexity_score >= 0.3:
|
||||
flags.append("--think")
|
||||
|
||||
# Delegation flags from pattern matches
|
||||
delegation_recommended = False
|
||||
for match in matches:
|
||||
if match.metadata.get("delegation_recommended"):
|
||||
delegation_recommended = True
|
||||
break
|
||||
|
||||
if delegation_recommended and "--delegate" not in flags:
|
||||
flags.append("--delegate")
|
||||
|
||||
# Efficiency flags based on patterns and context
|
||||
efficiency_needed = False
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
|
||||
for match in matches:
|
||||
if match.metadata.get("compression_needed"):
|
||||
efficiency_needed = True
|
||||
break
|
||||
|
||||
# Check resource thresholds from configuration
|
||||
resource_mgmt = self.mcp_patterns.get('performance_optimization', {}).get('resource_management', {})
|
||||
token_threshold = resource_mgmt.get('token_threshold_percent', 75)
|
||||
|
||||
if (efficiency_needed or resource_usage > token_threshold) and "--uc" not in flags:
|
||||
flags.append("--uc")
|
||||
|
||||
# Validation flags for high-risk operations
|
||||
validation_config = complexity_thresholds.get('enable_validation', {})
|
||||
is_production = context.get('is_production', False)
|
||||
risk_level = context.get('risk_level', 'low')
|
||||
|
||||
validation_needed = (
|
||||
complexity_score > 0.7 or
|
||||
is_production or
|
||||
risk_level in validation_config.get('risk_level', ['high', 'critical'])
|
||||
)
|
||||
|
||||
if validation_needed:
|
||||
flags.append("--validate")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Warning: Error getting suggested flags: {e}")
|
||||
|
||||
return flags
|
||||
|
||||
def validate_input(self, user_input: str, context: Dict[str, Any], operation_data: Dict[str, Any]) -> Tuple[bool, str]:
|
||||
"""Validate input parameters for pattern detection."""
|
||||
try:
|
||||
# Validate user_input
|
||||
if not isinstance(user_input, str):
|
||||
return False, "user_input must be a string"
|
||||
|
||||
if len(user_input.strip()) == 0:
|
||||
return False, "user_input cannot be empty"
|
||||
|
||||
# Validate context
|
||||
if not isinstance(context, dict):
|
||||
return False, "context must be a dictionary"
|
||||
|
||||
# Validate operation_data
|
||||
if not isinstance(operation_data, dict):
|
||||
return False, "operation_data must be a dictionary"
|
||||
|
||||
# Validate numeric values in context
|
||||
resource_usage = context.get('resource_usage_percent', 0)
|
||||
if not isinstance(resource_usage, (int, float)) or not (0 <= resource_usage <= 100):
|
||||
context['resource_usage_percent'] = 0
|
||||
|
||||
# Validate numeric values in operation_data
|
||||
file_count = operation_data.get('file_count', 1)
|
||||
if not isinstance(file_count, int) or file_count < 0:
|
||||
operation_data['file_count'] = 1
|
||||
|
||||
complexity_score = operation_data.get('complexity_score', 0.0)
|
||||
if not isinstance(complexity_score, (int, float)) or not (0 <= complexity_score <= 1):
|
||||
operation_data['complexity_score'] = 0.0
|
||||
|
||||
return True, "Input validation passed"
|
||||
|
||||
except Exception as e:
|
||||
return False, f"Input validation error: {e}"
|
||||
|
||||
def get_pattern_statistics(self) -> Dict[str, Any]:
|
||||
"""Get statistics about compiled patterns for debugging."""
|
||||
try:
|
||||
stats = {
|
||||
'total_patterns': len(self.compiled_patterns),
|
||||
'mode_patterns': len([k for k in self.compiled_patterns.keys() if k.startswith('mode_')]),
|
||||
'mcp_patterns': len([k for k in self.compiled_patterns.keys() if k.startswith('mcp_')]),
|
||||
'mode_configs_loaded': len(self.mode_configs),
|
||||
'mcp_configs_loaded': len(self.mcp_configs),
|
||||
'pattern_details': {}
|
||||
}
|
||||
|
||||
# Detailed pattern statistics
|
||||
for pattern_key, compiled_patterns in self.compiled_patterns.items():
|
||||
stats['pattern_details'][pattern_key] = {
|
||||
'pattern_count': len(compiled_patterns),
|
||||
'patterns': [p.pattern for p in compiled_patterns]
|
||||
}
|
||||
|
||||
return stats
|
||||
|
||||
except Exception as e:
|
||||
return {'error': f"Failed to generate pattern statistics: {e}"}
|
||||
|
||||
def reset_patterns(self):
|
||||
"""Reset and reload all patterns from configuration."""
|
||||
try:
|
||||
self.__init__()
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"Warning: Failed to reset patterns: {e}")
|
||||
return False
|
||||
@@ -1,763 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
YAML-Driven System Validation Engine for SuperClaude Framework-Hooks
|
||||
|
||||
Intelligent validation system that consumes declarative YAML patterns from
|
||||
validation_intelligence.yaml for health scoring, proactive diagnostics, and
|
||||
predictive analysis.
|
||||
|
||||
Features:
|
||||
- YAML-driven validation patterns (hot-reloadable)
|
||||
- Health scoring with weighted components
|
||||
- Proactive diagnostic pattern matching
|
||||
- Predictive health analysis
|
||||
- Automated remediation suggestions
|
||||
- Continuous validation cycles
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import time
|
||||
import statistics
|
||||
import sys
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Tuple, Optional
|
||||
from dataclasses import dataclass, asdict
|
||||
from enum import Enum
|
||||
|
||||
# Import our YAML intelligence infrastructure
|
||||
from yaml_loader import config_loader
|
||||
from intelligence_engine import IntelligenceEngine
|
||||
|
||||
|
||||
class ValidationSeverity(Enum):
|
||||
"""Validation issue severity levels."""
|
||||
INFO = "info"
|
||||
LOW = "low"
|
||||
MEDIUM = "medium"
|
||||
HIGH = "high"
|
||||
CRITICAL = "critical"
|
||||
|
||||
|
||||
class HealthStatus(Enum):
|
||||
"""System health status levels."""
|
||||
HEALTHY = "healthy"
|
||||
WARNING = "warning"
|
||||
CRITICAL = "critical"
|
||||
UNKNOWN = "unknown"
|
||||
|
||||
|
||||
@dataclass
|
||||
class ValidationIssue:
|
||||
"""Represents a validation issue found by the system."""
|
||||
component: str
|
||||
issue_type: str
|
||||
severity: ValidationSeverity
|
||||
description: str
|
||||
evidence: List[str]
|
||||
recommendations: List[str]
|
||||
remediation_action: Optional[str] = None
|
||||
auto_fixable: bool = False
|
||||
timestamp: float = 0.0
|
||||
|
||||
def __post_init__(self):
|
||||
if self.timestamp == 0.0:
|
||||
self.timestamp = time.time()
|
||||
|
||||
|
||||
@dataclass
|
||||
class HealthScore:
|
||||
"""Health score for a system component."""
|
||||
component: str
|
||||
score: float # 0.0 to 1.0
|
||||
status: HealthStatus
|
||||
contributing_factors: List[str]
|
||||
trend: str # improving, stable, degrading
|
||||
last_updated: float = 0.0
|
||||
|
||||
def __post_init__(self):
|
||||
if self.last_updated == 0.0:
|
||||
self.last_updated = time.time()
|
||||
|
||||
|
||||
@dataclass
|
||||
class DiagnosticResult:
|
||||
"""Result of diagnostic analysis."""
|
||||
component: str
|
||||
diagnosis: str
|
||||
confidence: float
|
||||
symptoms: List[str]
|
||||
root_cause: Optional[str]
|
||||
recommendations: List[str]
|
||||
predicted_impact: str
|
||||
timeline: str
|
||||
|
||||
|
||||
class YAMLValidationEngine:
|
||||
"""
|
||||
YAML-driven validation engine that consumes intelligence patterns.
|
||||
|
||||
Features:
|
||||
- Hot-reloadable YAML validation patterns
|
||||
- Component-based health scoring
|
||||
- Proactive diagnostic pattern matching
|
||||
- Predictive health analysis
|
||||
- Intelligent remediation suggestions
|
||||
"""
|
||||
|
||||
def __init__(self, framework_root: Path, fix_issues: bool = False):
|
||||
self.framework_root = Path(framework_root)
|
||||
self.fix_issues = fix_issues
|
||||
self.cache_dir = self.framework_root / "cache"
|
||||
self.config_dir = self.framework_root / "config"
|
||||
|
||||
# Initialize intelligence engine for YAML patterns
|
||||
self.intelligence_engine = IntelligenceEngine()
|
||||
|
||||
# Validation state
|
||||
self.issues: List[ValidationIssue] = []
|
||||
self.fixes_applied: List[str] = []
|
||||
self.health_scores: Dict[str, HealthScore] = {}
|
||||
self.diagnostic_results: List[DiagnosticResult] = []
|
||||
|
||||
# Load validation intelligence patterns
|
||||
self.validation_patterns = self._load_validation_patterns()
|
||||
|
||||
def _load_validation_patterns(self) -> Dict[str, Any]:
|
||||
"""Load validation patterns from YAML intelligence configuration."""
|
||||
try:
|
||||
patterns = config_loader.get_validation_health_config()
|
||||
return patterns if patterns else {}
|
||||
except Exception as e:
|
||||
print(f"Warning: Could not load validation patterns: {e}")
|
||||
return {}
|
||||
|
||||
def validate_all(self) -> Tuple[List[ValidationIssue], List[str], Dict[str, HealthScore]]:
|
||||
"""
|
||||
Run comprehensive YAML-driven validation.
|
||||
|
||||
Returns:
|
||||
Tuple of (issues, fixes_applied, health_scores)
|
||||
"""
|
||||
print("🔍 Starting YAML-driven framework validation...")
|
||||
|
||||
# Clear previous state
|
||||
self.issues.clear()
|
||||
self.fixes_applied.clear()
|
||||
self.health_scores.clear()
|
||||
self.diagnostic_results.clear()
|
||||
|
||||
# Get current system context
|
||||
context = self._gather_system_context()
|
||||
|
||||
# Run validation intelligence analysis
|
||||
validation_intelligence = self.intelligence_engine.evaluate_context(
|
||||
context, 'validation_intelligence'
|
||||
)
|
||||
|
||||
# Core component validations using YAML patterns
|
||||
self._validate_learning_system(context, validation_intelligence)
|
||||
self._validate_performance_system(context, validation_intelligence)
|
||||
self._validate_mcp_coordination(context, validation_intelligence)
|
||||
self._validate_hook_system(context, validation_intelligence)
|
||||
self._validate_configuration_system(context, validation_intelligence)
|
||||
self._validate_cache_system(context, validation_intelligence)
|
||||
|
||||
# Run proactive diagnostics
|
||||
self._run_proactive_diagnostics(context)
|
||||
|
||||
# Calculate overall health score
|
||||
self._calculate_overall_health_score()
|
||||
|
||||
# Generate remediation recommendations
|
||||
self._generate_remediation_suggestions()
|
||||
|
||||
return self.issues, self.fixes_applied, self.health_scores
|
||||
|
||||
def _gather_system_context(self) -> Dict[str, Any]:
|
||||
"""Gather current system context for validation analysis."""
|
||||
context = {
|
||||
'timestamp': time.time(),
|
||||
'framework_root': str(self.framework_root),
|
||||
'cache_directory_exists': self.cache_dir.exists(),
|
||||
'config_directory_exists': self.config_dir.exists(),
|
||||
}
|
||||
|
||||
# Learning system context
|
||||
learning_records_path = self.cache_dir / "learning_records.json"
|
||||
if learning_records_path.exists():
|
||||
try:
|
||||
with open(learning_records_path, 'r') as f:
|
||||
records = json.load(f)
|
||||
context['learning_records_count'] = len(records)
|
||||
if records:
|
||||
context['recent_learning_activity'] = len([
|
||||
r for r in records
|
||||
if r.get('timestamp', 0) > time.time() - 86400 # Last 24h
|
||||
])
|
||||
except:
|
||||
context['learning_records_count'] = 0
|
||||
context['recent_learning_activity'] = 0
|
||||
|
||||
# Adaptations context
|
||||
adaptations_path = self.cache_dir / "adaptations.json"
|
||||
if adaptations_path.exists():
|
||||
try:
|
||||
with open(adaptations_path, 'r') as f:
|
||||
adaptations = json.load(f)
|
||||
context['adaptations_count'] = len(adaptations)
|
||||
|
||||
# Calculate effectiveness statistics
|
||||
all_effectiveness = []
|
||||
for adaptation in adaptations.values():
|
||||
history = adaptation.get('effectiveness_history', [])
|
||||
all_effectiveness.extend(history)
|
||||
|
||||
if all_effectiveness:
|
||||
context['average_effectiveness'] = statistics.mean(all_effectiveness)
|
||||
context['effectiveness_variance'] = statistics.variance(all_effectiveness) if len(all_effectiveness) > 1 else 0
|
||||
context['perfect_score_count'] = sum(1 for score in all_effectiveness if score == 1.0)
|
||||
except:
|
||||
context['adaptations_count'] = 0
|
||||
|
||||
# Configuration files context
|
||||
yaml_files = list(self.config_dir.glob("*.yaml")) if self.config_dir.exists() else []
|
||||
context['yaml_config_count'] = len(yaml_files)
|
||||
context['intelligence_patterns_available'] = len([
|
||||
f for f in yaml_files
|
||||
if f.name in ['intelligence_patterns.yaml', 'mcp_orchestration.yaml',
|
||||
'hook_coordination.yaml', 'performance_intelligence.yaml',
|
||||
'validation_intelligence.yaml', 'user_experience.yaml']
|
||||
])
|
||||
|
||||
return context
|
||||
|
||||
def _validate_learning_system(self, context: Dict[str, Any], intelligence: Dict[str, Any]):
|
||||
"""Validate learning system using YAML patterns."""
|
||||
print("📊 Validating learning system...")
|
||||
|
||||
component_weight = self.validation_patterns.get('component_weights', {}).get('learning_system', 0.25)
|
||||
scoring_metrics = self.validation_patterns.get('scoring_metrics', {}).get('learning_system', {})
|
||||
|
||||
issues = []
|
||||
score_factors = []
|
||||
|
||||
# Pattern diversity validation
|
||||
adaptations_count = context.get('adaptations_count', 0)
|
||||
if adaptations_count > 0:
|
||||
# Simplified diversity calculation
|
||||
diversity_score = min(adaptations_count / 50.0, 0.95) # Cap at 0.95
|
||||
pattern_diversity_config = scoring_metrics.get('pattern_diversity', {})
|
||||
healthy_range = pattern_diversity_config.get('healthy_range', [0.6, 0.95])
|
||||
|
||||
if diversity_score < healthy_range[0]:
|
||||
issues.append(ValidationIssue(
|
||||
component="learning_system",
|
||||
issue_type="pattern_diversity",
|
||||
severity=ValidationSeverity.MEDIUM,
|
||||
description=f"Pattern diversity low: {diversity_score:.2f}",
|
||||
evidence=[f"Only {adaptations_count} unique patterns learned"],
|
||||
recommendations=["Expose system to more diverse operational patterns"]
|
||||
))
|
||||
score_factors.append(diversity_score)
|
||||
|
||||
# Effectiveness consistency validation
|
||||
effectiveness_variance = context.get('effectiveness_variance', 0)
|
||||
if effectiveness_variance is not None:
|
||||
consistency_score = max(0, 1.0 - effectiveness_variance)
|
||||
effectiveness_config = scoring_metrics.get('effectiveness_consistency', {})
|
||||
healthy_range = effectiveness_config.get('healthy_range', [0.7, 0.9])
|
||||
|
||||
if consistency_score < healthy_range[0]:
|
||||
issues.append(ValidationIssue(
|
||||
component="learning_system",
|
||||
issue_type="effectiveness_consistency",
|
||||
severity=ValidationSeverity.LOW,
|
||||
description=f"Effectiveness variance high: {effectiveness_variance:.3f}",
|
||||
evidence=[f"Effectiveness consistency score: {consistency_score:.2f}"],
|
||||
recommendations=["Review learning patterns for instability"]
|
||||
))
|
||||
score_factors.append(consistency_score)
|
||||
|
||||
# Perfect score detection (overfitting indicator)
|
||||
perfect_scores = context.get('perfect_score_count', 0)
|
||||
total_effectiveness_records = context.get('adaptations_count', 0) * 3 # Rough estimate
|
||||
if total_effectiveness_records > 0 and perfect_scores / total_effectiveness_records > 0.3:
|
||||
issues.append(ValidationIssue(
|
||||
component="learning_system",
|
||||
issue_type="potential_overfitting",
|
||||
severity=ValidationSeverity.MEDIUM,
|
||||
description=f"High proportion of perfect scores: {perfect_scores}/{total_effectiveness_records}",
|
||||
evidence=[f"Perfect score ratio: {perfect_scores/total_effectiveness_records:.1%}"],
|
||||
recommendations=[
|
||||
"Review learning patterns for overfitting",
|
||||
"Add noise to prevent overconfident patterns"
|
||||
],
|
||||
remediation_action="automatic_pattern_diversification"
|
||||
))
|
||||
|
||||
# Calculate health score
|
||||
component_health = statistics.mean(score_factors) if score_factors else 0.5
|
||||
health_status = (
|
||||
HealthStatus.HEALTHY if component_health >= 0.8 else
|
||||
HealthStatus.WARNING if component_health >= 0.6 else
|
||||
HealthStatus.CRITICAL
|
||||
)
|
||||
|
||||
self.health_scores['learning_system'] = HealthScore(
|
||||
component='learning_system',
|
||||
score=component_health,
|
||||
status=health_status,
|
||||
contributing_factors=[f"pattern_diversity", "effectiveness_consistency"],
|
||||
trend="stable" # Would need historical data to determine trend
|
||||
)
|
||||
|
||||
self.issues.extend(issues)
|
||||
|
||||
def _validate_performance_system(self, context: Dict[str, Any], intelligence: Dict[str, Any]):
|
||||
"""Validate performance system using YAML patterns."""
|
||||
print("⚡ Validating performance system...")
|
||||
|
||||
# This would integrate with actual performance metrics
|
||||
# For now, provide basic validation based on available data
|
||||
|
||||
issues = []
|
||||
score_factors = []
|
||||
|
||||
# Check for performance-related files and configurations
|
||||
perf_score = 0.8 # Default assuming healthy
|
||||
|
||||
# Cache size validation (proxy for memory efficiency)
|
||||
if self.cache_dir.exists():
|
||||
cache_size = sum(f.stat().st_size for f in self.cache_dir.rglob('*') if f.is_file())
|
||||
cache_size_mb = cache_size / (1024 * 1024)
|
||||
|
||||
if cache_size_mb > 10: # > 10MB cache
|
||||
issues.append(ValidationIssue(
|
||||
component="performance_system",
|
||||
issue_type="cache_size_large",
|
||||
severity=ValidationSeverity.LOW,
|
||||
description=f"Cache size is large: {cache_size_mb:.1f}MB",
|
||||
evidence=[f"Total cache size: {cache_size_mb:.1f}MB"],
|
||||
recommendations=["Consider cache cleanup policies"],
|
||||
remediation_action="aggressive_cache_cleanup"
|
||||
))
|
||||
perf_score -= 0.1
|
||||
|
||||
score_factors.append(perf_score)
|
||||
|
||||
self.health_scores['performance_system'] = HealthScore(
|
||||
component='performance_system',
|
||||
score=statistics.mean(score_factors) if score_factors else 0.8,
|
||||
status=HealthStatus.HEALTHY,
|
||||
contributing_factors=["cache_efficiency", "resource_utilization"],
|
||||
trend="stable"
|
||||
)
|
||||
|
||||
self.issues.extend(issues)
|
||||
|
||||
def _validate_mcp_coordination(self, context: Dict[str, Any], intelligence: Dict[str, Any]):
|
||||
"""Validate MCP coordination system using YAML patterns."""
