mirror of
https://github.com/SuperClaude-Org/SuperClaude_Framework.git
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da0a356eec
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>
51 lines
1.3 KiB
JSON
51 lines
1.3 KiB
JSON
{
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"session_summary": {
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"session_id": "3087d7e3-8411-4b9f-9929-33eb542bc5ab",
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"duration_minutes": 0.0,
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"operations_completed": 0,
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"tools_utilized": 0,
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"mcp_servers_used": 0,
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"superclaude_enabled": false
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},
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"performance_metrics": {
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"overall_score": 0.2,
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"productivity_score": 0.0,
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"quality_score": 1.0,
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"efficiency_score": 0.8,
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"satisfaction_estimate": 0.5
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},
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"superclaude_effectiveness": {
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"framework_enabled": false,
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"effectiveness_score": 0.0,
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"intelligence_utilization": 0.0,
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"learning_events_generated": 2,
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"adaptations_created": 0
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},
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"quality_analysis": {
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"error_rate": 0.0,
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"operation_success_rate": 1.0,
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"bottlenecks": [
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"low_productivity"
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],
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"optimization_opportunities": []
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},
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"learning_summary": {
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"insights_generated": 1,
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"key_insights": [
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{
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"insight_type": "effectiveness_concern",
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"description": "SuperClaude effectiveness below optimal",
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"confidence": 0.4105,
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"impact_score": 0.8
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}
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],
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"learning_effectiveness": 0.3284
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},
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"resource_utilization": {},
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"session_metadata": {
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"start_time": 0,
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"end_time": 1754472203.8801544,
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"framework_version": "1.0.0",
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"analytics_version": "stop_1.0"
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}
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} |