SuperClaude/Framework-Hooks/config/intelligence_patterns.yaml

# Intelligence Patterns Configuration
# Core learning intelligence patterns for SuperClaude Framework-Hooks
# Defines multi-dimensional pattern recognition, adaptive learning, and intelligence behaviors

# Metadata
version: "1.0.0"
last_updated: "2025-01-06"
description: "Core intelligence patterns for declarative learning and adaptation"

# Learning Intelligence Configuration
learning_intelligence:
  pattern_recognition:
    # Multi-dimensional pattern analysis
    dimensions:
      primary:
        - context_type      # Type of operation context
        - complexity_score  # Operation complexity (0.0-1.0)
        - operation_type    # Category of operation
        - performance_score # Performance effectiveness (0.0-1.0)
      secondary:
        - file_count       # Number of files involved
        - directory_count  # Number of directories
        - mcp_server       # MCP server involved
        - user_expertise   # Detected user skill level
      
    # Pattern signature generation
    signature_generation:
      method: "multi_dimensional_hash"
      include_context: true
      fallback_signature: "unknown_pattern"
      max_signature_length: 128
      
    # Pattern clustering for similar behavior grouping
    clustering:
      algorithm: "k_means"
      min_cluster_size: 3
      max_clusters: 20
      similarity_threshold: 0.8
      recalculate_interval: 100  # operations
      
  adaptive_learning:
    # Dynamic learning rate adjustment
    learning_rate:
      initial: 0.7
      min: 0.1
      max: 1.0
      adaptation_strategy: "confidence_based"
      
    # Confidence scoring
    confidence_scoring:
      base_confidence: 0.5
      consistency_weight: 0.4
      frequency_weight: 0.3
      recency_weight: 0.3
      
    # Effectiveness thresholds
    effectiveness_thresholds:
      learn_threshold: 0.7    # Minimum effectiveness to create adaptation
      confidence_threshold: 0.6  # Minimum confidence to apply adaptation
      forget_threshold: 0.3   # Below this, remove adaptation
      
  pattern_quality:
    # Pattern validation rules
    validation_rules:
      min_usage_count: 3
      max_consecutive_perfect_scores: 10
      effectiveness_variance_limit: 0.5
      required_dimensions: ["context_type", "operation_type"]
      
    # Quality scoring
    quality_metrics:
      diversity_score_weight: 0.4
      consistency_score_weight: 0.3
      usage_frequency_weight: 0.3
      
# Pattern Analysis Configuration
pattern_analysis:
  anomaly_detection:
    # Detect unusual patterns that might indicate issues
    anomaly_patterns:
      - name: "overfitting_detection"
        condition: {consecutive_perfect_scores: ">10"}
        severity: "medium"
        action: "flag_for_review"
        
      - name: "pattern_stagnation" 
        condition: {no_new_patterns: ">30_days"}
        severity: "low"
        action: "suggest_pattern_diversity"
        
      - name: "effectiveness_degradation"
        condition: {effectiveness_trend: "decreasing", duration: ">7_days"}
        severity: "high"
        action: "trigger_pattern_analysis"
        
  trend_analysis:
    # Track learning trends over time
    tracking_windows:
      short_term: 24    # hours
      medium_term: 168  # hours (1 week) 
      long_term: 720    # hours (30 days)
      
    trend_indicators:
      - effectiveness_trend
      - pattern_diversity_trend
      - confidence_trend
      - usage_frequency_trend

# Intelligence Enhancement Patterns
intelligence_enhancement:
  predictive_capabilities:
    # Predictive pattern matching
    prediction_horizon: 5  # operations ahead
    prediction_confidence_threshold: 0.7
    prediction_accuracy_tracking: true
    
  context_awareness:
    # Context understanding and correlation
    context_correlation:
      enable_cross_session: true
      enable_project_correlation: true
      enable_user_correlation: true
      correlation_strength_threshold: 0.6
      
  adaptive_strategies:
    # Strategy adaptation based on performance
    strategy_adaptation:
      performance_window: 20  # operations
      adaptation_threshold: 0.8
      rollback_threshold: 0.5
      max_adaptations_per_session: 5

# Pattern Lifecycle Management
lifecycle_management:
  pattern_evolution:
    # How patterns evolve over time
    evolution_triggers:
      - usage_count_milestone: [10, 50, 100, 500]
      - effectiveness_improvement: 0.1
      - confidence_improvement: 0.1
      
    evolution_actions:
      - promote_to_global
      - increase_weight
      - expand_context
      - merge_similar_patterns
      
  pattern_cleanup:
    # Automatic pattern cleanup
    cleanup_triggers:
      max_patterns: 1000
      unused_pattern_age: 30  # days
      low_effectiveness_threshold: 0.3
      
    cleanup_strategies:
      - archive_unused
      - merge_similar  
      - remove_ineffective
      - compress_historical

# Integration Configuration  
integration:
  cache_management:
    # Pattern caching for performance
    cache_patterns: true
    cache_duration: 3600  # seconds
    max_cache_size: 100   # patterns
    cache_invalidation: "smart"  # smart, time_based, usage_based
    
  performance_optimization:
    # Performance tuning
    lazy_loading: true
    batch_processing: true
    background_analysis: true
    max_processing_time_ms: 100
    
  compatibility:
    # Backwards compatibility
    support_legacy_patterns: true
    migration_assistance: true
    graceful_degradation: true