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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>
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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.mdfor execution patterns - Performance:
performance.yaml.mdfor performance optimization - User Experience:
user_experience.yaml.mdfor user-focused intelligence