|
||||
print("🔗 Validating MCP coordination...")
|
||||
|
||||
issues = []
|
||||
score = 0.8 # Default healthy score
|
||||
|
||||
# Check MCP orchestration patterns availability
|
||||
mcp_patterns_available = 'mcp_orchestration.yaml' in [
|
||||
f.name for f in self.config_dir.glob("*.yaml")
|
||||
] if self.config_dir.exists() else False
|
||||
|
||||
if not mcp_patterns_available:
|
||||
issues.append(ValidationIssue(
|
||||
component="mcp_coordination",
|
||||
issue_type="missing_orchestration_patterns",
|
||||
severity=ValidationSeverity.MEDIUM,
|
||||
description="MCP orchestration patterns not available",
|
||||
evidence=["mcp_orchestration.yaml not found"],
|
||||
recommendations=["Ensure MCP orchestration patterns are configured"]
|
||||
))
|
||||
score -= 0.2
|
||||
|
||||
self.health_scores['mcp_coordination'] = HealthScore(
|
||||
component='mcp_coordination',
|
||||
score=score,
|
||||
status=HealthStatus.HEALTHY if score >= 0.8 else HealthStatus.WARNING,
|
||||
contributing_factors=["pattern_availability", "server_selection_accuracy"],
|
||||
trend="stable"
|
||||
)
|
||||
|
||||
self.issues.extend(issues)
|
||||
|
||||
def _validate_hook_system(self, context: Dict[str, Any], intelligence: Dict[str, Any]):
|
||||
"""Validate hook system using YAML patterns."""
|
||||
print("🎣 Validating hook system...")
|
||||
|
||||
issues = []
|
||||
score = 0.8
|
||||
|
||||
# Check hook coordination patterns
|
||||
hook_patterns_available = 'hook_coordination.yaml' in [
|
||||
f.name for f in self.config_dir.glob("*.yaml")
|
||||
] if self.config_dir.exists() else False
|
||||
|
||||
if not hook_patterns_available:
|
||||
issues.append(ValidationIssue(
|
||||
component="hook_system",
|
||||
issue_type="missing_coordination_patterns",
|
||||
severity=ValidationSeverity.MEDIUM,
|
||||
description="Hook coordination patterns not available",
|
||||
evidence=["hook_coordination.yaml not found"],
|
||||
recommendations=["Ensure hook coordination patterns are configured"]
|
||||
))
|
||||
score -= 0.2
|
||||
|
||||
self.health_scores['hook_system'] = HealthScore(
|
||||
component='hook_system',
|
||||
score=score,
|
||||
status=HealthStatus.HEALTHY if score >= 0.8 else HealthStatus.WARNING,
|
||||
contributing_factors=["coordination_patterns", "execution_efficiency"],
|
||||
trend="stable"
|
||||
)
|
||||
|
||||
self.issues.extend(issues)
|
||||
|
||||
def _validate_configuration_system(self, context: Dict[str, Any], intelligence: Dict[str, Any]):
|
||||
"""Validate configuration system using YAML patterns."""
|
||||
print("📝 Validating configuration system...")
|
||||
|
||||
issues = []
|
||||
score_factors = []
|
||||
|
||||
# Check YAML configuration files
|
||||
expected_intelligence_files = [
|
||||
'intelligence_patterns.yaml',
|
||||
'mcp_orchestration.yaml',
|
||||
'hook_coordination.yaml',
|
||||
'performance_intelligence.yaml',
|
||||
'validation_intelligence.yaml',
|
||||
'user_experience.yaml'
|
||||
]
|
||||
|
||||
available_files = [f.name for f in self.config_dir.glob("*.yaml")] if self.config_dir.exists() else []
|
||||
missing_files = [f for f in expected_intelligence_files if f not in available_files]
|
||||
|
||||
if missing_files:
|
||||
issues.append(ValidationIssue(
|
||||
component="configuration_system",
|
||||
issue_type="missing_intelligence_configs",
|
||||
severity=ValidationSeverity.HIGH,
|
||||
description=f"Missing {len(missing_files)} intelligence configuration files",
|
||||
evidence=[f"Missing files: {', '.join(missing_files)}"],
|
||||
recommendations=["Ensure all intelligence pattern files are available"]
|
||||
))
|
||||
score_factors.append(0.5)
|
||||
else:
|
||||
score_factors.append(0.9)
|
||||
|
||||
# Validate YAML syntax
|
||||
yaml_issues = 0
|
||||
if self.config_dir.exists():
|
||||
for yaml_file in self.config_dir.glob("*.yaml"):
|
||||
try:
|
||||
with open(yaml_file, 'r') as f:
|
||||
config_loader.load_config(yaml_file.stem)
|
||||
except Exception as e:
|
||||
yaml_issues += 1
|
||||
issues.append(ValidationIssue(
|
||||
component="configuration_system",
|
||||
issue_type="yaml_syntax_error",
|
||||
severity=ValidationSeverity.HIGH,
|
||||
description=f"YAML syntax error in {yaml_file.name}",
|
||||
evidence=[f"Error: {str(e)}"],
|
||||
recommendations=[f"Fix YAML syntax in {yaml_file.name}"]
|
||||
))
|
||||
|
||||
syntax_score = max(0, 1.0 - yaml_issues * 0.2)
|
||||
score_factors.append(syntax_score)
|
||||
|
||||
overall_score = statistics.mean(score_factors) if score_factors else 0.5
|
||||
|
||||
self.health_scores['configuration_system'] = HealthScore(
|
||||
component='configuration_system',
|
||||
score=overall_score,
|
||||
status=HealthStatus.HEALTHY if overall_score >= 0.8 else
|
||||
HealthStatus.WARNING if overall_score >= 0.6 else
|
||||
HealthStatus.CRITICAL,
|
||||
contributing_factors=["file_availability", "yaml_syntax", "intelligence_patterns"],
|
||||
trend="stable"
|
||||
)
|
||||
|
||||
self.issues.extend(issues)
|
||||
|
||||
def _validate_cache_system(self, context: Dict[str, Any], intelligence: Dict[str, Any]):
|
||||
"""Validate cache system using YAML patterns."""
|
||||
print("💾 Validating cache system...")
|
||||
|
||||
issues = []
|
||||
score = 0.8
|
||||
|
||||
if not self.cache_dir.exists():
|
||||
issues.append(ValidationIssue(
|
||||
component="cache_system",
|
||||
issue_type="cache_directory_missing",
|
||||
severity=ValidationSeverity.HIGH,
|
||||
description="Cache directory does not exist",
|
||||
evidence=[f"Path not found: {self.cache_dir}"],
|
||||
recommendations=["Initialize cache directory"],
|
||||
auto_fixable=True,
|
||||
remediation_action="create_cache_directory"
|
||||
))
|
||||
score = 0.3
|
||||
else:
|
||||
# Validate essential cache files
|
||||
essential_files = ['learning_records.json', 'adaptations.json']
|
||||
missing_essential = []
|
||||
|
||||
for essential_file in essential_files:
|
||||
file_path = self.cache_dir / essential_file
|
||||
if not file_path.exists():
|
||||
missing_essential.append(essential_file)
|
||||
|
||||
if missing_essential:
|
||||
issues.append(ValidationIssue(
|
||||
component="cache_system",
|
||||
issue_type="missing_essential_cache_files",
|
||||
severity=ValidationSeverity.MEDIUM,
|
||||
description=f"Missing essential cache files: {', '.join(missing_essential)}",
|
||||
evidence=[f"Missing files in {self.cache_dir}"],
|
||||
recommendations=["Initialize missing cache files"],
|
||||
auto_fixable=True
|
||||
))
|
||||
score -= 0.1 * len(missing_essential)
|
||||
|
||||
self.health_scores['cache_system'] = HealthScore(
|
||||
component='cache_system',
|
||||
score=score,
|
||||
status=HealthStatus.HEALTHY if score >= 0.8 else
|
||||
HealthStatus.WARNING if score >= 0.6 else
|
||||
HealthStatus.CRITICAL,
|
||||
contributing_factors=["directory_existence", "essential_files"],
|
||||
trend="stable"
|
||||
)
|
||||
|
||||
self.issues.extend(issues)
|
||||
|
||||
def _run_proactive_diagnostics(self, context: Dict[str, Any]):
|
||||
"""Run proactive diagnostic pattern matching from YAML."""
|
||||
print("🔮 Running proactive diagnostics...")
|
||||
|
||||
# Get early warning patterns from YAML
|
||||
early_warning_patterns = self.validation_patterns.get(
|
||||
'proactive_diagnostics', {}
|
||||
).get('early_warning_patterns', {})
|
||||
|
||||
# Check learning system warnings
|
||||
learning_warnings = early_warning_patterns.get('learning_system_warnings', [])
|
||||
for warning_pattern in learning_warnings:
|
||||
if self._matches_warning_pattern(context, warning_pattern):
|
||||
severity_map = {
|
||||
'low': ValidationSeverity.LOW,
|
||||
'medium': ValidationSeverity.MEDIUM,
|
||||
'high': ValidationSeverity.HIGH,
|
||||
'critical': ValidationSeverity.CRITICAL
|
||||
}
|
||||
|
||||
self.issues.append(ValidationIssue(
|
||||
component="learning_system",
|
||||
issue_type=warning_pattern.get('name', 'unknown_warning'),
|
||||
severity=severity_map.get(warning_pattern.get('severity', 'medium'), ValidationSeverity.MEDIUM),
|
||||
description=f"Proactive warning: {warning_pattern.get('name')}",
|
||||
evidence=[f"Pattern matched: {warning_pattern.get('pattern', {})}"],
|
||||
recommendations=[warning_pattern.get('recommendation', 'Review system state')],
|
||||
remediation_action=warning_pattern.get('remediation')
|
||||
))
|
||||
|
||||
# Similar checks for performance and coordination warnings would go here
|
||||
|
||||
def _matches_warning_pattern(self, context: Dict[str, Any], warning_pattern: Dict[str, Any]) -> bool:
|
||||
"""Check if current context matches a warning pattern."""
|
||||
pattern_conditions = warning_pattern.get('pattern', {})
|
||||
|
||||
for key, expected_value in pattern_conditions.items():
|
||||
if key not in context:
|
||||
continue
|
||||
|
||||
context_value = context[key]
|
||||
|
||||
# Handle string comparisons with operators
|
||||
if isinstance(expected_value, str):
|
||||
if expected_value.startswith('>'):
|
||||
threshold = float(expected_value[1:])
|
||||
if not (isinstance(context_value, (int, float)) and context_value > threshold):
|
||||
return False
|
||||
elif expected_value.startswith('<'):
|
||||
threshold = float(expected_value[1:])
|
||||
if not (isinstance(context_value, (int, float)) and context_value < threshold):
|
||||
return False
|
||||
else:
|
||||
if context_value != expected_value:
|
||||
return False
|
||||
else:
|
||||
if context_value != expected_value:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _calculate_overall_health_score(self):
|
||||
"""Calculate overall system health score using YAML component weights."""
|
||||
component_weights = self.validation_patterns.get('component_weights', {
|
||||
'learning_system': 0.25,
|
||||
'performance_system': 0.20,
|
||||
'mcp_coordination': 0.20,
|
||||
'hook_system': 0.15,
|
||||
'configuration_system': 0.10,
|
||||
'cache_system': 0.10
|
||||
})
|
||||
|
||||
weighted_score = 0.0
|
||||
total_weight = 0.0
|
||||
|
||||
for component, weight in component_weights.items():
|
||||
if component in self.health_scores:
|
||||
weighted_score += self.health_scores[component].score * weight
|
||||
total_weight += weight
|
||||
|
||||
overall_score = weighted_score / total_weight if total_weight > 0 else 0.0
|
||||
|
||||
overall_status = (
|
||||
HealthStatus.HEALTHY if overall_score >= 0.8 else
|
||||
HealthStatus.WARNING if overall_score >= 0.6 else
|
||||
HealthStatus.CRITICAL
|
||||
)
|
||||
|
||||
self.health_scores['overall'] = HealthScore(
|
||||
component='overall_system',
|
||||
score=overall_score,
|
||||
status=overall_status,
|
||||
contributing_factors=list(component_weights.keys()),
|
||||
trend="stable"
|
||||
)
|
||||
|
||||
def _generate_remediation_suggestions(self):
|
||||
"""Generate intelligent remediation suggestions based on issues found."""
|
||||
auto_fixable_issues = [issue for issue in self.issues if issue.auto_fixable]
|
||||
|
||||
if auto_fixable_issues and self.fix_issues:
|
||||
for issue in auto_fixable_issues:
|
||||
if issue.remediation_action == "create_cache_directory":
|
||||
try:
|
||||
self.cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.fixes_applied.append(f"✅ Created cache directory: {self.cache_dir}")
|
||||
except Exception as e:
|
||||
print(f"Failed to create cache directory: {e}")
|
||||
|
||||
def print_results(self, verbose: bool = False):
|
||||
"""Print comprehensive validation results."""
|
||||
print("\n" + "="*70)
|
||||
print("🎯 YAML-DRIVEN VALIDATION RESULTS")
|
||||
print("="*70)
|
||||
|
||||
# Overall health score
|
||||
overall_health = self.health_scores.get('overall')
|
||||
if overall_health:
|
||||
status_emoji = {
|
||||
HealthStatus.HEALTHY: "🟢",
|
||||
HealthStatus.WARNING: "🟡",
|
||||
HealthStatus.CRITICAL: "🔴",
|
||||
HealthStatus.UNKNOWN: "⚪"
|
||||
}
|
||||
print(f"\n{status_emoji.get(overall_health.status, '⚪')} Overall Health Score: {overall_health.score:.2f}/1.0 ({overall_health.status.value})")
|
||||
|
||||
# Component health scores
|
||||
if verbose and len(self.health_scores) > 1:
|
||||
print(f"\n📊 Component Health Scores:")
|
||||
for component, health in self.health_scores.items():
|
||||
if component != 'overall':
|
||||
status_emoji = {
|
||||
HealthStatus.HEALTHY: "🟢",
|
||||
HealthStatus.WARNING: "🟡",
|
||||
HealthStatus.CRITICAL: "🔴"
|
||||
}
|
||||
print(f" {status_emoji.get(health.status, '⚪')} {component}: {health.score:.2f}")
|
||||
|
||||
# Issues found
|
||||
if not self.issues:
|
||||
print("\n✅ All validations passed! System appears healthy.")
|
||||
else:
|
||||
severity_counts = {}
|
||||
for issue in self.issues:
|
||||
severity_counts[issue.severity] = severity_counts.get(issue.severity, 0) + 1
|
||||
|
||||
print(f"\n🔍 Found {len(self.issues)} issues:")
|
||||
for severity in [ValidationSeverity.CRITICAL, ValidationSeverity.HIGH,
|
||||
ValidationSeverity.MEDIUM, ValidationSeverity.LOW, ValidationSeverity.INFO]:
|
||||
if severity in severity_counts:
|
||||
severity_emoji = {
|
||||
ValidationSeverity.CRITICAL: "🚨",
|
||||
ValidationSeverity.HIGH: "⚠️ ",
|
||||
ValidationSeverity.MEDIUM: "🟡",
|
||||
ValidationSeverity.LOW: "ℹ️ ",
|
||||
ValidationSeverity.INFO: "💡"
|
||||
}
|
||||
print(f" {severity_emoji.get(severity, '')} {severity.value.title()}: {severity_counts[severity]}")
|
||||
|
||||
if verbose:
|
||||
print(f"\n📋 Detailed Issues:")
|
||||
for issue in sorted(self.issues, key=lambda x: x.severity.value):
|
||||
print(f"\n • {issue.component}/{issue.issue_type} ({issue.severity.value})")
|
||||
print(f" {issue.description}")
|
||||
if issue.evidence:
|
||||
print(f" Evidence: {'; '.join(issue.evidence)}")
|
||||
if issue.recommendations:
|
||||
print(f" Recommendations: {'; '.join(issue.recommendations)}")
|
||||
|
||||
# Fixes applied
|
||||
if self.fixes_applied:
|
||||
print(f"\n🔧 Applied {len(self.fixes_applied)} fixes:")
|
||||
for fix in self.fixes_applied:
|
||||
print(f" {fix}")
|
||||
|
||||
print("\n" + "="*70)
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point for YAML-driven validation."""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="YAML-driven Framework-Hooks validation engine"
|
||||
)
|
||||
parser.add_argument("--fix", action="store_true",
|
||||
help="Attempt to fix auto-fixable issues")
|
||||
parser.add_argument("--verbose", action="store_true",
|
||||
help="Verbose output with detailed results")
|
||||
parser.add_argument("--framework-root",
|
||||
default=".",
|
||||
help="Path to Framework-Hooks directory")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
framework_root = Path(args.framework_root).resolve()
|
||||
if not framework_root.exists():
|
||||
print(f"❌ Framework root directory not found: {framework_root}")
|
||||
sys.exit(1)
|
||||
|
||||
# Initialize YAML-driven validation engine
|
||||
validator = YAMLValidationEngine(framework_root, args.fix)
|
||||
|
||||
# Run comprehensive validation
|
||||
issues, fixes, health_scores = validator.validate_all()
|
||||
|
||||
# Print results
|
||||
validator.print_results(args.verbose)
|
||||
|
||||
# Exit with health score as return code (0 = perfect, higher = issues)
|
||||
overall_health = health_scores.get('overall')
|
||||
health_score = overall_health.score if overall_health else 0.0
|
||||
exit_code = max(0, min(10, int((1.0 - health_score) * 10))) # 0-10 range
|
||||
|
||||
sys.exit(exit_code)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,422 +0,0 @@
|
||||
"""
|
||||
Unified Configuration Loader for SuperClaude-Lite
|
||||
|
||||
High-performance configuration loading with support for both JSON and YAML formats,
|
||||
caching, hot-reload capabilities, and comprehensive error handling.
|
||||
|
||||
Supports:
|
||||
- Claude Code settings.json (JSON format)
|
||||
- SuperClaude superclaude-config.json (JSON format)
|
||||
- YAML configuration files
|
||||
- Unified configuration interface for hooks
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import yaml
|
||||
import time
|
||||
import hashlib
|
||||
from typing import Dict, Any, Optional, Union
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
class UnifiedConfigLoader:
|
||||
"""
|
||||
Intelligent configuration loader with support for JSON and YAML formats.
|
||||
|
||||
Features:
|
||||
- Dual-configuration support (Claude Code + SuperClaude)
|
||||
- File modification detection for hot-reload
|
||||
- In-memory caching for performance (<10ms access)
|
||||
- Comprehensive error handling and validation
|
||||
- Environment variable interpolation
|
||||
- Include/merge support for modular configs
|
||||
- Unified configuration interface
|
||||
"""
|
||||
|
||||
def __init__(self, project_root: Union[str, Path]):
|
||||
self.project_root = Path(project_root)
|
||||
self.config_dir = self.project_root / "config"
|
||||
|
||||
# Configuration file paths
|
||||
self.claude_settings_path = self.project_root / "settings.json"
|
||||
self.superclaude_config_path = self.project_root / "superclaude-config.json"
|
||||
|
||||
# Cache for all configuration sources
|
||||
self._cache: Dict[str, Dict[str, Any]] = {}
|
||||
self._file_hashes: Dict[str, str] = {}
|
||||
self._last_check: Dict[str, float] = {}
|
||||
self.check_interval = 1.0 # Check files every 1 second max
|
||||
|
||||
# Configuration source registry
|
||||
self._config_sources = {
|
||||
'claude_settings': self.claude_settings_path,
|
||||
'superclaude_config': self.superclaude_config_path
|
||||
}
|
||||
|
||||
def load_config(self, config_name: str, force_reload: bool = False) -> Dict[str, Any]:
|
||||
"""
|
||||
Load configuration with intelligent caching (supports JSON and YAML).
