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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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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.mdfor basic performance settings - Intelligence Patterns:
intelligence_patterns.yaml.mdfor core learning patterns - Hook Coordination:
hook_coordination.yaml.mdfor performance-aware execution