SuperClaude/Framework-Hooks/DOCUMENTATION_UPDATE_SUMMARY.md

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docs: Complete Framework-Hooks documentation overhaul Major documentation update focused on technical accuracy and developer clarity: Documentation Changes: - Rewrote README.md with focus on hooks system architecture - Updated all core docs (Overview, Integration, Performance) to match implementation - Created 6 missing configuration docs for undocumented YAML files - Updated all 7 hook docs to reflect actual Python implementations - Created docs for 2 missing shared modules (intelligence_engine, validate_system) - Updated all 5 pattern docs with real YAML examples - Added 4 essential operational docs (INSTALLATION, TROUBLESHOOTING, CONFIGURATION, QUICK_REFERENCE) Key Improvements: - Removed all marketing language in favor of humble technical documentation - Fixed critical configuration discrepancies (logging defaults, performance targets) - Used actual code examples and configuration from implementation - Complete coverage: 15 configs, 10 modules, 7 hooks, 3 pattern tiers - Based all documentation on actual file review and code analysis Technical Accuracy: - Corrected performance targets to match performance.yaml - Fixed timeout values from settings.json (10-15 seconds) - Updated module count and descriptions to match actual shared/ directory - Aligned all examples with actual YAML and Python implementations The documentation now provides accurate, practical information for developers working with the Framework-Hooks system, focusing on what it actually does rather than aspirational features. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-08-06 15:13:07 +02:00
# Hook Documentation Update Summary
## Overview
Updated hook documentation files to accurately reflect the actual Python implementations, removing marketing language and aspirational features in favor of technical accuracy.
## Key Changes Made
### Common Updates Across All Hooks
1. **Replaced aspirational descriptions** with accurate technical implementation details
2. **Added actual execution context** including timeout values from `settings.json`
3. **Updated execution flows** to match stdin/stdout JSON processing pattern
4. **Documented actual shared module dependencies** and their usage
5. **Simplified language** to focus on what the code actually does
6. **Added implementation line counts** for context
7. **Corrected performance targets** to match configuration values
### Specific Hook Updates
#### session_start.md
- **Lines**: 704-line Python implementation
- **Timeout**: 10 seconds (from settings.json)
- **Key Features**: Lazy loading architecture, project structure analysis, user intent analysis, MCP server configuration
- **Shared Modules**: framework_logic, pattern_detection, mcp_intelligence, compression_engine, learning_engine, yaml_loader, logger
- **Performance**: <50ms target
#### pre_tool_use.md
- **Lines**: 648-line Python implementation
- **Timeout**: 15 seconds (from settings.json)
- **Key Features**: Operation characteristics analysis, tool chain context analysis, MCP server routing, performance optimization
- **Performance**: <200ms target
#### post_tool_use.md
- **Lines**: 794-line Python implementation
- **Timeout**: 10 seconds (from settings.json)
- **Key Features**: Validation against RULES.md and PRINCIPLES.md, effectiveness measurement, error pattern detection, learning integration
- **Performance**: <100ms target
#### pre_compact.md
- **Timeout**: 15 seconds (from settings.json)
- **Key Features**: MODE_Token_Efficiency implementation, selective compression, symbol systems
- **Performance**: <150ms target
#### notification.md
- **Timeout**: 10 seconds (from settings.json)
- **Key Features**: Just-in-time capability loading, notification type handling
- **Processing**: High/medium/low priority notification handling
#### stop.md
- **Timeout**: 15 seconds (from settings.json)
- **Key Features**: Session analytics, learning consolidation, data persistence
- **Performance**: <200ms target
#### subagent_stop.md
- **Timeout**: 15 seconds (from settings.json)
- **Key Features**: Delegation effectiveness measurement, multi-agent coordination analytics
- **Performance**: <150ms target
## Technical Accuracy Improvements
1. **Execution Pattern**: All hooks follow stdin JSON → process → stdout JSON pattern
2. **Error Handling**: All hooks implement graceful fallback with basic functionality preservation
3. **Shared Modules**: Documented actual module imports and specific method usage
4. **Configuration**: Referenced actual configuration files and fallback strategies
5. **Performance**: Corrected timeout values and performance targets based on actual settings
## Language Changes
- **Before**: "comprehensive intelligence layer", "transformative capabilities", "revolutionary approach"
- **After**: "analyzes project context", "implements pattern detection", "provides MCP server coordination"
- **Before**: Complex architectural descriptions without implementation details
- **After**: Actual method names, class structures, and execution flows
- **Before**: Aspirational features not yet implemented
- **After**: Features that actually exist in the Python code
## Documentation Quality
- Focused on practical implementation details developers need
- Removed marketing language in favor of technical precision
- Added concrete examples from actual code
- Clarified what each hook actually does vs. what it might do
- Made timeouts and performance targets realistic and accurate
## Files Updated
- `/docs/Hooks/session_start.md` - Major revision focusing on actual implementation
- `/docs/Hooks/pre_tool_use.md` - Streamlined to match 648-line implementation
- `/docs/Hooks/post_tool_use.md` - Focused on validation and learning implementation
- `/docs/Hooks/pre_compact.md` - Simplified compression implementation description
- `/docs/Hooks/notification.md` - Concise notification handling description
- `/docs/Hooks/stop.md` - Session analytics and persistence focus
- `/docs/Hooks/subagent_stop.md` - Delegation analytics focus
## Result
Documentation now accurately represents what the Python implementations actually do, with humble technical language focused on practical functionality rather than aspirational capabilities.