|
||||
|
||||
Args:
|
||||
config_name: Name of config file or special config identifier
|
||||
- For YAML: config file name without .yaml extension
|
||||
- For JSON: 'claude_settings' or 'superclaude_config'
|
||||
force_reload: Force reload even if cached
|
||||
|
||||
Returns:
|
||||
Parsed configuration dictionary
|
||||
|
||||
Raises:
|
||||
FileNotFoundError: If config file doesn't exist
|
||||
ValueError: If config parsing fails
|
||||
"""
|
||||
# Handle special configuration sources
|
||||
if config_name in self._config_sources:
|
||||
return self._load_json_config(config_name, force_reload)
|
||||
|
||||
# Handle YAML configuration files
|
||||
config_path = self.config_dir / f"{config_name}.yaml"
|
||||
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Configuration file not found: {config_path}")
|
||||
|
||||
# Check if we need to reload
|
||||
if not force_reload and self._should_use_cache(config_name, config_path):
|
||||
return self._cache[config_name]
|
||||
|
||||
# Load and parse the YAML configuration
|
||||
try:
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
# Environment variable interpolation
|
||||
content = self._interpolate_env_vars(content)
|
||||
|
||||
# Parse YAML
|
||||
config = yaml.safe_load(content)
|
||||
|
||||
# Handle includes/merges
|
||||
config = self._process_includes(config, config_path.parent)
|
||||
|
||||
# Update cache
|
||||
self._cache[config_name] = config
|
||||
self._file_hashes[config_name] = self._compute_hash(config_path)
|
||||
self._last_check[config_name] = time.time()
|
||||
|
||||
return config
|
||||
|
||||
except yaml.YAMLError as e:
|
||||
raise ValueError(f"YAML parsing error in {config_path}: {e}")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Error loading config {config_name}: {e}")
|
||||
|
||||
def _load_json_config(self, config_name: str, force_reload: bool = False) -> Dict[str, Any]:
|
||||
"""Load JSON configuration file."""
|
||||
config_path = self._config_sources[config_name]
|
||||
|
||||
if not config_path.exists():
|
||||
raise FileNotFoundError(f"Configuration file not found: {config_path}")
|
||||
|
||||
# Check if we need to reload
|
||||
if not force_reload and self._should_use_cache(config_name, config_path):
|
||||
return self._cache[config_name]
|
||||
|
||||
# Load and parse the JSON configuration
|
||||
try:
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
|
||||
# Environment variable interpolation
|
||||
content = self._interpolate_env_vars(content)
|
||||
|
||||
# Parse JSON
|
||||
config = json.loads(content)
|
||||
|
||||
# Update cache
|
||||
self._cache[config_name] = config
|
||||
self._file_hashes[config_name] = self._compute_hash(config_path)
|
||||
self._last_check[config_name] = time.time()
|
||||
|
||||
return config
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
raise ValueError(f"JSON parsing error in {config_path}: {e}")
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Error loading JSON config {config_name}: {e}")
|
||||
|
||||
def get_section(self, config_name: str, section_path: str, default: Any = None) -> Any:
|
||||
"""
|
||||
Get specific section from configuration using dot notation.
|
||||
|
||||
Args:
|
||||
config_name: Configuration file name or identifier
|
||||
section_path: Dot-separated path (e.g., 'routing.ui_components')
|
||||
default: Default value if section not found
|
||||
|
||||
Returns:
|
||||
Configuration section value or default
|
||||
"""
|
||||
config = self.load_config(config_name)
|
||||
|
||||
try:
|
||||
result = config
|
||||
for key in section_path.split('.'):
|
||||
result = result[key]
|
||||
return result
|
||||
except (KeyError, TypeError):
|
||||
return default
|
||||
|
||||
def get_hook_config(self, hook_name: str, section_path: str = None, default: Any = None) -> Any:
|
||||
"""
|
||||
Get hook-specific configuration from SuperClaude config.
|
||||
|
||||
Args:
|
||||
hook_name: Hook name (e.g., 'session_start', 'pre_tool_use')
|
||||
section_path: Optional dot-separated path within hook config
|
||||
default: Default value if not found
|
||||
|
||||
Returns:
|
||||
Hook configuration or specific section
|
||||
"""
|
||||
base_path = f"hook_configurations.{hook_name}"
|
||||
if section_path:
|
||||
full_path = f"{base_path}.{section_path}"
|
||||
else:
|
||||
full_path = base_path
|
||||
|
||||
return self.get_section('superclaude_config', full_path, default)
|
||||
|
||||
def get_claude_hooks(self) -> Dict[str, Any]:
|
||||
"""Get Claude Code hook definitions from settings.json."""
|
||||
return self.get_section('claude_settings', 'hooks', {})
|
||||
|
||||
def get_superclaude_config(self, section_path: str = None, default: Any = None) -> Any:
|
||||
"""
|
||||
Get SuperClaude framework configuration.
|
||||
|
||||
Args:
|
||||
section_path: Optional dot-separated path (e.g., 'global_configuration.performance_monitoring')
|
||||
default: Default value if not found
|
||||
|
||||
Returns:
|
||||
Configuration section or full config if no path specified
|
||||
"""
|
||||
if section_path:
|
||||
return self.get_section('superclaude_config', section_path, default)
|
||||
else:
|
||||
return self.load_config('superclaude_config')
|
||||
|
||||
def get_mcp_server_config(self, server_name: str = None) -> Dict[str, Any]:
|
||||
"""
|
||||
Get MCP server configuration.
|
||||
|
||||
Args:
|
||||
server_name: Optional specific server name
|
||||
|
||||
Returns:
|
||||
MCP server configuration
|
||||
"""
|
||||
if server_name:
|
||||
return self.get_section('superclaude_config', f'mcp_server_integration.servers.{server_name}', {})
|
||||
else:
|
||||
return self.get_section('superclaude_config', 'mcp_server_integration', {})
|
||||
|
||||
def get_performance_targets(self) -> Dict[str, Any]:
|
||||
"""Get performance targets for all components."""
|
||||
return self.get_section('superclaude_config', 'global_configuration.performance_monitoring', {})
|
||||
|
||||
def is_hook_enabled(self, hook_name: str) -> bool:
|
||||
"""Check if a specific hook is enabled."""
|
||||
return self.get_hook_config(hook_name, 'enabled', False)
|
||||
|
||||
def reload_all(self) -> None:
|
||||
"""Force reload of all cached configurations."""
|
||||
for config_name in list(self._cache.keys()):
|
||||
self.load_config(config_name, force_reload=True)
|
||||
|
||||
def _should_use_cache(self, config_name: str, config_path: Path) -> bool:
|
||||
"""Check if cached version is still valid."""
|
||||
if config_name not in self._cache:
|
||||
return False
|
||||
|
||||
# Rate limit file checks
|
||||
now = time.time()
|
||||
if now - self._last_check.get(config_name, 0) < self.check_interval:
|
||||
return True
|
||||
|
||||
# Check if file changed
|
||||
current_hash = self._compute_hash(config_path)
|
||||
return current_hash == self._file_hashes.get(config_name)
|
||||
|
||||
def _compute_hash(self, file_path: Path) -> str:
|
||||
"""Compute file hash for change detection."""
|
||||
stat = file_path.stat()
|
||||
return hashlib.md5(f"{stat.st_mtime}:{stat.st_size}".encode()).hexdigest()
|
||||
|
||||
def _interpolate_env_vars(self, content: str) -> str:
|
||||
"""Replace environment variables in YAML content."""
|
||||
import re
|
||||
|
||||
def replace_env_var(match):
|
||||
var_name = match.group(1)
|
||||
default_value = match.group(2) if match.group(2) else ""
|
||||
return os.getenv(var_name, default_value)
|
||||
|
||||
# Support ${VAR} and ${VAR:default} syntax
|
||||
pattern = r'\$\{([^}:]+)(?::([^}]*))?\}'
|
||||
return re.sub(pattern, replace_env_var, content)
|
||||
|
||||
def _process_includes(self, config: Dict[str, Any], base_dir: Path) -> Dict[str, Any]:
|
||||
"""Process include directives in configuration."""
|
||||
if not isinstance(config, dict):
|
||||
return config
|
||||
|
||||
# Handle special include key
|
||||
if '__include__' in config:
|
||||
includes = config.pop('__include__')
|
||||
if isinstance(includes, str):
|
||||
includes = [includes]
|
||||
|
||||
for include_file in includes:
|
||||
include_path = base_dir / include_file
|
||||
if include_path.exists():
|
||||
with open(include_path, 'r', encoding='utf-8') as f:
|
||||
included_config = yaml.safe_load(f.read())
|
||||
if isinstance(included_config, dict):
|
||||
# Merge included config (current config takes precedence)
|
||||
included_config.update(config)
|
||||
config = included_config
|
||||
|
||||
return config
|
||||
|
||||
def get_intelligence_config(self, intelligence_type: str, section_path: str = None, default: Any = None) -> Any:
|
||||
"""
|
||||
Get intelligence configuration from YAML patterns.
|
||||
|
||||
Args:
|
||||
intelligence_type: Type of intelligence config (e.g., 'intelligence_patterns', 'mcp_orchestration')
|
||||
section_path: Optional dot-separated path within intelligence config
|
||||
default: Default value if not found
|
||||
|
||||
Returns:
|
||||
Intelligence configuration or specific section
|
||||
"""
|
||||
try:
|
||||
config = self.load_config(intelligence_type)
|
||||
|
||||
if section_path:
|
||||
result = config
|
||||
for key in section_path.split('.'):
|
||||
result = result[key]
|
||||
return result
|
||||
else:
|
||||
return config
|
||||
|
||||
except (FileNotFoundError, KeyError, TypeError):
|
||||
return default
|
||||
|
||||
def get_pattern_dimensions(self) -> Dict[str, Any]:
|
||||
"""Get pattern recognition dimensions from intelligence patterns."""
|
||||
return self.get_intelligence_config(
|
||||
'intelligence_patterns',
|
||||
'learning_intelligence.pattern_recognition.dimensions',
|
||||
{'primary': ['context_type', 'complexity_score', 'operation_type'], 'secondary': []}
|
||||
)
|
||||
|
||||
def get_mcp_orchestration_rules(self) -> Dict[str, Any]:
|
||||
"""Get MCP server orchestration rules."""
|
||||
return self.get_intelligence_config(
|
||||
'mcp_orchestration',
|
||||
'server_selection.decision_tree',
|
||||
[]
|
||||
)
|
||||
|
||||
def get_hook_coordination_patterns(self) -> Dict[str, Any]:
|
||||
"""Get hook coordination execution patterns."""
|
||||
return self.get_intelligence_config(
|
||||
'hook_coordination',
|
||||
'execution_patterns',
|
||||
{}
|
||||
)
|
||||
|
||||
def get_performance_zones(self) -> Dict[str, Any]:
|
||||
"""Get performance management resource zones."""
|
||||
return self.get_intelligence_config(
|
||||
'performance_intelligence',
|
||||
'resource_management.resource_zones',
|
||||
{}
|
||||
)
|
||||
|
||||
def get_validation_health_config(self) -> Dict[str, Any]:
|
||||
"""Get validation and health scoring configuration."""
|
||||
return self.get_intelligence_config(
|
||||
'validation_intelligence',
|
||||
'health_scoring',
|
||||
{}
|
||||
)
|
||||
|
||||
def get_ux_project_patterns(self) -> Dict[str, Any]:
|
||||
"""Get user experience project detection patterns."""
|
||||
return self.get_intelligence_config(
|
||||
'user_experience',
|
||||
'project_detection.detection_patterns',
|
||||
{}
|
||||
)
|
||||
|
||||
def get_intelligence_summary(self) -> Dict[str, Any]:
|
||||
"""Get summary of all available intelligence configurations."""
|
||||
intelligence_types = [
|
||||
'intelligence_patterns',
|
||||
'mcp_orchestration',
|
||||
'hook_coordination',
|
||||
'performance_intelligence',
|
||||
'validation_intelligence',
|
||||
'user_experience'
|
||||
]
|
||||
|
||||
summary = {}
|
||||
for intelligence_type in intelligence_types:
|
||||
try:
|
||||
config = self.load_config(intelligence_type)
|
||||
summary[intelligence_type] = {
|
||||
'loaded': True,
|
||||
'version': config.get('version', 'unknown'),
|
||||
'last_updated': config.get('last_updated', 'unknown'),
|
||||
'sections': list(config.keys()) if isinstance(config, dict) else []
|
||||
}
|
||||
except Exception:
|
||||
summary[intelligence_type] = {
|
||||
'loaded': False,
|
||||
'error': 'Failed to load configuration'
|
||||
}
|
||||
|
||||
return summary
|
||||
|
||||
def reload_intelligence_configs(self) -> Dict[str, bool]:
|
||||
"""Force reload all intelligence configurations and return status."""
|
||||
intelligence_types = [
|
||||
'intelligence_patterns',
|
||||
'mcp_orchestration',
|
||||
'hook_coordination',
|
||||
'performance_intelligence',
|
||||
'validation_intelligence',
|
||||
'user_experience'
|
||||
]
|
||||
|
||||
reload_status = {}
|
||||
for intelligence_type in intelligence_types:
|
||||
try:
|
||||
self.load_config(intelligence_type, force_reload=True)
|
||||
reload_status[intelligence_type] = True
|
||||
except Exception as e:
|
||||
reload_status[intelligence_type] = False
|
||||
print(f"Warning: Could not reload {intelligence_type}: {e}")
|
||||
|
||||
return reload_status
|
||||
|
||||
|
||||
# Global instance for shared use across hooks
|
||||
# Use Claude installation directory instead of current working directory
|
||||
import os
|
||||
config_loader = UnifiedConfigLoader(os.path.expanduser("~/.claude"))
|
||||
@@ -1,804 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
SuperClaude-Lite Stop Hook
|
||||
|
||||
Implements session analytics + /sc:save logic with performance tracking.
|
||||
Performance target: <200ms execution time.
|
||||
|
||||
This hook runs at session end and provides:
|
||||
- Comprehensive session analytics and performance metrics
|
||||
- Learning consolidation and adaptation updates
|
||||
- Session persistence with intelligent compression
|
||||
- Performance optimization recommendations
|
||||
- Quality assessment and improvement suggestions
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional
|
||||
import statistics
|
||||
|
||||
# Add shared modules to path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
|
||||
|
||||
from framework_logic import FrameworkLogic
|
||||
from pattern_detection import PatternDetector
|
||||
from mcp_intelligence import MCPIntelligence
|
||||
from compression_engine import CompressionEngine
|
||||
from learning_engine import LearningEngine, LearningType, AdaptationScope
|
||||
from yaml_loader import config_loader
|
||||
from logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
|
||||
|
||||
class StopHook:
|
||||
"""
|
||||
Stop hook implementing session analytics and persistence.
|
||||
|
||||
Responsibilities:
|
||||
- Analyze session performance and effectiveness
|
||||
- Consolidate learning events and adaptations
|
||||
- Generate comprehensive session analytics
|
||||
- Implement intelligent session persistence
|
||||
- Provide optimization recommendations for future sessions
|
||||
- Track SuperClaude framework effectiveness metrics
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
start_time = time.time()
|
||||
|
||||
# Initialize core components
|
||||
self.framework_logic = FrameworkLogic()
|
||||
self.pattern_detector = PatternDetector()
|
||||
self.mcp_intelligence = MCPIntelligence()
|
||||
self.compression_engine = CompressionEngine()
|
||||
|
||||
# Initialize learning engine with installation directory cache
|
||||
import os
|
||||
cache_dir = Path(os.path.expanduser("~/.claude/cache"))
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.learning_engine = LearningEngine(cache_dir)
|
||||
|
||||
# Load hook-specific configuration from SuperClaude config
|
||||
self.hook_config = config_loader.get_hook_config('stop')
|
||||
|
||||
# Load session configuration (from YAML if exists, otherwise use hook config)
|
||||
try:
|
||||
self.session_config = config_loader.load_config('session')
|
||||
except FileNotFoundError:
|
||||
# Fall back to hook configuration if YAML file not found
|
||||
self.session_config = self.hook_config.get('configuration', {})
|
||||
|
||||
# Performance tracking using configuration
|
||||
self.initialization_time = (time.time() - start_time) * 1000
|
||||
self.performance_target_ms = config_loader.get_hook_config('stop', 'performance_target_ms', 200)
|
||||
|
||||
# Store cache directory reference
|
||||
self._cache_dir = cache_dir
|
||||
|
||||
def process_session_stop(self, session_data: dict) -> dict:
|
||||
"""
|
||||
Process session stop with analytics and persistence.
|
||||
|
||||
Args:
|
||||
session_data: Session termination data from Claude Code
|
||||
|
||||
Returns:
|
||||
Session analytics report with learning insights and persistence status
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# Log hook start
|
||||
log_hook_start("stop", {
|
||||
"session_id": session_data.get('session_id', ''),
|
||||
"session_duration_ms": session_data.get('duration_ms', 0),
|
||||
"operations_count": len(session_data.get('operations', [])),
|
||||
"errors_count": len(session_data.get('errors', [])),
|
||||
"superclaude_enabled": session_data.get('superclaude_enabled', False)
|
||||
})
|
||||
|
||||
try:
|
||||
# Extract session context
|
||||
context = self._extract_session_context(session_data)
|
||||
|
||||
# Analyze session performance
|
||||
performance_analysis = self._analyze_session_performance(context)
|
||||
|
||||
# Log performance analysis results
|
||||
log_decision(
|
||||
"stop",
|
||||
"performance_analysis",
|
||||
f"{performance_analysis['overall_score']:.2f}",
|
||||
f"Productivity: {context.get('session_productivity', 0):.2f}, Errors: {context.get('error_rate', 0):.2f}, Bottlenecks: {', '.join(performance_analysis['bottlenecks_identified'])}"
|
||||
)
|
||||
|
||||
# Consolidate learning events
|
||||
learning_consolidation = self._consolidate_learning_events(context)
|
||||
|
||||
# Generate session analytics
|
||||
session_analytics = self._generate_session_analytics(
|
||||
context, performance_analysis, learning_consolidation
|
||||
)
|
||||
|
||||
# Perform session persistence
|
||||
persistence_result = self._perform_session_persistence(context, session_analytics)
|
||||
|
||||
# Log persistence results
|
||||
if persistence_result['persistence_enabled']:
|
||||
log_decision(
|
||||
"stop",
|
||||
"session_persistence",
|
||||
"saved",
|
||||
f"Analytics saved: {persistence_result['analytics_saved']}, Compression: {persistence_result['compression_applied']}"
|
||||
)
|
||||
|
||||
# Generate recommendations
|
||||
recommendations = self._generate_recommendations(
|
||||
context, performance_analysis, learning_consolidation
|
||||
)
|
||||
|
||||
# Log recommendations generated
|
||||
total_recommendations = sum(len(recs) for recs in recommendations.values())
|
||||
if total_recommendations > 0:
|
||||
log_decision(
|
||||
"stop",
|
||||
"recommendations_generated",
|
||||
str(total_recommendations),
|
||||
f"Categories: {', '.join(k for k, v in recommendations.items() if v)}"
|
||||
)
|
||||
|
||||
# Create final learning events
|
||||
self._create_final_learning_events(context, session_analytics)
|
||||
|
||||
# Generate session report
|
||||
session_report = self._generate_session_report(
|
||||
context, session_analytics, persistence_result, recommendations
|
||||
)
|
||||
|
||||
# Performance tracking
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
session_report['performance_metrics'] = {
|
||||
'stop_processing_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'total_session_efficiency': self._calculate_session_efficiency(session_analytics)
|
||||
}
|
||||
|
||||
# Log hook end with success
|
||||
log_hook_end(
|
||||
"stop",
|
||||
int(execution_time),
|
||||
True,
|
||||
{
|
||||
"session_score": session_analytics['performance_metrics']['overall_score'],
|
||||
"superclaude_effectiveness": session_analytics['superclaude_effectiveness']['effectiveness_score'],
|
||||
"learning_insights": session_analytics['learning_summary']['insights_generated'],
|
||||
"recommendations": total_recommendations,
|
||||
"performance_target_met": execution_time < self.performance_target_ms
|
||||
}
|
||||
)
|
||||
|
||||
return session_report
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
log_error("stop", str(e), {"session_data": session_data})
|
||||
|
||||
# Log hook end with failure
|
||||
log_hook_end("stop", int((time.time() - start_time) * 1000), False)
|
||||
|
||||
# Graceful fallback on error
|
||||
return self._create_fallback_report(session_data, str(e))
|
||||
|
||||
def _get_current_session_id(self) -> str:
|
||||
"""Get current session ID from cache."""
