# 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