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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>
82 lines
1.9 KiB
JSON
82 lines
1.9 KiB
JSON
{
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"session_id": "91a37b4e-f0f3-41bb-9143-01dc8ce45a2c",
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"superclaude_enabled": true,
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"initialization_timestamp": 1754476722.0451996,
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"active_modes": [],
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"mode_configurations": {},
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"mcp_servers": {
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"enabled_servers": [
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"morphllm",
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"sequential"
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],
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"activation_order": [
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"morphllm",
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"sequential"
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],
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"coordination_strategy": "collaborative"
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},
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"compression": {
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"compression_level": "minimal",
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"estimated_savings": {
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"token_reduction": 0.15,
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"time_savings": 0.05
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},
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"quality_impact": 0.98,
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"selective_compression_enabled": true
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},
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"performance": {
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"resource_monitoring_enabled": true,
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"optimization_targets": {
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"session_start_ms": 50,
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"tool_routing_ms": 200,
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"validation_ms": 100,
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"compression_ms": 150,
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"enabled": true,
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"real_time_tracking": true,
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"target_enforcement": true,
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"optimization_suggestions": true,
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"performance_analytics": true
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},
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"delegation_threshold": 0.6
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},
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"learning": {
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"adaptation_enabled": true,
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"effectiveness_tracking": true,
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"applied_adaptations": [
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{
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"id": "adapt_1754413397_2",
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"confidence": 0.8,
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"effectiveness": 1.0
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},
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{
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"id": "adapt_1754411689_0",
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"confidence": 0.9,
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"effectiveness": 0.8
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},
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{
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"id": "adapt_1754411724_1",
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"confidence": 0.8,
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"effectiveness": 0.9
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}
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]
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},
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"context": {
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"project_type": "unknown",
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"complexity_score": 0.0,
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"brainstorming_mode": false,
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"user_expertise": "intermediate"
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},
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"quality_gates": [
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"syntax_validation"
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],
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"metadata": {
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"framework_version": "1.0.0",
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"hook_version": "session_start_1.0",
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"configuration_source": "superclaude_intelligence"
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},
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"performance_metrics": {
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"initialization_time_ms": 31.55827522277832,
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"target_met": true,
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"efficiency_score": 0.3688344955444336
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}
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} |