SuperClaude/Framework-Hooks/cache/session_55ca6726.json

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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
{
"session_summary": {
"session_id": "55ca6726",
"duration_minutes": 0.0,
"operations_completed": 9,
"tools_utilized": 9,
"mcp_servers_used": 0,
"superclaude_enabled": false
},
"performance_metrics": {
"overall_score": 0.6000000000000001,
"productivity_score": 1.0,
"quality_score": 1.0,
"efficiency_score": 0.8,
"satisfaction_estimate": 1.0
},
"superclaude_effectiveness": {
"framework_enabled": false,
"effectiveness_score": 0.0,
"intelligence_utilization": 0.0,
"learning_events_generated": 2,
"adaptations_created": 0
},
"quality_analysis": {
"error_rate": 0.0,
"operation_success_rate": 1.0,
"bottlenecks": [],
"optimization_opportunities": [
"mcp_server_coordination"
]
},
"learning_summary": {
"insights_generated": 1,
"key_insights": [
{
"insight_type": "effectiveness_concern",
"description": "SuperClaude effectiveness below optimal",
"confidence": 0.42799999999999994,
"impact_score": 0.8
}
],
"learning_effectiveness": 0.3424
},
"resource_utilization": {},
"session_metadata": {
"start_time": 0,
"end_time": 1754476829.542602,
"framework_version": "1.0.0",
"analytics_version": "stop_1.0"
}
}