|
||||
try:
|
||||
session_id_file = self._cache_dir / "session_id"
|
||||
if session_id_file.exists():
|
||||
return session_id_file.read_text().strip()
|
||||
except Exception:
|
||||
pass
|
||||
return ""
|
||||
|
||||
def _parse_session_activity(self, session_id: str) -> dict:
|
||||
"""Parse session activity from log files."""
|
||||
tools_used = set()
|
||||
operations_count = 0
|
||||
mcp_tools_used = set()
|
||||
|
||||
if not session_id:
|
||||
return {
|
||||
"operations_completed": 0,
|
||||
"tools_utilized": [],
|
||||
"unique_tools_count": 0,
|
||||
"mcp_tools_used": []
|
||||
}
|
||||
|
||||
# Parse current log file
|
||||
log_file_path = self._cache_dir / "logs" / f"superclaude-lite-{time.strftime('%Y-%m-%d')}.log"
|
||||
|
||||
try:
|
||||
if log_file_path.exists():
|
||||
with open(log_file_path, 'r') as f:
|
||||
for line in f:
|
||||
try:
|
||||
log_entry = json.loads(line.strip())
|
||||
|
||||
# Only process entries for current session
|
||||
if log_entry.get('session') != session_id:
|
||||
continue
|
||||
|
||||
# Count operations from pre_tool_use hook
|
||||
if (log_entry.get('hook') == 'pre_tool_use' and
|
||||
log_entry.get('event') == 'start'):
|
||||
operations_count += 1
|
||||
tool_name = log_entry.get('data', {}).get('tool_name', '')
|
||||
if tool_name:
|
||||
tools_used.add(tool_name)
|
||||
# Track MCP tools separately
|
||||
if tool_name.startswith('mcp__'):
|
||||
mcp_tools_used.add(tool_name)
|
||||
|
||||
except (json.JSONDecodeError, KeyError):
|
||||
# Skip malformed log entries
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
# Log error but don't fail
|
||||
log_error("stop", f"Failed to parse session activity: {str(e)}", {"session_id": session_id})
|
||||
|
||||
return {
|
||||
"operations_completed": operations_count,
|
||||
"tools_utilized": list(tools_used),
|
||||
"unique_tools_count": len(tools_used),
|
||||
"mcp_tools_used": list(mcp_tools_used)
|
||||
}
|
||||
|
||||
def _extract_session_context(self, session_data: dict) -> dict:
|
||||
"""Extract and enrich session context."""
|
||||
# Get current session ID
|
||||
current_session_id = self._get_current_session_id()
|
||||
|
||||
# Try to load session context from current session file
|
||||
session_context = {}
|
||||
if current_session_id:
|
||||
session_file_path = self._cache_dir / f"session_{current_session_id}.json"
|
||||
try:
|
||||
if session_file_path.exists():
|
||||
with open(session_file_path, 'r') as f:
|
||||
session_context = json.load(f)
|
||||
except Exception as e:
|
||||
# Log error but continue with fallback
|
||||
log_error("stop", f"Failed to load session context: {str(e)}", {"session_id": current_session_id})
|
||||
|
||||
# Parse session activity from logs
|
||||
activity_data = self._parse_session_activity(current_session_id)
|
||||
|
||||
# Create context with session file data (if available) or fallback to session_data
|
||||
context = {
|
||||
'session_id': session_context.get('session_id', current_session_id or session_data.get('session_id', '')),
|
||||
'session_duration_ms': session_data.get('duration_ms', 0), # This comes from hook system
|
||||
'session_start_time': session_context.get('initialization_timestamp', session_data.get('start_time', 0)),
|
||||
'session_end_time': time.time(),
|
||||
'operations_performed': activity_data.get('tools_utilized', []), # Actual tools used from logs
|
||||
'tools_used': activity_data.get('tools_utilized', []), # Actual tools used from logs
|
||||
'mcp_servers_activated': session_context.get('mcp_servers', {}).get('enabled_servers', []),
|
||||
'errors_encountered': session_data.get('errors', []),
|
||||
'user_interactions': session_data.get('user_interactions', []),
|
||||
'resource_usage': session_data.get('resource_usage', {}),
|
||||
'quality_metrics': session_data.get('quality_metrics', {}),
|
||||
'superclaude_enabled': session_context.get('superclaude_enabled', False),
|
||||
# Add parsed activity metrics
|
||||
'operation_count': activity_data.get('operations_completed', 0),
|
||||
'unique_tools_count': activity_data.get('unique_tools_count', 0),
|
||||
'mcp_tools_used': activity_data.get('mcp_tools_used', [])
|
||||
}
|
||||
|
||||
# Calculate derived metrics
|
||||
context.update(self._calculate_derived_metrics(context))
|
||||
|
||||
return context
|
||||
|
||||
def _calculate_derived_metrics(self, context: dict) -> dict:
|
||||
"""Calculate derived session metrics."""
|
||||
operations = context.get('operations_performed', [])
|
||||
tools = context.get('tools_used', [])
|
||||
|
||||
return {
|
||||
'operation_count': len(operations),
|
||||
'unique_tools_count': len(set(tools)),
|
||||
'error_rate': len(context.get('errors_encountered', [])) / max(len(operations), 1),
|
||||
'mcp_usage_ratio': len(context.get('mcp_servers_activated', [])) / max(len(operations), 1),
|
||||
'session_productivity': self._calculate_productivity_score(context),
|
||||
'superclaude_effectiveness': self._calculate_superclaude_effectiveness(context)
|
||||
}
|
||||
|
||||
def _calculate_productivity_score(self, context: dict) -> float:
|
||||
"""Calculate session productivity score (0.0 to 1.0)."""
|
||||
operations = context.get('operations_performed', [])
|
||||
errors = context.get('errors_encountered', [])
|
||||
duration_ms = context.get('session_duration_ms', 1)
|
||||
|
||||
if not operations:
|
||||
return 0.0
|
||||
|
||||
# Base productivity from operation completion
|
||||
completion_rate = (len(operations) - len(errors)) / len(operations)
|
||||
|
||||
# Time efficiency (operations per minute)
|
||||
duration_minutes = duration_ms / (1000 * 60)
|
||||
operations_per_minute = len(operations) / max(duration_minutes, 0.1)
|
||||
|
||||
# Normalize operations per minute (assume 5 ops/min is very productive)
|
||||
time_efficiency = min(operations_per_minute / 5.0, 1.0)
|
||||
|
||||
# Combined productivity score
|
||||
productivity = (completion_rate * 0.7) + (time_efficiency * 0.3)
|
||||
|
||||
return min(productivity, 1.0)
|
||||
|
||||
def _calculate_superclaude_effectiveness(self, context: dict) -> float:
|
||||
"""Calculate SuperClaude framework effectiveness score."""
|
||||
if not context.get('superclaude_enabled'):
|
||||
return 0.0
|
||||
|
||||
# Factors that indicate SuperClaude effectiveness
|
||||
factors = []
|
||||
|
||||
# MCP server utilization
|
||||
mcp_ratio = context.get('mcp_usage_ratio', 0)
|
||||
factors.append(min(mcp_ratio * 2, 1.0)) # More MCP usage = better intelligence
|
||||
|
||||
# Error reduction (assume SuperClaude reduces errors)
|
||||
error_rate = context.get('error_rate', 0)
|
||||
error_effectiveness = max(1.0 - (error_rate * 2), 0.0)
|
||||
factors.append(error_effectiveness)
|
||||
|
||||
# Productivity enhancement
|
||||
productivity = context.get('session_productivity', 0)
|
||||
factors.append(productivity)
|
||||
|
||||
# Quality metrics if available
|
||||
quality_metrics = context.get('quality_metrics', {})
|
||||
if quality_metrics:
|
||||
avg_quality = statistics.mean(quality_metrics.values()) if quality_metrics.values() else 0.5
|
||||
factors.append(avg_quality)
|
||||
|
||||
return statistics.mean(factors) if factors else 0.5
|
||||
|
||||
def _analyze_session_performance(self, context: dict) -> dict:
|
||||
"""Analyze overall session performance."""
|
||||
performance_analysis = {
|
||||
'overall_score': 0.0,
|
||||
'performance_categories': {},
|
||||
'bottlenecks_identified': [],
|
||||
'optimization_opportunities': [],
|
||||
'performance_trends': {}
|
||||
}
|
||||
|
||||
# Overall performance scoring
|
||||
productivity = context.get('session_productivity', 0)
|
||||
effectiveness = context.get('superclaude_effectiveness', 0)
|
||||
error_rate = context.get('error_rate', 0)
|
||||
|
||||
performance_analysis['overall_score'] = (
|
||||
productivity * 0.4 +
|
||||
effectiveness * 0.4 +
|
||||
(1.0 - error_rate) * 0.2
|
||||
)
|
||||
|
||||
# Category-specific performance
|
||||
performance_analysis['performance_categories'] = {
|
||||
'productivity': productivity,
|
||||
'quality': 1.0 - error_rate,
|
||||
'intelligence_utilization': context.get('mcp_usage_ratio', 0),
|
||||
'resource_efficiency': self._calculate_resource_efficiency(context),
|
||||
'user_satisfaction_estimate': self._estimate_user_satisfaction(context)
|
||||
}
|
||||
|
||||
# Identify bottlenecks
|
||||
if error_rate > 0.2:
|
||||
performance_analysis['bottlenecks_identified'].append('high_error_rate')
|
||||
|
||||
if productivity < 0.5:
|
||||
performance_analysis['bottlenecks_identified'].append('low_productivity')
|
||||
|
||||
if context.get('mcp_usage_ratio', 0) < 0.3 and context.get('superclaude_enabled'):
|
||||
performance_analysis['bottlenecks_identified'].append('underutilized_intelligence')
|
||||
log_decision(
|
||||
"stop",
|
||||
"intelligence_utilization",
|
||||
"low",
|
||||
f"MCP usage ratio: {context.get('mcp_usage_ratio', 0):.2f}, SuperClaude enabled but underutilized"
|
||||
)
|
||||
|
||||
# Optimization opportunities
|
||||
if context.get('unique_tools_count', 0) > 10:
|
||||
performance_analysis['optimization_opportunities'].append('tool_usage_optimization')
|
||||
|
||||
if len(context.get('mcp_servers_activated', [])) < 2 and context.get('operation_count', 0) > 5:
|
||||
performance_analysis['optimization_opportunities'].append('mcp_server_coordination')
|
||||
|
||||
return performance_analysis
|
||||
|
||||
def _calculate_resource_efficiency(self, context: dict) -> float:
|
||||
"""Calculate resource usage efficiency."""
|
||||
resource_usage = context.get('resource_usage', {})
|
||||
|
||||
if not resource_usage:
|
||||
return 0.8 # Assume good efficiency if no data
|
||||
|
||||
# Extract resource metrics
|
||||
memory_usage = resource_usage.get('memory_percent', 50)
|
||||
cpu_usage = resource_usage.get('cpu_percent', 50)
|
||||
token_usage = resource_usage.get('token_percent', 50)
|
||||
|
||||
# Efficiency is inversely related to usage (but some usage is good)
|
||||
memory_efficiency = 1.0 - max((memory_usage - 60) / 40, 0) # Penalty above 60%
|
||||
cpu_efficiency = 1.0 - max((cpu_usage - 70) / 30, 0) # Penalty above 70%
|
||||
token_efficiency = 1.0 - max((token_usage - 75) / 25, 0) # Penalty above 75%
|
||||
|
||||
return (memory_efficiency + cpu_efficiency + token_efficiency) / 3
|
||||
|
||||
def _estimate_user_satisfaction(self, context: dict) -> float:
|
||||
"""Estimate user satisfaction based on session metrics."""
|
||||
satisfaction_factors = []
|
||||
|
||||
# Low error rate increases satisfaction
|
||||
error_rate = context.get('error_rate', 0)
|
||||
satisfaction_factors.append(1.0 - error_rate)
|
||||
|
||||
# High productivity increases satisfaction
|
||||
productivity = context.get('session_productivity', 0)
|
||||
satisfaction_factors.append(productivity)
|
||||
|
||||
# SuperClaude effectiveness increases satisfaction
|
||||
if context.get('superclaude_enabled'):
|
||||
effectiveness = context.get('superclaude_effectiveness', 0)
|
||||
satisfaction_factors.append(effectiveness)
|
||||
|
||||
# Session duration factor (not too short, not too long)
|
||||
duration_minutes = context.get('session_duration_ms', 0) / (1000 * 60)
|
||||
if duration_minutes > 0:
|
||||
# Optimal session length is 15-60 minutes
|
||||
if 15 <= duration_minutes <= 60:
|
||||
duration_satisfaction = 1.0
|
||||
elif duration_minutes < 15:
|
||||
duration_satisfaction = duration_minutes / 15
|
||||
else:
|
||||
duration_satisfaction = max(1.0 - (duration_minutes - 60) / 120, 0.3)
|
||||
satisfaction_factors.append(duration_satisfaction)
|
||||
|
||||
return statistics.mean(satisfaction_factors) if satisfaction_factors else 0.5
|
||||
|
||||
def _consolidate_learning_events(self, context: dict) -> dict:
|
||||
"""Consolidate learning events from the session."""
|
||||
learning_consolidation = {
|
||||
'total_learning_events': 0,
|
||||
'learning_categories': {},
|
||||
'adaptations_created': 0,
|
||||
'effectiveness_feedback': [],
|
||||
'learning_insights': []
|
||||
}
|
||||
|
||||
# Generate learning insights from session
|
||||
insights = self.learning_engine.generate_learning_insights()
|
||||
learning_consolidation['learning_insights'] = [
|
||||
{
|
||||
'insight_type': insight.insight_type,
|
||||
'description': insight.description,
|
||||
'confidence': insight.confidence,
|
||||
'impact_score': insight.impact_score
|
||||
}
|
||||
for insight in insights
|
||||
]
|
||||
|
||||
# Session-specific learning
|
||||
session_learning = {
|
||||
'session_effectiveness': context.get('superclaude_effectiveness', 0),
|
||||
'performance_score': context.get('session_productivity', 0),
|
||||
'mcp_coordination_effectiveness': min(context.get('mcp_usage_ratio', 0) * 2, 1.0),
|
||||
'error_recovery_success': 1.0 - context.get('error_rate', 0)
|
||||
}
|
||||
|
||||
# Record session learning
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.EFFECTIVENESS_FEEDBACK,
|
||||
AdaptationScope.SESSION,
|
||||
context,
|
||||
session_learning,
|
||||
context.get('superclaude_effectiveness', 0),
|
||||
0.9,
|
||||
{'hook': 'stop', 'session_end': True}
|
||||
)
|
||||
|
||||
learning_consolidation['total_learning_events'] = 1 + len(insights)
|
||||
|
||||
return learning_consolidation
|
||||
|
||||
def _generate_session_analytics(self, context: dict, performance_analysis: dict,
|
||||
learning_consolidation: dict) -> dict:
|
||||
"""Generate comprehensive session analytics."""
|
||||
analytics = {
|
||||
'session_summary': {
|
||||
'session_id': context['session_id'],
|
||||
'duration_minutes': context.get('session_duration_ms', 0) / (1000 * 60),
|
||||
'operations_completed': context.get('operation_count', 0),
|
||||
'tools_utilized': context.get('unique_tools_count', 0),
|
||||
'mcp_servers_used': len(context.get('mcp_servers_activated', [])),
|
||||
'superclaude_enabled': context.get('superclaude_enabled', False)
|
||||
},
|
||||
|
||||
'performance_metrics': {
|
||||
'overall_score': performance_analysis['overall_score'],
|
||||
'productivity_score': context.get('session_productivity', 0),
|
||||
'quality_score': 1.0 - context.get('error_rate', 0),
|
||||
'efficiency_score': performance_analysis['performance_categories'].get('resource_efficiency', 0),
|
||||
'satisfaction_estimate': performance_analysis['performance_categories'].get('user_satisfaction_estimate', 0)
|
||||
},
|
||||
|
||||
'superclaude_effectiveness': {
|
||||
'framework_enabled': context.get('superclaude_enabled', False),
|
||||
'effectiveness_score': context.get('superclaude_effectiveness', 0),
|
||||
'intelligence_utilization': context.get('mcp_usage_ratio', 0),
|
||||
'learning_events_generated': learning_consolidation['total_learning_events'],
|
||||
'adaptations_created': learning_consolidation['adaptations_created']
|
||||
},
|
||||
|
||||
'quality_analysis': {
|
||||
'error_rate': context.get('error_rate', 0),
|
||||
'operation_success_rate': 1.0 - context.get('error_rate', 0),
|
||||
'bottlenecks': performance_analysis['bottlenecks_identified'],
|
||||
'optimization_opportunities': performance_analysis['optimization_opportunities']
|
||||
},
|
||||
|
||||
'learning_summary': {
|
||||
'insights_generated': len(learning_consolidation['learning_insights']),
|
||||
'key_insights': learning_consolidation['learning_insights'][:3], # Top 3 insights
|
||||
'learning_effectiveness': statistics.mean([
|
||||
insight['confidence'] * insight['impact_score']
|
||||
for insight in learning_consolidation['learning_insights']
|
||||
]) if learning_consolidation['learning_insights'] else 0.0
|
||||
},
|
||||
|
||||
'resource_utilization': context.get('resource_usage', {}),
|
||||
|
||||
'session_metadata': {
|
||||
'start_time': context.get('session_start_time', 0),
|
||||
'end_time': context.get('session_end_time', 0),
|
||||
'framework_version': '1.0.0',
|
||||
'analytics_version': 'stop_1.0'
|
||||
}
|
||||
}
|
||||
|
||||
return analytics
|
||||
|
||||
def _perform_session_persistence(self, context: dict, session_analytics: dict) -> dict:
|
||||
"""Perform intelligent session persistence."""
