SuperClaude/Framework-Hooks/cache/session_9f57690b-3e1a-4533-9902-a7638defd941.json
NomenAK da0a356eec 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>
2025-08-06 13:26:04 +02:00

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{
"session_summary": {
"session_id": "9f57690b-3e1a-4533-9902-a7638defd941",
"duration_minutes": 0.0,
"operations_completed": 0,
"tools_utilized": 0,
"mcp_servers_used": 0,
"superclaude_enabled": false
},
"performance_metrics": {
"overall_score": 0.2,
"productivity_score": 0.0,
"quality_score": 1.0,
"efficiency_score": 0.8,
"satisfaction_estimate": 0.5
},
"superclaude_effectiveness": {
"framework_enabled": false,
"effectiveness_score": 0.0,
"intelligence_utilization": 0.0,
"learning_events_generated": 1,
"adaptations_created": 0
},
"quality_analysis": {
"error_rate": 0.0,
"operation_success_rate": 1.0,
"bottlenecks": [
"low_productivity"
],
"optimization_opportunities": []
},
"learning_summary": {
"insights_generated": 0,
"key_insights": [],
"learning_effectiveness": 0.0
},
"resource_utilization": {},
"session_metadata": {
"start_time": 0,
"end_time": 1754476402.3517025,
"framework_version": "1.0.0",
"analytics_version": "stop_1.0"
}
}