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9edf3f8802
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>
185 lines
6.3 KiB
Markdown
185 lines
6.3 KiB
Markdown
# Learned Patterns: Adaptive Behavior Learning
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## Overview
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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.
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## Purpose
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Learned patterns handle:
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- **Project Optimizations**: Track effective workflows and performance improvements for specific projects
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- **User Preferences**: Learn individual user behavior patterns and communication styles
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- **Performance Metrics**: Monitor effectiveness of different MCP servers and coordination strategies
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- **Error Prevention**: Learn from past issues to prevent recurring problems
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## Current Learned Patterns
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### User Preferences Pattern (`user_preferences.yaml`)
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This pattern tracks individual user behavior and preferences:
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```yaml
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user_profile:
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id: "example_user"
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created: "2025-01-31"
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last_updated: "2025-01-31"
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sessions_analyzed: 0
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learned_preferences:
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communication_style:
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verbosity_preference: "balanced" # minimal, balanced, detailed
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technical_depth: "high" # low, medium, high
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symbol_usage_comfort: "high" # low, medium, high
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abbreviation_tolerance: "medium" # low, medium, high
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workflow_patterns:
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preferred_thinking_mode: "--think-hard"
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mcp_server_preferences:
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- "serena" # Most frequently beneficial
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- "sequential" # High success rate
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- "context7" # Frequently requested
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mode_activation_frequency:
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task_management: 0.8 # High usage
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token_efficiency: 0.6 # Medium usage
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brainstorming: 0.3 # Low usage
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introspection: 0.4 # Medium usage
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project_type_expertise:
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python: 0.9 # High proficiency
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react: 0.7 # Good proficiency
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javascript: 0.8 # High proficiency
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documentation: 0.6 # Medium proficiency
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performance_preferences:
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speed_vs_quality: "quality_focused" # speed_focused, balanced, quality_focused
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compression_tolerance: 0.7 # How much compression user accepts
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context_size_preference: "medium" # small, medium, large
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learning_insights:
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effective_patterns:
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- pattern: "serena + morphllm hybrid"
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success_rate: 0.92
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context: "large refactoring tasks"
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- pattern: "sequential + context7"
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success_rate: 0.88
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context: "complex debugging"
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- pattern: "magic + context7"
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success_rate: 0.85
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context: "UI component creation"
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adaptive_thresholds:
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mode_activation:
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brainstorming: 0.6 # Lowered from 0.7 due to user preference
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task_management: 0.9 # Raised from 0.8 due to frequent use
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token_efficiency: 0.65 # Adjusted based on tolerance
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introspection: 0.5 # Lowered due to user comfort with meta-analysis
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### Project Optimizations Pattern (`project_optimizations.yaml`)
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This pattern tracks project-specific performance and optimization data:
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```yaml
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project_profile:
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id: "superclaude_framework"
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type: "python_framework"
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created: "2025-01-31"
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last_analyzed: "2025-01-31"
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optimization_cycles: 0
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learned_optimizations:
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file_patterns:
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high_frequency_files:
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patterns:
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- "commands/*.md"
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- "Core/*.md"
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- "Modes/*.md"
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- "MCP/*.md"
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frequency_weight: 0.9
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cache_priority: "high"
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structural_patterns:
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patterns:
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- "markdown documentation with YAML frontmatter"
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- "python scripts with comprehensive docstrings"
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- "modular architecture with clear separation"
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optimization: "maintain full context for these patterns"
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workflow_optimizations:
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effective_sequences:
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- sequence: ["Read", "Edit", "Validate"]
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success_rate: 0.95
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context: "documentation updates"
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- sequence: ["Glob", "Read", "MultiEdit"]
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success_rate: 0.88
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context: "multi-file refactoring"
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- sequence: ["Serena analyze", "Morphllm execute"]
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success_rate: 0.92
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context: "large codebase changes"
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mcp_server_effectiveness:
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serena:
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effectiveness: 0.9
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optimal_contexts:
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- "framework documentation analysis"
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- "cross-file relationship mapping"
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- "memory-driven development"
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performance_notes: "excellent for project context"
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sequential:
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effectiveness: 0.85
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optimal_contexts:
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- "complex architectural decisions"
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- "multi-step problem solving"
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- "systematic analysis"
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performance_notes: "valuable for thinking-intensive tasks"
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morphllm:
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effectiveness: 0.8
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optimal_contexts:
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- "pattern-based editing"
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- "documentation updates"
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- "style consistency"
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performance_notes: "efficient for text transformations"
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performance_insights:
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bottleneck_identification:
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- area: "large markdown file processing"
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impact: "medium"
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optimization: "selective reading with targeted edits"
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- area: "cross-file reference validation"
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impact: "low"
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optimization: "cached reference mapping"
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acceleration_opportunities:
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- opportunity: "pattern-based file detection"
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potential_improvement: "40% faster file processing"
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implementation: "regex pre-filtering"
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- opportunity: "intelligent caching"
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potential_improvement: "60% faster repeated operations"
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implementation: "content-aware cache keys"
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## Learning Process
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Learned patterns evolve through:
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1. **Data Collection**: Track user interactions, tool effectiveness, and performance metrics
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2. **Pattern Analysis**: Identify successful workflows and optimization opportunities
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3. **Threshold Adjustment**: Adapt confidence thresholds based on user behavior
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4. **Performance Tracking**: Monitor the effectiveness of different strategies
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5. **Cross-Session Persistence**: Maintain learning across multiple work sessions
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## Integration Notes
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Learned patterns integrate with Framework-Hooks through:
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- **Adaptive Thresholds**: Modify activation thresholds based on learned preferences
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- **Server Selection**: Prioritize MCP servers based on measured effectiveness
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- **Workflow Optimization**: Apply learned effective sequences to new tasks
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- **Performance Monitoring**: Track and optimize based on measured performance
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The learned patterns provide a feedback mechanism that allows Framework-Hooks to improve its behavior based on actual usage patterns and results. |