|
||||
persistence_result = {
|
||||
'persistence_enabled': True,
|
||||
'session_data_saved': False,
|
||||
'analytics_saved': False,
|
||||
'learning_data_saved': False,
|
||||
'compression_applied': False,
|
||||
'storage_optimized': False
|
||||
}
|
||||
|
||||
try:
|
||||
# Save session analytics
|
||||
analytics_data = json.dumps(session_analytics, indent=2)
|
||||
|
||||
# Apply compression if session data is large
|
||||
if len(analytics_data) > 10000: # 10KB threshold
|
||||
compression_result = self.compression_engine.compress_content(
|
||||
analytics_data,
|
||||
context,
|
||||
{'content_type': 'session_data'}
|
||||
)
|
||||
persistence_result['compression_applied'] = True
|
||||
persistence_result['compression_ratio'] = compression_result.compression_ratio
|
||||
|
||||
# Simulate saving (real implementation would use actual storage)
|
||||
cache_dir = Path(os.path.expanduser("~/.claude/cache"))
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
session_file = cache_dir / f"session_{context['session_id']}.json"
|
||||
|
||||
with open(session_file, 'w') as f:
|
||||
f.write(analytics_data)
|
||||
|
||||
persistence_result['session_data_saved'] = True
|
||||
persistence_result['analytics_saved'] = True
|
||||
|
||||
# Learning data is automatically saved by learning engine
|
||||
persistence_result['learning_data_saved'] = True
|
||||
|
||||
# Optimize storage by cleaning old sessions
|
||||
self._cleanup_old_sessions(cache_dir)
|
||||
persistence_result['storage_optimized'] = True
|
||||
|
||||
except Exception as e:
|
||||
persistence_result['error'] = str(e)
|
||||
persistence_result['persistence_enabled'] = False
|
||||
|
||||
return persistence_result
|
||||
|
||||
def _cleanup_old_sessions(self, cache_dir: Path):
|
||||
"""Clean up old session files to optimize storage."""
|
||||
session_files = list(cache_dir.glob("session_*.json"))
|
||||
|
||||
# Keep only the most recent 50 sessions
|
||||
if len(session_files) > 50:
|
||||
session_files.sort(key=lambda f: f.stat().st_mtime, reverse=True)
|
||||
for old_file in session_files[50:]:
|
||||
try:
|
||||
old_file.unlink()
|
||||
except:
|
||||
pass # Ignore cleanup errors
|
||||
|
||||
def _generate_recommendations(self, context: dict, performance_analysis: dict,
|
||||
learning_consolidation: dict) -> dict:
|
||||
"""Generate recommendations for future sessions."""
|
||||
recommendations = {
|
||||
'performance_improvements': [],
|
||||
'superclaude_optimizations': [],
|
||||
'learning_suggestions': [],
|
||||
'workflow_enhancements': []
|
||||
}
|
||||
|
||||
# Performance recommendations
|
||||
if performance_analysis['overall_score'] < 0.7:
|
||||
recommendations['performance_improvements'].extend([
|
||||
'Focus on reducing error rate through validation',
|
||||
'Consider enabling more SuperClaude intelligence features',
|
||||
'Optimize tool selection and usage patterns'
|
||||
])
|
||||
|
||||
# SuperClaude optimization recommendations
|
||||
if context.get('superclaude_enabled') and context.get('superclaude_effectiveness', 0) < 0.6:
|
||||
recommendations['superclaude_optimizations'].extend([
|
||||
'Enable more MCP servers for better intelligence',
|
||||
'Use delegation features for complex operations',
|
||||
'Activate compression for resource optimization'
|
||||
])
|
||||
elif not context.get('superclaude_enabled'):
|
||||
recommendations['superclaude_optimizations'].append(
|
||||
'Consider enabling SuperClaude framework for enhanced productivity'
|
||||
)
|
||||
|
||||
# Learning suggestions
|
||||
if learning_consolidation['total_learning_events'] < 3:
|
||||
recommendations['learning_suggestions'].append(
|
||||
'Engage with more complex operations to improve system learning'
|
||||
)
|
||||
|
||||
# Workflow enhancements
|
||||
if context.get('error_rate', 0) > 0.1:
|
||||
recommendations['workflow_enhancements'].extend([
|
||||
'Use validation hooks to catch errors early',
|
||||
'Enable pre-tool-use intelligence for better routing'
|
||||
])
|
||||
|
||||
return recommendations
|
||||
|
||||
def _create_final_learning_events(self, context: dict, session_analytics: dict):
|
||||
"""Create final learning events for the session."""
|
||||
# Record overall session effectiveness
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.USER_PREFERENCE,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'session_pattern': 'completion',
|
||||
'satisfaction_score': session_analytics['performance_metrics']['satisfaction_estimate'],
|
||||
'productivity_achieved': session_analytics['performance_metrics']['productivity_score'],
|
||||
'superclaude_usage': context.get('superclaude_enabled', False)
|
||||
},
|
||||
session_analytics['performance_metrics']['overall_score'],
|
||||
1.0, # High confidence in final session metrics
|
||||
{'hook': 'stop', 'final_learning': True}
|
||||
)
|
||||
|
||||
def _calculate_session_efficiency(self, session_analytics: dict) -> float:
|
||||
"""Calculate overall session efficiency score."""
|
||||
performance_metrics = session_analytics.get('performance_metrics', {})
|
||||
|
||||
efficiency_components = [
|
||||
performance_metrics.get('productivity_score', 0),
|
||||
performance_metrics.get('quality_score', 0),
|
||||
performance_metrics.get('efficiency_score', 0),
|
||||
session_analytics.get('superclaude_effectiveness', {}).get('effectiveness_score', 0)
|
||||
]
|
||||
|
||||
return statistics.mean([comp for comp in efficiency_components if comp > 0])
|
||||
|
||||
def _generate_session_report(self, context: dict, session_analytics: dict,
|
||||
persistence_result: dict, recommendations: dict) -> dict:
|
||||
"""Generate final session report."""
|
||||
return {
|
||||
'session_id': context['session_id'],
|
||||
'session_completed': True,
|
||||
'completion_timestamp': context.get('session_end_time', time.time()),
|
||||
|
||||
'analytics': session_analytics,
|
||||
'persistence': persistence_result,
|
||||
'recommendations': recommendations,
|
||||
|
||||
'summary': {
|
||||
'session_success': session_analytics['performance_metrics']['overall_score'] > 0.6,
|
||||
'superclaude_effective': session_analytics['superclaude_effectiveness']['effectiveness_score'] > 0.6,
|
||||
'learning_achieved': session_analytics['learning_summary']['insights_generated'] > 0,
|
||||
'recommendations_generated': sum(len(recs) for recs in recommendations.values()) > 0
|
||||
},
|
||||
|
||||
'next_session_preparation': {
|
||||
'enable_superclaude': True,
|
||||
'suggested_optimizations': recommendations.get('superclaude_optimizations', [])[:2],
|
||||
'learning_focus_areas': [insight['insight_type'] for insight in
|
||||
session_analytics['learning_summary']['key_insights']]
|
||||
},
|
||||
|
||||
'metadata': {
|
||||
'hook_version': 'stop_1.0',
|
||||
'report_timestamp': time.time(),
|
||||
'analytics_comprehensive': True
|
||||
}
|
||||
}
|
||||
|
||||
def _create_fallback_report(self, session_data: dict, error: str) -> dict:
|
||||
"""Create fallback session report on error."""
|
||||
return {
|
||||
'session_id': session_data.get('session_id', 'unknown'),
|
||||
'session_completed': False,
|
||||
'error': error,
|
||||
'fallback_mode': True,
|
||||
|
||||
'analytics': {
|
||||
'session_summary': {
|
||||
'session_id': session_data.get('session_id', 'unknown'),
|
||||
'error_occurred': True
|
||||
},
|
||||
'performance_metrics': {
|
||||
'overall_score': 0.0
|
||||
}
|
||||
},
|
||||
|
||||
'persistence': {
|
||||
'persistence_enabled': False,
|
||||
'error': error
|
||||
},
|
||||
|
||||
'performance_metrics': {
|
||||
'stop_processing_time_ms': 0,
|
||||
'target_met': False,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
"""Main hook execution function."""
|
||||
try:
|
||||
# Read session data from stdin
|
||||
session_data = json.loads(sys.stdin.read())
|
||||
|
||||
# Initialize and run hook
|
||||
hook = StopHook()
|
||||
result = hook.process_session_stop(session_data)
|
||||
|
||||
# Output result as JSON
|
||||
print(json.dumps(result, indent=2))
|
||||
|
||||
except Exception as e:
|
||||
# Output error as JSON
|
||||
error_result = {
|
||||
'session_completed': False,
|
||||
'error': str(e),
|
||||
'fallback_mode': True
|
||||
}
|
||||
print(json.dumps(error_result, indent=2))
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,771 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
SuperClaude-Lite Subagent Stop Hook
|
||||
|
||||
Implements MODE_Task_Management delegation coordination and analytics.
|
||||
Performance target: <150ms execution time.
|
||||
|
||||
This hook runs when subagents complete tasks and provides:
|
||||
- Subagent performance analytics and coordination metrics
|
||||
- Task delegation effectiveness measurement
|
||||
- Cross-agent learning and adaptation
|
||||
- Wave orchestration optimization
|
||||
- Parallel execution performance tracking
|
||||
"""
|
||||
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, List, Optional
|
||||
import statistics
|
||||
|
||||
# Add shared modules to path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "shared"))
|
||||
|
||||
from framework_logic import FrameworkLogic
|
||||
from pattern_detection import PatternDetector
|
||||
from mcp_intelligence import MCPIntelligence
|
||||
from compression_engine import CompressionEngine
|
||||
from learning_engine import LearningEngine, LearningType, AdaptationScope
|
||||
from yaml_loader import config_loader
|
||||
from logger import log_hook_start, log_hook_end, log_decision, log_error
|
||||
|
||||
|
||||
class SubagentStopHook:
|
||||
"""
|
||||
Subagent stop hook implementing task management coordination.
|
||||
|
||||
Responsibilities:
|
||||
- Analyze subagent task completion and performance
|
||||
- Measure delegation effectiveness and coordination success
|
||||
- Learn from parallel execution patterns
|
||||
- Optimize wave orchestration strategies
|
||||
- Coordinate cross-agent knowledge sharing
|
||||
- Track task management framework effectiveness
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
start_time = time.time()
|
||||
|
||||
# Initialize core components
|
||||
self.framework_logic = FrameworkLogic()
|
||||
self.pattern_detector = PatternDetector()
|
||||
self.mcp_intelligence = MCPIntelligence()
|
||||
self.compression_engine = CompressionEngine()
|
||||
|
||||
# Initialize learning engine with installation directory cache
|
||||
import os
|
||||
cache_dir = Path(os.path.expanduser("~/.claude/cache"))
|
||||
cache_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.learning_engine = LearningEngine(cache_dir)
|
||||
|
||||
# Load task management configuration
|
||||
self.task_config = config_loader.get_section('session', 'task_management', {})
|
||||
|
||||
# Load hook-specific configuration from SuperClaude config
|
||||
self.hook_config = config_loader.get_hook_config('subagent_stop')
|
||||
|
||||
# Performance tracking using configuration
|
||||
self.initialization_time = (time.time() - start_time) * 1000
|
||||
self.performance_target_ms = config_loader.get_hook_config('subagent_stop', 'performance_target_ms', 150)
|
||||
|
||||
def process_subagent_stop(self, subagent_data: dict) -> dict:
|
||||
"""
|
||||
Process subagent completion with coordination analytics.
|
||||
|
||||
Args:
|
||||
subagent_data: Subagent completion data from Claude Code
|
||||
|
||||
Returns:
|
||||
Coordination analytics with delegation effectiveness and optimization insights
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
# Log hook start
|
||||
log_hook_start("subagent_stop", {
|
||||
"subagent_id": subagent_data.get('subagent_id', ''),
|
||||
"task_id": subagent_data.get('task_id', ''),
|
||||
"task_type": subagent_data.get('task_type', 'unknown'),
|
||||
"delegation_strategy": subagent_data.get('delegation_strategy', 'unknown'),
|
||||
"parallel_tasks": len(subagent_data.get('parallel_tasks', [])),
|
||||
"wave_context": subagent_data.get('wave_context', {})
|
||||
})
|
||||
|
||||
try:
|
||||
# Extract subagent context
|
||||
context = self._extract_subagent_context(subagent_data)
|
||||
|
||||
# Analyze task completion performance
|
||||
task_analysis = self._analyze_task_completion(context)
|
||||
|
||||
# Log task completion analysis
|
||||
log_decision(
|
||||
"subagent_stop",
|
||||
"task_completion",
|
||||
"completed" if task_analysis['completion_success'] else "failed",
|
||||
f"Quality: {task_analysis['completion_quality']:.2f}, Efficiency: {task_analysis['completion_efficiency']:.2f}"
|
||||
)
|
||||
|
||||
# Measure delegation effectiveness
|
||||
delegation_analysis = self._analyze_delegation_effectiveness(context, task_analysis)
|
||||
|
||||
# Log delegation effectiveness
|
||||
log_decision(
|
||||
"subagent_stop",
|
||||
"delegation_effectiveness",
|
||||
f"{delegation_analysis['delegation_value']:.2f}",
|
||||
f"Strategy: {delegation_analysis['delegation_strategy']}, Overhead: {delegation_analysis['coordination_overhead']:.1%}"
|
||||
)
|
||||
|
||||
# Analyze coordination patterns
|
||||
coordination_analysis = self._analyze_coordination_patterns(context, delegation_analysis)
|
||||
|
||||
# Generate optimization recommendations
|
||||
optimization_insights = self._generate_optimization_insights(
|
||||
context, task_analysis, delegation_analysis, coordination_analysis
|
||||
)
|
||||
|
||||
# Record coordination learning
|
||||
self._record_coordination_learning(context, delegation_analysis, optimization_insights)
|
||||
|
||||
# Update wave orchestration metrics
|
||||
wave_metrics = self._update_wave_orchestration_metrics(context, coordination_analysis)
|
||||
|
||||
# Log wave orchestration if applicable
|
||||
if context.get('wave_total', 1) > 1:
|
||||
log_decision(
|
||||
"subagent_stop",
|
||||
"wave_orchestration",
|
||||
f"wave_{context.get('wave_position', 0) + 1}_of_{context.get('wave_total', 1)}",
|
||||
f"Performance: {wave_metrics['wave_performance']:.2f}, Efficiency: {wave_metrics['orchestration_efficiency']:.2f}"
|
||||
)
|
||||
|
||||
# Generate coordination report
|
||||
coordination_report = self._generate_coordination_report(
|
||||
context, task_analysis, delegation_analysis, coordination_analysis,
|
||||
optimization_insights, wave_metrics
|
||||
)
|
||||
|
||||
# Performance tracking
|
||||
execution_time = (time.time() - start_time) * 1000
|
||||
coordination_report['performance_metrics'] = {
|
||||
'coordination_analysis_time_ms': execution_time,
|
||||
'target_met': execution_time < self.performance_target_ms,
|
||||
'coordination_efficiency': self._calculate_coordination_efficiency(context, execution_time)
|
||||
}
|
||||
|
||||
# Log hook end with success
|
||||
log_hook_end(
|
||||
"subagent_stop",
|
||||
int(execution_time),
|
||||
True,
|
||||
{
|
||||
"task_success": task_analysis['completion_success'],
|
||||
"delegation_value": delegation_analysis['delegation_value'],
|
||||
"coordination_strategy": coordination_analysis['coordination_strategy'],
|
||||
"wave_enabled": context.get('wave_total', 1) > 1,
|
||||
"performance_target_met": execution_time < self.performance_target_ms
|
||||
}
|
||||
)
|
||||
|
||||
return coordination_report
|
||||
|
||||
except Exception as e:
|
||||
# Log error
|
||||
log_error("subagent_stop", str(e), {"subagent_data": subagent_data})
|
||||
|
||||
# Log hook end with failure
|
||||
log_hook_end("subagent_stop", int((time.time() - start_time) * 1000), False)
|
||||
|
||||
# Graceful fallback on error
|
||||
return self._create_fallback_report(subagent_data, str(e))
|
||||
|
||||
def _extract_subagent_context(self, subagent_data: dict) -> dict:
|
||||
"""Extract and enrich subagent context."""
|
||||
context = {
|
||||
'subagent_id': subagent_data.get('subagent_id', ''),
|
||||
'parent_session_id': subagent_data.get('parent_session_id', ''),
|
||||
'task_id': subagent_data.get('task_id', ''),
|
||||
'task_type': subagent_data.get('task_type', 'unknown'),
|
||||
'delegation_strategy': subagent_data.get('delegation_strategy', 'unknown'),
|
||||
'execution_time_ms': subagent_data.get('execution_time_ms', 0),
|
||||
'task_result': subagent_data.get('result', {}),
|
||||
'task_status': subagent_data.get('status', 'unknown'),
|
||||
'resources_used': subagent_data.get('resources', {}),
|
||||
'coordination_data': subagent_data.get('coordination', {}),
|
||||
'parallel_tasks': subagent_data.get('parallel_tasks', []),
|
||||
'wave_context': subagent_data.get('wave_context', {}),
|
||||
'completion_timestamp': time.time()
|
||||
}
|
||||
|
||||
# Analyze task characteristics
|
||||
context.update(self._analyze_task_characteristics(context))
|
||||
|
||||
# Extract coordination metrics
|
||||
context.update(self._extract_coordination_metrics(context))
|
||||
|
||||
return context
|
||||
|
||||
def _analyze_task_characteristics(self, context: dict) -> dict:
|
||||
"""Analyze characteristics of the completed task."""
|
||||
task_result = context.get('task_result', {})
|
||||
|
||||
characteristics = {
|
||||
'task_complexity': self._calculate_task_complexity(context),
|
||||
'task_success': context.get('task_status') == 'completed',
|
||||
'partial_success': context.get('task_status') == 'partial',
|
||||
'task_error': context.get('task_status') == 'error',
|
||||
'output_quality': self._assess_output_quality(task_result),
|
||||
'resource_efficiency': self._calculate_resource_efficiency(context),
|
||||
'coordination_required': len(context.get('parallel_tasks', [])) > 0
|
||||
}
|
||||
|
||||
return characteristics
|
||||
|
||||
def _calculate_task_complexity(self, context: dict) -> float:
|
||||
"""Calculate task complexity score (0.0 to 1.0)."""
|
||||
complexity_factors = []
|
||||
|
||||
# Task type complexity
|
||||
task_type = context.get('task_type', 'unknown')
|
||||
type_complexity = {
|
||||
'file_analysis': 0.3,
|
||||
'code_generation': 0.6,
|
||||
'multi_file_edit': 0.7,
|
||||
'architecture_analysis': 0.9,
|
||||
'system_refactor': 1.0
|
||||
}
|
||||
complexity_factors.append(type_complexity.get(task_type, 0.5))
|
||||
|
||||
# Execution time complexity
|
||||
execution_time = context.get('execution_time_ms', 0)
|
||||
if execution_time > 0:
|
||||
# Normalize to 5 seconds as high complexity
|
||||
time_complexity = min(execution_time / 5000, 1.0)
|
||||
complexity_factors.append(time_complexity)
|
||||
|
||||
# Resource usage complexity
|
||||
resources = context.get('resources_used', {})
|
||||
if resources:
|
||||
resource_complexity = max(
|
||||
resources.get('memory_mb', 0) / 1000, # 1GB = high
|
||||
resources.get('cpu_percent', 0) / 100
|
||||
)
|
||||
complexity_factors.append(min(resource_complexity, 1.0))
|
||||
|
||||
# Coordination complexity
|
||||
if context.get('coordination_required'):
|
||||
complexity_factors.append(0.4) # Coordination adds complexity
|
||||
|
||||
return statistics.mean(complexity_factors) if complexity_factors else 0.5
|
||||
|
||||
def _assess_output_quality(self, task_result: dict) -> float:
|
||||
"""Assess quality of task output (0.0 to 1.0)."""
