feat: Implement YAML-first declarative intelligence architecture

Revolutionary transformation from hardcoded Python intelligence to hot-reloadable
YAML patterns, enabling dynamic configuration without code changes.

## Phase 1: Foundation Intelligence Complete

### YAML Intelligence Patterns (6 files)
- intelligence_patterns.yaml: Multi-dimensional pattern recognition with adaptive learning
- mcp_orchestration.yaml: Server selection decision trees with load balancing
- hook_coordination.yaml: Parallel execution patterns with dependency resolution
- performance_intelligence.yaml: Resource zones and auto-optimization triggers
- validation_intelligence.yaml: Health scoring and proactive diagnostic patterns
- user_experience.yaml: Project detection and smart UX adaptations

### Python Infrastructure Enhanced (4 components)
- intelligence_engine.py: Generic YAML pattern interpreter with hot-reload
- learning_engine.py: Enhanced with YAML intelligence integration
- yaml_loader.py: Added intelligence configuration helper methods
- validate_system.py: New YAML-driven validation with health scoring

### Key Features Implemented
- Hot-reload intelligence: Update patterns without code changes or restarts
- Declarative configuration: All intelligence logic expressed in YAML
- Graceful fallbacks: System works correctly even with missing YAML files
- Multi-pattern coordination: Intelligent recommendations from multiple sources
- Health scoring: Component-weighted validation with predictive diagnostics
- Generic architecture: Single engine consumes all intelligence pattern types

### Testing Results
✅ All components integrate correctly
✅ Hot-reload mechanism functional
✅ Graceful error handling verified
✅ YAML-driven validation operational
✅ Health scoring system working (detected real system issues)

This enables users to modify intelligence behavior by editing YAML files,
add new pattern types without coding, and hot-reload improvements in real-time.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

Framework-Hooks/config/intelligence_patterns.yaml

@@ -0,0 +1,181 @@
+# 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