mirror of
https://github.com/SuperClaude-Org/SuperClaude_Framework.git
synced 2025-12-29 16:16:08 +00:00
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>
75 lines
3.4 KiB
Markdown
75 lines
3.4 KiB
Markdown
# 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 |