|
||||
if not task_result:
|
||||
return 0.0
|
||||
|
||||
quality_indicators = []
|
||||
|
||||
# Check for quality metrics in result
|
||||
if 'quality_score' in task_result:
|
||||
quality_indicators.append(task_result['quality_score'])
|
||||
|
||||
# Check for validation results
|
||||
if task_result.get('validation_passed'):
|
||||
quality_indicators.append(0.8)
|
||||
elif task_result.get('validation_failed'):
|
||||
quality_indicators.append(0.3)
|
||||
|
||||
# Check for error indicators
|
||||
if task_result.get('errors'):
|
||||
error_penalty = min(len(task_result['errors']) * 0.2, 0.6)
|
||||
quality_indicators.append(1.0 - error_penalty)
|
||||
|
||||
# Check for completeness
|
||||
if task_result.get('completeness_ratio'):
|
||||
quality_indicators.append(task_result['completeness_ratio'])
|
||||
|
||||
# Default quality estimation
|
||||
if not quality_indicators:
|
||||
# Estimate quality from task status
|
||||
status = task_result.get('status', 'unknown')
|
||||
if status == 'success':
|
||||
quality_indicators.append(0.8)
|
||||
elif status == 'partial':
|
||||
quality_indicators.append(0.6)
|
||||
else:
|
||||
quality_indicators.append(0.4)
|
||||
|
||||
return statistics.mean(quality_indicators)
|
||||
|
||||
def _calculate_resource_efficiency(self, context: dict) -> float:
|
||||
"""Calculate resource usage efficiency."""
|
||||
resources = context.get('resources_used', {})
|
||||
execution_time = context.get('execution_time_ms', 1)
|
||||
|
||||
if not resources:
|
||||
return 0.7 # Assume moderate efficiency
|
||||
|
||||
# Memory efficiency (lower usage = higher efficiency)
|
||||
memory_mb = resources.get('memory_mb', 100)
|
||||
memory_efficiency = max(1.0 - (memory_mb / 1000), 0.1) # Penalty above 1GB
|
||||
|
||||
# CPU efficiency (moderate usage is optimal)
|
||||
cpu_percent = resources.get('cpu_percent', 50)
|
||||
if cpu_percent < 30:
|
||||
cpu_efficiency = cpu_percent / 30 # Underutilization penalty
|
||||
elif cpu_percent > 80:
|
||||
cpu_efficiency = (100 - cpu_percent) / 20 # Overutilization penalty
|
||||
else:
|
||||
cpu_efficiency = 1.0 # Optimal range
|
||||
|
||||
# Time efficiency (faster is better, but not at quality cost)
|
||||
expected_time = resources.get('expected_time_ms', execution_time)
|
||||
if expected_time > 0:
|
||||
time_efficiency = min(expected_time / execution_time, 1.0)
|
||||
else:
|
||||
time_efficiency = 0.8
|
||||
|
||||
return (memory_efficiency + cpu_efficiency + time_efficiency) / 3
|
||||
|
||||
def _extract_coordination_metrics(self, context: dict) -> dict:
|
||||
"""Extract coordination-specific metrics."""
|
||||
coordination_data = context.get('coordination_data', {})
|
||||
|
||||
return {
|
||||
'coordination_overhead_ms': coordination_data.get('overhead_ms', 0),
|
||||
'synchronization_points': coordination_data.get('sync_points', 0),
|
||||
'data_exchange_size': coordination_data.get('data_exchange_bytes', 0),
|
||||
'coordination_success': coordination_data.get('success', True),
|
||||
'parallel_efficiency': coordination_data.get('parallel_efficiency', 1.0),
|
||||
'wave_position': context.get('wave_context', {}).get('position', 0),
|
||||
'wave_total': context.get('wave_context', {}).get('total_waves', 1)
|
||||
}
|
||||
|
||||
def _analyze_task_completion(self, context: dict) -> dict:
|
||||
"""Analyze task completion performance."""
|
||||
task_analysis = {
|
||||
'completion_success': context.get('task_success', False),
|
||||
'completion_quality': context.get('output_quality', 0.0),
|
||||
'completion_efficiency': context.get('resource_efficiency', 0.0),
|
||||
'completion_time_performance': 0.0,
|
||||
'error_analysis': {},
|
||||
'success_factors': [],
|
||||
'improvement_areas': []
|
||||
}
|
||||
|
||||
# Time performance analysis
|
||||
execution_time = context.get('execution_time_ms', 0)
|
||||
task_type = context.get('task_type', 'unknown')
|
||||
|
||||
# Expected times by task type (rough estimates)
|
||||
expected_times = {
|
||||
'file_analysis': 500,
|
||||
'code_generation': 2000,
|
||||
'multi_file_edit': 1500,
|
||||
'architecture_analysis': 3000,
|
||||
'system_refactor': 5000
|
||||
}
|
||||
|
||||
expected_time = expected_times.get(task_type, 1000)
|
||||
if execution_time > 0:
|
||||
task_analysis['completion_time_performance'] = min(expected_time / execution_time, 1.0)
|
||||
|
||||
# Success factor identification
|
||||
if task_analysis['completion_success']:
|
||||
if task_analysis['completion_quality'] > 0.8:
|
||||
task_analysis['success_factors'].append('high_output_quality')
|
||||
if task_analysis['completion_efficiency'] > 0.8:
|
||||
task_analysis['success_factors'].append('efficient_resource_usage')
|
||||
if task_analysis['completion_time_performance'] > 0.8:
|
||||
task_analysis['success_factors'].append('fast_execution')
|
||||
|
||||
# Improvement area identification
|
||||
if task_analysis['completion_quality'] < 0.6:
|
||||
task_analysis['improvement_areas'].append('output_quality')
|
||||
if task_analysis['completion_efficiency'] < 0.6:
|
||||
task_analysis['improvement_areas'].append('resource_efficiency')
|
||||
if task_analysis['completion_time_performance'] < 0.6:
|
||||
task_analysis['improvement_areas'].append('execution_speed')
|
||||
|
||||
return task_analysis
|
||||
|
||||
def _analyze_delegation_effectiveness(self, context: dict, task_analysis: dict) -> dict:
|
||||
"""Analyze effectiveness of task delegation."""
|
||||
delegation_analysis = {
|
||||
'delegation_strategy': context.get('delegation_strategy', 'unknown'),
|
||||
'delegation_success': context.get('task_success', False),
|
||||
'delegation_efficiency': 0.0,
|
||||
'coordination_overhead': 0.0,
|
||||
'parallel_benefit': 0.0,
|
||||
'delegation_value': 0.0
|
||||
}
|
||||
|
||||
# Calculate delegation efficiency
|
||||
coordination_overhead = context.get('coordination_overhead_ms', 0)
|
||||
execution_time = context.get('execution_time_ms', 1)
|
||||
|
||||
if execution_time > 0:
|
||||
delegation_analysis['coordination_overhead'] = coordination_overhead / execution_time
|
||||
delegation_analysis['delegation_efficiency'] = max(
|
||||
1.0 - delegation_analysis['coordination_overhead'], 0.0
|
||||
)
|
||||
|
||||
# Calculate parallel benefit
|
||||
parallel_tasks = context.get('parallel_tasks', [])
|
||||
if len(parallel_tasks) > 1:
|
||||
# Estimate parallel benefit based on task coordination
|
||||
parallel_efficiency = context.get('parallel_efficiency', 1.0)
|
||||
theoretical_speedup = len(parallel_tasks)
|
||||
actual_speedup = theoretical_speedup * parallel_efficiency
|
||||
delegation_analysis['parallel_benefit'] = actual_speedup / theoretical_speedup
|
||||
|
||||
# Overall delegation value
|
||||
quality_factor = task_analysis['completion_quality']
|
||||
efficiency_factor = delegation_analysis['delegation_efficiency']
|
||||
parallel_factor = delegation_analysis['parallel_benefit'] if parallel_tasks else 1.0
|
||||
|
||||
delegation_analysis['delegation_value'] = (
|
||||
quality_factor * 0.4 +
|
||||
efficiency_factor * 0.3 +
|
||||
parallel_factor * 0.3
|
||||
)
|
||||
|
||||
return delegation_analysis
|
||||
|
||||
def _analyze_coordination_patterns(self, context: dict, delegation_analysis: dict) -> dict:
|
||||
"""Analyze coordination patterns and effectiveness."""
|
||||
coordination_analysis = {
|
||||
'coordination_strategy': 'unknown',
|
||||
'synchronization_effectiveness': 0.0,
|
||||
'data_flow_efficiency': 0.0,
|
||||
'wave_coordination_success': 0.0,
|
||||
'cross_agent_learning': 0.0,
|
||||
'coordination_patterns_detected': []
|
||||
}
|
||||
|
||||
# Determine coordination strategy
|
||||
if context.get('wave_total', 1) > 1:
|
||||
coordination_analysis['coordination_strategy'] = 'wave_orchestration'
|
||||
elif len(context.get('parallel_tasks', [])) > 1:
|
||||
coordination_analysis['coordination_strategy'] = 'parallel_coordination'
|
||||
else:
|
||||
coordination_analysis['coordination_strategy'] = 'single_agent'
|
||||
|
||||
# Synchronization effectiveness
|
||||
sync_points = context.get('synchronization_points', 0)
|
||||
coordination_success = context.get('coordination_success', True)
|
||||
|
||||
if sync_points > 0 and coordination_success:
|
||||
coordination_analysis['synchronization_effectiveness'] = 1.0
|
||||
elif sync_points > 0:
|
||||
coordination_analysis['synchronization_effectiveness'] = 0.5
|
||||
else:
|
||||
coordination_analysis['synchronization_effectiveness'] = 0.8 # No sync needed
|
||||
|
||||
# Data flow efficiency
|
||||
data_exchange = context.get('data_exchange_size', 0)
|
||||
if data_exchange > 0:
|
||||
# Efficiency based on data size (smaller is more efficient)
|
||||
coordination_analysis['data_flow_efficiency'] = max(1.0 - (data_exchange / 1000000), 0.1) # 1MB threshold
|
||||
else:
|
||||
coordination_analysis['data_flow_efficiency'] = 1.0 # No data exchange needed
|
||||
|
||||
# Wave coordination success
|
||||
wave_position = context.get('wave_position', 0)
|
||||
wave_total = context.get('wave_total', 1)
|
||||
|
||||
if wave_total > 1:
|
||||
# Success based on position completion and delegation value
|
||||
wave_progress = (wave_position + 1) / wave_total
|
||||
delegation_value = delegation_analysis.get('delegation_value', 0)
|
||||
coordination_analysis['wave_coordination_success'] = (wave_progress + delegation_value) / 2
|
||||
else:
|
||||
coordination_analysis['wave_coordination_success'] = 1.0
|
||||
|
||||
# Detect coordination patterns
|
||||
if delegation_analysis['delegation_value'] > 0.8:
|
||||
coordination_analysis['coordination_patterns_detected'].append('effective_delegation')
|
||||
|
||||
if coordination_analysis['synchronization_effectiveness'] > 0.8:
|
||||
coordination_analysis['coordination_patterns_detected'].append('efficient_synchronization')
|
||||
|
||||
if coordination_analysis['wave_coordination_success'] > 0.8:
|
||||
coordination_analysis['coordination_patterns_detected'].append('successful_wave_orchestration')
|
||||
|
||||
# Log detected patterns if any
|
||||
if coordination_analysis['coordination_patterns_detected']:
|
||||
log_decision(
|
||||
"subagent_stop",
|
||||
"coordination_patterns",
|
||||
str(len(coordination_analysis['coordination_patterns_detected'])),
|
||||
f"Patterns: {', '.join(coordination_analysis['coordination_patterns_detected'])}"
|
||||
)
|
||||
|
||||
return coordination_analysis
|
||||
|
||||
def _generate_optimization_insights(self, context: dict, task_analysis: dict,
|
||||
delegation_analysis: dict, coordination_analysis: dict) -> dict:
|
||||
"""Generate optimization insights for future delegations."""
|
||||
insights = {
|
||||
'delegation_optimizations': [],
|
||||
'coordination_improvements': [],
|
||||
'wave_strategy_recommendations': [],
|
||||
'performance_enhancements': [],
|
||||
'learning_opportunities': []
|
||||
}
|
||||
|
||||
# Delegation optimizations
|
||||
if delegation_analysis['delegation_value'] < 0.6:
|
||||
insights['delegation_optimizations'].extend([
|
||||
'Consider alternative delegation strategies',
|
||||
'Reduce coordination overhead',
|
||||
'Improve task partitioning'
|
||||
])
|
||||
|
||||
if delegation_analysis['coordination_overhead'] > 0.3:
|
||||
insights['delegation_optimizations'].append('Minimize coordination overhead')
|
||||
|
||||
# Coordination improvements
|
||||
if coordination_analysis['synchronization_effectiveness'] < 0.7:
|
||||
insights['coordination_improvements'].append('Improve synchronization mechanisms')
|
||||
|
||||
if coordination_analysis['data_flow_efficiency'] < 0.7:
|
||||
insights['coordination_improvements'].append('Optimize data exchange patterns')
|
||||
|
||||
# Wave strategy recommendations
|
||||
wave_success = coordination_analysis['wave_coordination_success']
|
||||
if wave_success < 0.6 and context.get('wave_total', 1) > 1:
|
||||
insights['wave_strategy_recommendations'].extend([
|
||||
'Adjust wave orchestration strategy',
|
||||
'Consider different task distribution',
|
||||
'Improve wave synchronization'
|
||||
])
|
||||
elif wave_success > 0.8:
|
||||
insights['wave_strategy_recommendations'].append('Wave orchestration working well - maintain strategy')
|
||||
|
||||
# Performance enhancements
|
||||
if task_analysis['completion_time_performance'] < 0.6:
|
||||
insights['performance_enhancements'].append('Optimize task execution speed')
|
||||
|
||||
if task_analysis['completion_efficiency'] < 0.6:
|
||||
insights['performance_enhancements'].append('Improve resource utilization')
|
||||
|
||||
return insights
|
||||
|
||||
def _record_coordination_learning(self, context: dict, delegation_analysis: dict,
|
||||
optimization_insights: dict):
|
||||
"""Record coordination learning for future optimization."""
|
||||
# Record delegation effectiveness
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.PERFORMANCE_OPTIMIZATION,
|
||||
AdaptationScope.PROJECT,
|
||||
context,
|
||||
{
|
||||
'delegation_strategy': context.get('delegation_strategy'),
|
||||
'task_type': context.get('task_type'),
|
||||
'delegation_value': delegation_analysis['delegation_value'],
|
||||
'coordination_overhead': delegation_analysis['coordination_overhead'],
|
||||
'parallel_benefit': delegation_analysis['parallel_benefit']
|
||||
},
|
||||
delegation_analysis['delegation_value'],
|
||||
0.8,
|
||||
{'hook': 'subagent_stop', 'coordination_learning': True}
|
||||
)
|
||||
|
||||
# Record task pattern learning
|
||||
if context.get('task_success'):
|
||||
self.learning_engine.record_learning_event(
|
||||
LearningType.OPERATION_PATTERN,
|
||||
AdaptationScope.USER,
|
||||
context,
|
||||
{
|
||||
'successful_task_pattern': context.get('task_type'),
|
||||
'success_factors': optimization_insights.get('performance_enhancements', []),
|
||||
'delegation_effective': delegation_analysis['delegation_value'] > 0.7
|
||||
},
|
||||
delegation_analysis['delegation_value'],
|
||||
0.9,
|
||||
{'task_success_pattern': True}
|
||||
)
|
||||
|
||||
def _update_wave_orchestration_metrics(self, context: dict, coordination_analysis: dict) -> dict:
|
||||
"""Update wave orchestration performance metrics."""
|
||||
wave_metrics = {
|
||||
'wave_performance': 0.0,
|
||||
'orchestration_efficiency': 0.0,
|
||||
'wave_learning_value': 0.0,
|
||||
'next_wave_recommendations': []
|
||||
}
|
||||
|
||||
if context.get('wave_total', 1) > 1:
|
||||
wave_success = coordination_analysis['wave_coordination_success']
|
||||
wave_metrics['wave_performance'] = wave_success
|
||||
|
||||
# Calculate orchestration efficiency
|
||||
coordination_overhead = context.get('coordination_overhead_ms', 0)
|
||||
execution_time = context.get('execution_time_ms', 1)
|
||||
|
||||
if execution_time > 0:
|
||||
wave_metrics['orchestration_efficiency'] = max(
|
||||
1.0 - (coordination_overhead / execution_time), 0.0
|
||||
)
|
||||
|
||||
# Learning value from wave coordination
|
||||
wave_metrics['wave_learning_value'] = wave_success * 0.8 # Waves provide valuable learning
|
||||
|
||||
# Next wave recommendations
|
||||
if wave_success > 0.8:
|
||||
wave_metrics['next_wave_recommendations'].append('Continue current wave strategy')
|
||||
else:
|
||||
wave_metrics['next_wave_recommendations'].extend([
|
||||
'Adjust wave coordination strategy',
|
||||
'Improve inter-wave communication'
|
||||
])
|
||||
|
||||
return wave_metrics
|
||||
|
||||
def _calculate_coordination_efficiency(self, context: dict, execution_time_ms: float) -> float:
|
||||
"""Calculate coordination processing efficiency."""
|
||||
# Efficiency based on coordination overhead vs processing time
|
||||
coordination_overhead = context.get('coordination_overhead_ms', 0)
|
||||
task_execution_time = context.get('execution_time_ms', 1)
|
||||
|
||||
if task_execution_time > 0:
|
||||
coordination_ratio = coordination_overhead / task_execution_time
|
||||
coordination_efficiency = max(1.0 - coordination_ratio, 0.0)
|
||||
else:
|
||||
coordination_efficiency = 0.8
|
||||
|
||||
# Processing time efficiency
|
||||
processing_efficiency = min(100 / max(execution_time_ms, 1), 1.0) # Target: 100ms
|
||||
|
||||
return (coordination_efficiency + processing_efficiency) / 2
|
||||
|
||||
def _generate_coordination_report(self, context: dict, task_analysis: dict,
|
||||
delegation_analysis: dict, coordination_analysis: dict,
|
||||
optimization_insights: dict, wave_metrics: dict) -> dict:
|
||||
"""Generate comprehensive coordination report."""
|
||||
return {
|
||||
'subagent_id': context['subagent_id'],
|
||||
'task_id': context['task_id'],
|
||||
'completion_timestamp': context['completion_timestamp'],
|
||||
|
||||
'task_completion': {
|
||||
'success': task_analysis['completion_success'],
|
||||
'quality_score': task_analysis['completion_quality'],
|
||||
'efficiency_score': task_analysis['completion_efficiency'],
|
||||
'time_performance': task_analysis['completion_time_performance'],
|
||||
'success_factors': task_analysis['success_factors'],
|
||||
'improvement_areas': task_analysis['improvement_areas']
|
||||
},
|
||||
|
||||
'delegation_analysis': {
|
||||
'strategy': delegation_analysis['delegation_strategy'],
|
||||
'effectiveness': delegation_analysis['delegation_value'],
|
||||
'efficiency': delegation_analysis['delegation_efficiency'],
|
||||
'coordination_overhead': delegation_analysis['coordination_overhead'],
|
||||
'parallel_benefit': delegation_analysis['parallel_benefit']
|
||||
},
|
||||
|
||||
'coordination_metrics': {
|
||||
'strategy': coordination_analysis['coordination_strategy'],
|
||||
'synchronization_effectiveness': coordination_analysis['synchronization_effectiveness'],
|
||||
'data_flow_efficiency': coordination_analysis['data_flow_efficiency'],
|
||||
'patterns_detected': coordination_analysis['coordination_patterns_detected']
|
||||
},
|
||||
|
||||
'wave_orchestration': {
|
||||
'enabled': context.get('wave_total', 1) > 1,
|
||||
'wave_position': context.get('wave_position', 0),
|
||||
'total_waves': context.get('wave_total', 1),
|
||||
'wave_performance': wave_metrics['wave_performance'],
|
||||
'orchestration_efficiency': wave_metrics['orchestration_efficiency'],
|
||||
'learning_value': wave_metrics['wave_learning_value']
|
||||
},
|
||||
|
||||
'optimization_insights': optimization_insights,
|
||||
|
||||
'performance_summary': {
|
||||
'overall_effectiveness': (
|
||||
task_analysis['completion_quality'] * 0.4 +
|
||||
delegation_analysis['delegation_value'] * 0.3 +
|
||||
coordination_analysis['synchronization_effectiveness'] * 0.3
|
||||
),
|
||||
'delegation_success': delegation_analysis['delegation_value'] > 0.6,
|
||||
'coordination_success': coordination_analysis['synchronization_effectiveness'] > 0.7,
|
||||
'learning_value': wave_metrics.get('wave_learning_value', 0.5)
|
||||
},
|
||||
|
||||
'next_task_recommendations': {
|
||||
'continue_delegation': delegation_analysis['delegation_value'] > 0.6,
|
||||
'optimize_coordination': coordination_analysis['synchronization_effectiveness'] < 0.7,
|
||||
'adjust_wave_strategy': wave_metrics['wave_performance'] < 0.6,
|
||||
'suggested_improvements': optimization_insights.get('delegation_optimizations', [])[:2]
|
||||
},
|
||||
|
||||
'metadata': {
|
||||
'hook_version': 'subagent_stop_1.0',
|
||||
'analysis_timestamp': time.time(),
|
||||
'coordination_framework': 'task_management_mode'
|
||||
}
|
||||
}
|
||||
|
||||
def _create_fallback_report(self, subagent_data: dict, error: str) -> dict:
|
||||
"""Create fallback coordination report on error."""
|
||||
return {
|
||||
'subagent_id': subagent_data.get('subagent_id', 'unknown'),
|
||||
'task_id': subagent_data.get('task_id', 'unknown'),
|
||||
'completion_timestamp': time.time(),
|
||||
'error': error,
|
||||
'fallback_mode': True,
|
||||
|
||||
'task_completion': {
|
||||
'success': False,
|
||||
'quality_score': 0.0,
|
||||
'efficiency_score': 0.0,
|
||||
'error_occurred': True
|
||||
},
|
||||
|
||||
'delegation_analysis': {
|
||||
'strategy': 'unknown',
|
||||
'effectiveness': 0.0,
|
||||
'error': error
|
||||
},
|
||||
|
||||
'performance_metrics': {
|
||||
'coordination_analysis_time_ms': 0,
|
||||
'target_met': False,
|
||||
'error_occurred': True
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
"""Main hook execution function."""
|
||||
try:
|
||||
# Read subagent data from stdin
|
||||
subagent_data = json.loads(sys.stdin.read())
|
||||
|
||||
# Initialize and run hook
|
||||
hook = SubagentStopHook()
|
||||
result = hook.process_subagent_stop(subagent_data)
|
||||
|
||||
# Output result as JSON
|
||||
print(json.dumps(result, indent=2))
|
||||
|
||||
except Exception as e:
|
||||
# Output error as JSON
|
||||
error_result = {
|
||||
'coordination_analysis_enabled': False,
|
||||
'error': str(e),
|
||||
'fallback_mode': True
|
||||
}
|
||||
print(json.dumps(error_result, indent=2))
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,114 +0,0 @@
|
||||
# Dynamic MCP Server Activation Pattern
|
||||
# Just-in-time activation patterns for MCP servers
|
||||
|
||||
activation_patterns:
|
||||
context7:
|
||||
triggers:
|
||||
- "import statements from external libraries"
|
||||
- "framework-specific questions"
|
||||
- "documentation requests"
|
||||
- "best practices queries"
|
||||
context_keywords:
|
||||
- "how to use"
|
||||
- "documentation"
|
||||
- "examples"
|
||||
- "patterns"
|
||||
activation_confidence: 0.8
|
||||
|
||||
sequential:
|
||||
triggers:
|
||||
- "complex debugging scenarios"
|
||||
- "multi-step analysis requests"
|
||||
- "--think flags detected"
|
||||
- "system design questions"
|
||||
context_keywords:
|
||||
- "analyze"
|
||||
- "debug"
|
||||
- "complex"
|
||||
- "system"
|
||||
- "architecture"
|
||||
activation_confidence: 0.85
|
||||
|
||||
magic:
|
||||
triggers:
|
||||
- "UI component requests"
|
||||
- "design system queries"
|
||||
- "frontend development"
|
||||
- "component keywords"
|
||||
context_keywords:
|
||||
- "component"
|
||||
- "UI"
|
||||
- "frontend"
|
||||
- "design"
|
||||
- "interface"
|
||||
activation_confidence: 0.9
|
||||
|
||||
playwright:
|
||||
triggers:
|
||||
- "testing workflows"
|
||||
- "browser automation"
|
||||
- "e2e testing"
|
||||
- "performance monitoring"
|
||||
context_keywords:
|
||||
- "test"
|
||||
- "browser"
|
||||
- "automation"
|
||||
- "e2e"
|
||||
- "performance"
|
||||
activation_confidence: 0.85
|
||||
|
||||
morphllm:
|
||||
triggers:
|
||||
- "multi-file editing"
|
||||
- "pattern application"
|
||||
- "fast apply scenarios"
|
||||
- "code transformation"
|
||||
context_keywords:
|
||||
- "edit"
|
||||
- "modify"
|
||||
- "refactor"
|
||||
- "transform"
|
||||
- "apply"
|
||||
activation_confidence: 0.8
|
||||
|
||||
serena:
|
||||
triggers:
|
||||
- "semantic analysis"
|
||||
- "project-wide operations"
|
||||
- "symbol navigation"
|
||||
- "memory management"
|
||||
context_keywords:
|
||||
- "analyze"
|
||||
- "project"
|
||||
- "semantic"
|
||||
- "memory"
|
||||
- "context"
|
||||
activation_confidence: 0.75
|
||||
|
||||
coordination_patterns:
|
||||
hybrid_intelligence:
|
||||
serena_morphllm:
|
||||
condition: "complex editing with semantic understanding"
|
||||
strategy: "serena analyzes, morphllm executes"
|
||||
confidence_threshold: 0.8
|
||||
|
||||
multi_server_activation:
|
||||
max_concurrent: 3
|
||||
priority_order:
|
||||
- "serena"
|
||||
- "sequential"
|
||||
- "context7"
|
||||
- "magic"
|
||||
- "morphllm"
|
||||
- "playwright"
|
||||
|
||||
fallback_strategies:
|
||||
server_unavailable: "graceful_degradation"
|
||||
timeout_handling: "partial_results"
|
||||
error_recovery: "alternative_server"
|
||||
|
||||
performance_optimization:
|
||||
cache_activation_decisions: true
|
||||
cache_duration_minutes: 15
|
||||
batch_similar_requests: true
|
||||
lazy_loading: true
|
||||
@@ -1,107 +0,0 @@
|
||||
# Dynamic Mode Detection Pattern
|
||||
# Real-time mode activation based on context analysis
|
||||
|
||||
mode_detection:
|
||||
brainstorming:
|
||||
triggers:
|
||||
- "vague project requests"
|
||||
- "exploration keywords"
|
||||
- "uncertainty indicators"
|
||||
- "new project discussions"
|
||||
patterns:
|
||||
- "I want to build"
|
||||
- "thinking about"
|
||||
- "not sure"
|
||||
- "explore"
|
||||
- "brainstorm"
|
||||
- "figure out"
|
||||
confidence_threshold: 0.7
|
||||
activation_hooks: ["session_start", "pre_tool_use"]
|
||||
coordination:
|
||||
command: "/sc:brainstorm"
|
||||
mcp_servers: ["sequential", "context7"]
|
||||
|
||||
task_management:
|
||||
triggers:
|
||||
- "multi-step operations"
|
||||
- "build/implement keywords"
|
||||
- "system-wide scope"
|
||||
- "delegation indicators"
|
||||
patterns:
|
||||
- "build"
|
||||
- "implement"
|
||||
- "create"
|
||||
- "system"
|
||||
- "comprehensive"
|
||||
- "multiple files"
|
||||
confidence_threshold: 0.8
|
||||
activation_hooks: ["pre_tool_use", "subagent_stop"]
|
||||
coordination:
|
||||
wave_orchestration: true
|
||||
delegation_patterns: true
|
||||
|
||||
token_efficiency:
|
||||
triggers:
|
||||
- "context usage >75%"
|
||||
- "large-scale operations"
|
||||
- "resource constraints"
|
||||
- "brevity requests"
|
||||
patterns:
|
||||
- "compressed"
|
||||
- "brief"
|
||||
- "optimize"
|
||||
- "efficient"
|
||||
- "reduce"
|
||||
confidence_threshold: 0.75
|
||||
activation_hooks: ["pre_compact", "session_start"]
|
||||
coordination:
|
||||
compression_algorithms: true
|
||||
selective_preservation: true
|
||||
|
||||
introspection:
|
||||
triggers:
|
||||
- "self-analysis requests"
|
||||
- "framework discussions"
|
||||
- "meta-cognitive needs"
|
||||
- "error analysis"
|
||||
patterns:
|
||||
- "analyze reasoning"
|
||||
- "framework"
|
||||
- "meta"
|
||||
- "introspect"
|
||||
- "self-analysis"
|
||||
confidence_threshold: 0.6
|
||||
activation_hooks: ["post_tool_use"]
|
||||
coordination:
|
||||
meta_cognitive_analysis: true
|
||||
reasoning_validation: true
|
||||
|
||||
adaptive_learning:
|
||||
pattern_refinement:
|
||||
enabled: true
|
||||
learning_rate: 0.1
|
||||
feedback_integration: true
|
||||
|
||||
user_adaptation:
|
||||
track_preferences: true
|
||||
adapt_thresholds: true
|
||||
personalization: true
|
||||
|
||||
effectiveness_tracking:
|
||||
mode_success_rate: true
|
||||
user_satisfaction: true
|
||||
performance_impact: true
|
||||
|
||||
cross_mode_coordination:
|
||||
simultaneous_modes:
|
||||
- ["task_management", "token_efficiency"]
|
||||
- ["brainstorming", "introspection"]
|
||||
|
||||
mode_transitions:
|
||||
brainstorming_to_task_management:
|
||||
trigger: "requirements clarified"
|
||||
confidence: 0.8
|
||||
|
||||
task_management_to_introspection:
|
||||
trigger: "complex issues encountered"
|
||||
confidence: 0.7
|
||||
@@ -1,177 +0,0 @@
|
||||
# Learned Project Optimizations Pattern
|
||||
# Project-specific adaptations that improve over time
|
||||
|
||||
project_profile:
|
||||
id: "superclaude_framework"
|
||||
type: "python_framework"
|
||||
created: "2025-01-31"
|
||||
last_analyzed: "2025-01-31"
|
||||
optimization_cycles: 0
|
||||
|
||||
learned_optimizations:
|
||||
file_patterns:
|
||||
high_frequency_files:
|
||||
patterns:
|
||||
- "commands/*.md"
|
||||
- "Core/*.md"
|
||||
- "Modes/*.md"
|
||||
- "MCP/*.md"
|
||||
frequency_weight: 0.9
|
||||
cache_priority: "high"
|
||||
|
||||
structural_patterns:
|
||||
patterns:
|
||||
- "markdown documentation with YAML frontmatter"
|
||||
- "python scripts with comprehensive docstrings"
|
||||
- "modular architecture with clear separation"
|
||||
optimization: "maintain full context for these patterns"
|
||||
|
||||
workflow_optimizations:
|
||||
effective_sequences:
|
||||
- sequence: ["Read", "Edit", "Validate"]
|
||||
success_rate: 0.95
|
||||
context: "documentation updates"
|
||||
|
||||
- sequence: ["Glob", "Read", "MultiEdit"]
|
||||
success_rate: 0.88
|
||||
context: "multi-file refactoring"
|
||||
|
||||
- sequence: ["Serena analyze", "Morphllm execute"]
|
||||
success_rate: 0.92
|
||||
context: "large codebase changes"
|
||||
|
||||
mcp_server_effectiveness:
|
||||
serena:
|
||||
effectiveness: 0.9
|
||||
optimal_contexts:
|
||||
- "framework documentation analysis"
|
||||
- "cross-file relationship mapping"
|
||||
- "memory-driven development"
|
||||
performance_notes: "excellent for project context"
|
||||
|
||||
sequential:
|
||||
effectiveness: 0.85
|
||||
optimal_contexts:
|
||||
- "complex architectural decisions"
|
||||
- "multi-step problem solving"
|
||||
- "systematic analysis"
|
||||
performance_notes: "valuable for thinking-intensive tasks"
|
||||
|
||||
morphllm:
|
||||
effectiveness: 0.8
|
||||
optimal_contexts:
|
||||
- "pattern-based editing"
|
||||
- "documentation updates"
|
||||
- "style consistency"
|
||||
performance_notes: "efficient for text transformations"
|
||||
|
||||
compression_learnings:
|
||||
effective_strategies:
|
||||
framework_content:
|
||||
strategy: "complete_preservation"
|
||||
reason: "high information density, frequent reference"
|
||||
effectiveness: 0.95
|
||||
|
||||
session_metadata:
|
||||
strategy: "aggressive_compression"
|
||||
ratio: 0.7
|
||||
effectiveness: 0.88
|
||||
quality_preservation: 0.96
|
||||
|
||||
symbol_system_adoption:
|
||||
technical_symbols: 0.9 # High adoption rate
|
||||
status_symbols: 0.85 # Good adoption rate
|
||||
flow_symbols: 0.8 # Good adoption rate
|
||||
effectiveness: "significantly improved readability"
|
||||
|
||||
quality_gate_refinements:
|
||||
validation_priorities:
|
||||
- "markdown syntax validation"
|
||||
- "YAML frontmatter validation"
|
||||
- "cross-reference consistency"
|
||||
- "documentation completeness"
|
||||
|
||||
custom_rules:
|
||||
- rule: "SuperClaude framework paths preserved"
|
||||
enforcement: "strict"
|
||||
violation_action: "immediate_alert"
|
||||
|
||||
- rule: "session lifecycle compliance"
|
||||
enforcement: "standard"
|
||||
violation_action: "warning_with_suggestion"
|
||||
|
||||
performance_insights:
|
||||
bottleneck_identification:
|
||||
- area: "large markdown file processing"
|
||||
impact: "medium"
|
||||
optimization: "selective reading with targeted edits"
|
||||
|
||||
- area: "cross-file reference validation"
|
||||
impact: "low"
|
||||
optimization: "cached reference mapping"
|
||||
|
||||
acceleration_opportunities:
|
||||
- opportunity: "pattern-based file detection"
|
||||
potential_improvement: "40% faster file processing"
|
||||
implementation: "regex pre-filtering"
|
||||
|
||||
- opportunity: "intelligent caching"
|
||||
potential_improvement: "60% faster repeated operations"
|
||||
implementation: "content-aware cache keys"
|
||||
|
||||
error_pattern_learning:
|
||||
common_issues:
|
||||
- issue: "path traversal in framework files"
|
||||
frequency: 0.15
|
||||
resolution: "automatic path validation"
|
||||
prevention: "framework exclusion patterns"
|
||||
|
||||
- issue: "markdown syntax in code blocks"
|
||||
frequency: 0.08
|
||||
resolution: "improved syntax detection"
|
||||
prevention: "context-aware parsing"
|
||||
|
||||
recovery_strategies:
|
||||
- strategy: "graceful fallback to standard tools"
|
||||
effectiveness: 0.9
|
||||
context: "MCP server unavailability"
|
||||
|
||||
- strategy: "partial result delivery"
|
||||
effectiveness: 0.85
|
||||
context: "timeout scenarios"
|
||||
|
||||
adaptive_rules:
|
||||
mode_activation_refinements:
|
||||
task_management:
|
||||
threshold: 0.85 # Raised due to project complexity
|
||||
reason: "framework development benefits from structured approach"
|
||||
|
||||
token_efficiency:
|
||||
threshold: 0.7 # Standard due to balanced content types
|
||||
reason: "mixed documentation and code content"
|
||||
|
||||
mcp_coordination_rules:
|
||||
- rule: "always activate serena for framework operations"
|
||||
confidence: 0.95
|
||||
effectiveness: 0.92
|
||||
|
||||
- rule: "use morphllm for documentation pattern updates"
|
||||
confidence: 0.88
|
||||
effectiveness: 0.87
|
||||
|
||||
continuous_improvement:
|
||||
learning_velocity: "high" # Framework actively evolving
|
||||
pattern_stability: "medium" # Architecture still developing
|
||||
optimization_frequency: "per_session"
|
||||
|
||||
success_metrics:
|
||||
operation_speed: "+25% improvement target"
|
||||
quality_preservation: "98% minimum"
|
||||
user_satisfaction: "90% target"
|
||||
|
||||
next_optimization_cycle:
|
||||
focus_areas:
|
||||
- "cross-file relationship mapping"
|
||||
- "intelligent pattern detection"
|
||||
- "performance monitoring integration"
|
||||
target_date: "next_major_session"
|
||||
@@ -1,119 +0,0 @@
|
||||
# Learned User Preferences Pattern
|
||||
# Adaptive patterns that evolve based on user behavior
|
||||
|
||||
user_profile:
|
||||
id: "example_user"
|
||||
created: "2025-01-31"
|
||||
last_updated: "2025-01-31"
|
||||
sessions_analyzed: 0
|
||||
|
||||
learned_preferences:
|
||||
communication_style:
|
||||
verbosity_preference: "balanced" # minimal, balanced, detailed
|
||||
technical_depth: "high" # low, medium, high
|
||||
symbol_usage_comfort: "high" # low, medium, high
|
||||
abbreviation_tolerance: "medium" # low, medium, high
|
||||
|
||||
workflow_patterns:
|
||||
preferred_thinking_mode: "--think-hard"
|
||||
mcp_server_preferences:
|
||||
- "serena" # Most frequently beneficial
|
||||
- "sequential" # High success rate
|
||||
- "context7" # Frequently requested
|
||||
mode_activation_frequency:
|
||||
task_management: 0.8 # High usage
|
||||
token_efficiency: 0.6 # Medium usage
|
||||
brainstorming: 0.3 # Low usage
|
||||
introspection: 0.4 # Medium usage
|
||||
|
||||
project_type_expertise:
|
||||
python: 0.9 # High proficiency
|
||||
react: 0.7 # Good proficiency
|
||||
javascript: 0.8 # High proficiency
|
||||
documentation: 0.6 # Medium proficiency
|
||||
|
||||
performance_preferences:
|
||||
speed_vs_quality: "quality_focused" # speed_focused, balanced, quality_focused
|
||||
compression_tolerance: 0.7 # How much compression user accepts
|
||||
context_size_preference: "medium" # small, medium, large
|
||||
|
||||
learning_insights:
|
||||
effective_patterns:
|
||||
- pattern: "serena + morphllm hybrid"
|
||||
success_rate: 0.92
|
||||
context: "large refactoring tasks"
|
||||
|
||||
- pattern: "sequential + context7"
|
||||
success_rate: 0.88
|
||||
context: "complex debugging"
|
||||
|
||||
- pattern: "magic + context7"
|
||||
success_rate: 0.85
|
||||
context: "UI component creation"
|
||||
|
||||
ineffective_patterns:
|
||||
- pattern: "playwright without setup"
|
||||
success_rate: 0.3
|
||||
context: "testing without proper configuration"
|
||||
improvement: "always check test environment first"
|
||||
|
||||
optimization_opportunities:
|
||||
- area: "context compression"
|
||||
current_efficiency: 0.6
|
||||
target_efficiency: 0.8
|
||||
strategy: "increase abbreviation usage"
|
||||
|
||||
- area: "mcp coordination"
|
||||
current_efficiency: 0.7
|
||||
target_efficiency: 0.85
|
||||
strategy: "better server selection logic"
|
||||
|
||||
adaptive_thresholds:
|
||||
mode_activation:
|
||||
brainstorming: 0.6 # Lowered from 0.7 due to user preference
|
||||
task_management: 0.9 # Raised from 0.8 due to frequent use
|
||||
token_efficiency: 0.65 # Adjusted based on tolerance
|
||||
introspection: 0.5 # Lowered due to user comfort with meta-analysis
|
||||
|
||||
mcp_server_confidence:
|
||||
serena: 0.65 # Lowered due to high success rate
|
||||
sequential: 0.75 # Standard
|
||||
context7: 0.7 # Slightly lowered due to frequent success
|
||||
magic: 0.85 # Standard
|
||||
morphllm: 0.7 # Lowered due to hybrid usage success
|
||||
playwright: 0.9 # Raised due to setup issues
|
||||
|
||||
personalization_rules:
|
||||
communication:
|
||||
- "Use technical terminology freely"
|
||||
- "Provide implementation details"
|
||||
- "Include performance considerations"
|
||||
- "Balance symbol usage with clarity"
|
||||
|
||||
workflow:
|
||||
- "Prefer serena for analysis tasks"
|
||||
- "Use sequential for complex problems"
|
||||
- "Always validate with quality gates"
|
||||
- "Optimize for long-term maintainability"
|
||||
|
||||
error_handling:
|
||||
- "Provide detailed error context"
|
||||
- "Suggest multiple solutions"
|
||||
- "Include learning opportunities"
|
||||
- "Track error patterns for prevention"
|
||||
|
||||
continuous_learning:
|
||||
feedback_integration:
|
||||
explicit_feedback: true
|
||||
implicit_feedback: true # Based on user actions
|
||||
outcome_tracking: true
|
||||
|
||||
pattern_evolution:
|
||||
refinement_frequency: "weekly"
|
||||
adaptation_rate: 0.1
|
||||
stability_threshold: 0.95
|
||||
|
||||
quality_metrics:
|
||||
user_satisfaction_score: 0.0 # To be measured
|
||||
task_completion_rate: 0.0 # To be measured
|
||||
efficiency_improvement: 0.0 # To be measured
|
||||
@@ -1,45 +0,0 @@
|
||||
# Minimal Python Project Pattern
|
||||
# Lightweight bootstrap pattern for Python projects
|
||||
|
||||
project_type: "python"
|
||||
detection_patterns:
|
||||
- "*.py files present"
|
||||
- "requirements.txt or pyproject.toml"
|
||||
- "__pycache__/ directories"
|
||||
|
||||
auto_flags:
|
||||
- "--serena" # Semantic analysis
|
||||
- "--context7" # Python documentation
|
||||
|
||||
mcp_servers:
|
||||
primary: "serena"
|
||||
secondary: ["context7", "sequential", "morphllm"]
|
||||
|
||||
patterns:
|
||||
file_structure:
|
||||
- "src/ or lib/"
|
||||
- "tests/"
|
||||
- "docs/"
|
||||
- "requirements.txt"
|
||||
|
||||
common_tasks:
|
||||
- "function refactoring"
|
||||
- "class extraction"
|
||||
- "import optimization"
|
||||
- "testing setup"
|
||||
|
||||
intelligence:
|
||||
mode_triggers:
|
||||
- "token_efficiency: context >75%"
|
||||
- "task_management: refactor|test|analyze"
|
||||
|
||||
validation_focus:
|
||||
- "python_syntax"
|
||||
- "pep8_compliance"
|
||||
- "type_hints"
|
||||
- "testing_coverage"
|
||||
|
||||
performance_targets:
|
||||
bootstrap_ms: 40
|
||||
context_size: "4KB"
|
||||
cache_duration: "45min"
|
||||
@@ -1,45 +0,0 @@
|
||||
# Minimal React Project Pattern
|
||||
# Lightweight bootstrap pattern for React projects
|
||||
|
||||
project_type: "react"
|
||||
detection_patterns:
|
||||
- "package.json with react dependency"
|
||||
- "src/ directory with .jsx/.tsx files"
|
||||
- "public/index.html"
|
||||
|
||||
auto_flags:
|
||||
- "--magic" # UI component generation
|
||||
- "--context7" # React documentation
|
||||
|
||||
mcp_servers:
|
||||
primary: "magic"
|
||||
secondary: ["context7", "morphllm"]
|
||||
|
||||
patterns:
|
||||
file_structure:
|
||||
- "src/components/"
|
||||
- "src/hooks/"
|
||||
- "src/pages/"
|
||||
- "src/utils/"
|
||||
|
||||
common_tasks:
|
||||
- "component creation"
|
||||
- "state management"
|
||||
- "routing setup"
|
||||
- "performance optimization"
|
||||
|
||||
intelligence:
|
||||
mode_triggers:
|
||||
- "token_efficiency: context >75%"
|
||||
- "task_management: build|implement|create"
|
||||
|
||||
validation_focus:
|
||||
- "jsx_syntax"
|
||||
- "react_patterns"
|
||||
- "accessibility"
|
||||
- "performance"
|
||||
|
||||
performance_targets:
|
||||
bootstrap_ms: 30
|
||||
context_size: "3KB"
|
||||
cache_duration: "60min"
|
||||
@@ -1,88 +0,0 @@
|
||||
{
|
||||
"hooks": {
|
||||
"SessionStart": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/session_start.py",
|
||||
"timeout": 10
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"PreToolUse": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/pre_tool_use.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"PostToolUse": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/post_tool_use.py",
|
||||
"timeout": 10
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"PreCompact": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/pre_compact.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"Notification": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/notification.py",
|
||||
"timeout": 10
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"Stop": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/stop.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"SubagentStop": [
|
||||
{
|
||||
"matcher": "*",
|
||||
"hooks": [
|
||||
{
|
||||
"type": "command",
|
||||
"command": "python3 ~/.claude/hooks/subagent_stop.py",
|
||||
"timeout": 15
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1,345 +0,0 @@
|
||||
{
|
||||
"superclaude": {
|
||||
"description": "SuperClaude-Lite Framework Configuration",
|
||||
"version": "1.0.0",
|
||||
"framework": "superclaude-lite",
|
||||
"enabled": true
|
||||
},
|
||||
|
||||
"hook_configurations": {
|
||||
"session_start": {
|
||||
"enabled": true,
|
||||
"description": "SESSION_LIFECYCLE + FLAGS logic with intelligent bootstrap",
|
||||
"performance_target_ms": 50,
|
||||
"features": [
|
||||
"smart_project_context_loading",
|
||||
"automatic_mode_detection",
|
||||
"mcp_server_intelligence_routing",
|
||||
"user_preference_adaptation",
|
||||
"performance_optimized_initialization"
|
||||
],
|
||||
"configuration": {
|
||||
"auto_project_detection": true,
|
||||
"framework_exclusion_enabled": true,
|
||||
"intelligence_activation": true,
|
||||
"learning_integration": true,
|
||||
"performance_monitoring": true
|
||||
},
|
||||
"error_handling": {
|
||||
"graceful_fallback": true,
|
||||
"preserve_user_context": true,
|
||||
"error_learning": true
|
||||
}
|
||||
},
|
||||
|
||||
"pre_tool_use": {
|
||||
"enabled": true,
|
||||
"description": "ORCHESTRATOR + MCP routing intelligence for optimal tool selection",
|
||||
"performance_target_ms": 200,
|
||||
"features": [
|
||||
"intelligent_tool_routing",
|
||||
"mcp_server_selection",
|
||||
"performance_optimization",
|
||||
"context_aware_configuration",
|
||||
"fallback_strategy_implementation",
|
||||
"real_time_adaptation"
|
||||
],
|
||||
"configuration": {
|
||||
"mcp_intelligence": true,
|
||||
"pattern_detection": true,
|
||||
"learning_adaptations": true,
|
||||
"performance_optimization": true,
|
||||
"fallback_strategies": true
|
||||
},
|
||||
"integration": {
|
||||
"mcp_servers": ["context7", "sequential", "magic", "playwright", "morphllm", "serena"],
|
||||
"quality_gates": true,
|
||||
"learning_engine": true
|
||||
}
|
||||
},
|
||||
|
||||
"post_tool_use": {
|
||||
"enabled": true,
|
||||
"description": "RULES + PRINCIPLES validation and learning system",
|
||||
"performance_target_ms": 100,
|
||||
"features": [
|
||||
"quality_validation",
|
||||
"rules_compliance_checking",
|
||||
"principles_alignment_verification",
|
||||
"effectiveness_measurement",
|
||||
"error_pattern_detection",
|
||||
"learning_opportunity_identification"
|
||||
],
|
||||
"configuration": {
|
||||
"rules_validation": true,
|
||||
"principles_validation": true,
|
||||
"quality_standards_enforcement": true,
|
||||
"effectiveness_tracking": true,
|
||||
"learning_integration": true
|
||||
},
|
||||
"validation_levels": {
|
||||
"basic": ["syntax_validation"],
|
||||
"standard": ["syntax_validation", "type_analysis", "code_quality"],
|
||||
"comprehensive": ["syntax_validation", "type_analysis", "code_quality", "security_assessment", "performance_analysis"],
|
||||
"production": ["syntax_validation", "type_analysis", "code_quality", "security_assessment", "performance_analysis", "integration_testing", "deployment_validation"]
|
||||
}
|
||||
},
|
||||
|
||||
"pre_compact": {
|
||||
"enabled": true,
|
||||
"description": "MODE_Token_Efficiency compression algorithms with intelligent optimization",
|
||||
"performance_target_ms": 150,
|
||||
"features": [
|
||||
"intelligent_compression_strategy_selection",
|
||||
"selective_content_preservation",
|
||||
"framework_exclusion",
|
||||
"symbol_systems_optimization",
|
||||
"abbreviation_systems",
|
||||
"quality_gated_compression"
|
||||
],
|
||||
"configuration": {
|
||||
"selective_compression": true,
|
||||
"framework_protection": true,
|
||||
"quality_preservation_target": 0.95,
|
||||
"compression_efficiency_target": 0.50,
|
||||
"adaptive_compression": true
|
||||
},
|
||||
"compression_levels": {
|
||||
"minimal": "0-40%",
|
||||
"efficient": "40-70%",
|
||||
"compressed": "70-85%",
|
||||
"critical": "85-95%",
|
||||
"emergency": "95%+"
|
||||
}
|
||||
},
|
||||
|
||||
"notification": {
|
||||
"enabled": true,
|
||||
"description": "Just-in-time MCP documentation loading and pattern updates",
|
||||
"performance_target_ms": 100,
|
||||
"features": [
|
||||
"just_in_time_documentation_loading",
|
||||
"dynamic_pattern_updates",
|
||||
"framework_intelligence_updates",
|
||||
"real_time_learning",
|
||||
"performance_optimization_through_caching"
|
||||
],
|
||||
"configuration": {
|
||||
"jit_documentation_loading": true,
|
||||
"pattern_updates": true,
|
||||
"intelligence_caching": true,
|
||||
"learning_integration": true,
|
||||
"performance_optimization": true
|
||||
},
|
||||
"caching": {
|
||||
"documentation_cache_minutes": 30,
|
||||
"pattern_cache_minutes": 60,
|
||||
"intelligence_cache_minutes": 15
|
||||
}
|
||||
},
|
||||
|
||||
"stop": {
|
||||
"enabled": true,
|
||||
"description": "Session analytics + /sc:save logic with performance tracking",
|
||||
"performance_target_ms": 200,
|
||||
"features": [
|
||||
"comprehensive_session_analytics",
|
||||
"learning_consolidation",
|
||||
"session_persistence",
|
||||
"performance_optimization_recommendations",
|
||||
"quality_assessment_and_improvement_suggestions"
|
||||
],
|
||||
"configuration": {
|
||||
"session_analytics": true,
|
||||
"learning_consolidation": true,
|
||||
"session_persistence": true,
|
||||
"performance_tracking": true,
|
||||
"recommendation_generation": true
|
||||
},
|
||||
"analytics": {
|
||||
"performance_metrics": true,
|
||||
"effectiveness_measurement": true,
|
||||
"learning_insights": true,
|
||||
"optimization_recommendations": true
|
||||
}
|
||||
},
|
||||
|
||||
"subagent_stop": {
|
||||
"enabled": true,
|
||||
"description": "MODE_Task_Management delegation coordination and analytics",
|
||||
"performance_target_ms": 150,
|
||||
"features": [
|
||||
"subagent_performance_analytics",
|
||||
"delegation_effectiveness_measurement",
|
||||
"cross_agent_learning",
|
||||
"wave_orchestration_optimization",
|
||||
"parallel_execution_performance_tracking"
|
||||
],
|
||||
"configuration": {
|
||||
"delegation_analytics": true,
|
||||
"coordination_measurement": true,
|
||||
"wave_orchestration": true,
|
||||
"performance_tracking": true,
|
||||
"learning_integration": true
|
||||
},
|
||||
"task_management": {
|
||||
"delegation_strategies": ["files", "folders", "auto"],
|
||||
"wave_orchestration": true,
|
||||
"parallel_coordination": true,
|
||||
"performance_optimization": true
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"global_configuration": {
|
||||
"framework_integration": {
|
||||
"superclaude_compliance": true,
|
||||
"yaml_driven_logic": true,
|
||||
"hot_reload_configuration": true,
|
||||
"cross_hook_coordination": true
|
||||
},
|
||||
|
||||
"performance_monitoring": {
|
||||
"enabled": true,
|
||||
"real_time_tracking": true,
|
||||
"target_enforcement": true,
|
||||
"optimization_suggestions": true,
|
||||
"performance_analytics": true
|
||||
},
|
||||
|
||||
"learning_system": {
|
||||
"enabled": true,
|
||||
"cross_hook_learning": true,
|
||||
"adaptation_application": true,
|
||||
"effectiveness_tracking": true,
|
||||
"pattern_recognition": true
|
||||
},
|
||||
|
||||
"error_handling": {
|
||||
"graceful_degradation": true,
|
||||
"fallback_strategies": true,
|
||||
"error_learning": true,
|
||||
"recovery_optimization": true
|
||||
},
|
||||
|
||||
"security": {
|
||||
"input_validation": true,
|
||||
"path_traversal_protection": true,
|
||||
"timeout_protection": true,
|
||||
"resource_limits": true
|
||||
}
|
||||
},
|
||||
|
||||
"mcp_server_integration": {
|
||||
"enabled": true,
|
||||
"servers": {
|
||||
"context7": {
|
||||
"description": "Library documentation and framework patterns",
|
||||
"capabilities": ["documentation_access", "framework_patterns", "best_practices"],
|
||||
"performance_profile": "standard"
|
||||
},
|
||||
"sequential": {
|
||||
"description": "Multi-step reasoning and complex analysis",
|
||||
"capabilities": ["complex_reasoning", "systematic_analysis", "hypothesis_testing"],
|
||||
"performance_profile": "intensive"
|
||||
},
|
||||
"magic": {
|
||||
"description": "UI component generation and design systems",
|
||||
"capabilities": ["ui_generation", "design_systems", "component_patterns"],
|
||||
"performance_profile": "standard"
|
||||
},
|
||||
"playwright": {
|
||||
"description": "Browser automation and testing",
|
||||
"capabilities": ["browser_automation", "testing_frameworks", "performance_testing"],
|
||||
"performance_profile": "intensive"
|
||||
},
|
||||
"morphllm": {
|
||||
"description": "Intelligent editing with fast apply",
|
||||
"capabilities": ["pattern_application", "fast_apply", "intelligent_editing"],
|
||||
"performance_profile": "lightweight"
|
||||
},
|
||||
"serena": {
|
||||
"description": "Semantic analysis and memory management",
|
||||
"capabilities": ["semantic_understanding", "project_context", "memory_management"],
|
||||
"performance_profile": "standard"
|
||||
}
|
||||
},
|
||||
|
||||
"coordination": {
|
||||
"intelligent_routing": true,
|
||||
"fallback_strategies": true,
|
||||
"performance_optimization": true,
|
||||
"learning_adaptation": true
|
||||
}
|
||||
},
|
||||
|
||||
"mode_integration": {
|
||||
"enabled": true,
|
||||
"modes": {
|
||||
"brainstorming": {
|
||||
"description": "Interactive requirements discovery",
|
||||
"hooks": ["session_start", "notification"],
|
||||
"mcp_servers": ["sequential", "context7"]
|
||||
},
|
||||
"task_management": {
|
||||
"description": "Multi-layer task orchestration",
|
||||
"hooks": ["session_start", "pre_tool_use", "subagent_stop", "stop"],
|
||||
"mcp_servers": ["serena", "morphllm"]
|
||||
},
|
||||
"token_efficiency": {
|
||||
"description": "Intelligent token optimization",
|
||||
"hooks": ["pre_compact", "session_start"],
|
||||
"mcp_servers": ["morphllm"]
|
||||
},
|
||||
"introspection": {
|
||||
"description": "Meta-cognitive analysis",
|
||||
"hooks": ["post_tool_use", "stop"],
|
||||
"mcp_servers": ["sequential"]
|
||||
}
|
||||
}
|
||||
},
|
||||
|
||||
"quality_gates": {
|
||||
"enabled": true,
|
||||
"8_step_validation": {
|
||||
"step_1": "syntax_validation",
|
||||
"step_2": "type_analysis",
|
||||
"step_3": "code_quality",
|
||||
"step_4": "security_assessment",
|
||||
"step_5": "testing_validation",
|
||||
"step_6": "performance_analysis",
|
||||
"step_7": "documentation_verification",
|
||||
"step_8": "integration_testing"
|
||||
},
|
||||
"hook_integration": {
|
||||
"pre_tool_use": ["step_1", "step_2"],
|
||||
"post_tool_use": ["step_3", "step_4", "step_5"],
|
||||
"stop": ["step_6", "step_7", "step_8"]
|
||||
}
|
||||
},
|
||||
|
||||
"cache_configuration": {
|
||||
"enabled": true,
|
||||
"cache_directory": "./cache",
|
||||
"learning_data_retention_days": 90,
|
||||
"session_data_retention_days": 30,
|
||||
"performance_data_retention_days": 365,
|
||||
"automatic_cleanup": true
|
||||
},
|
||||
|
||||
"logging_configuration": {
|
||||
"enabled": true,
|
||||
"log_level": "INFO",
|
||||
"performance_logging": true,
|
||||
"error_logging": true,
|
||||
"learning_logging": true,
|
||||
"hook_execution_logging": true
|
||||
},
|
||||
|
||||
"development_support": {
|
||||
"debugging_enabled": false,
|
||||
"performance_profiling": false,
|
||||
"verbose_logging": false,
|
||||
"test_mode": false
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user