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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>
59 lines
1.4 KiB
YAML
59 lines
1.4 KiB
YAML
# Mode detection patterns for SuperClaude-Lite
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mode_detection:
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brainstorming:
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enabled: true
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trigger_patterns:
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- "I want to build"
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- "thinking about"
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- "not sure"
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- "maybe.*could"
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- "brainstorm"
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- "explore"
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- "figure out"
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- "unclear.*requirements"
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- "ambiguous.*needs"
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confidence_threshold: 0.7
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auto_activate: true
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task_management:
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enabled: true
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trigger_patterns:
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- "multiple.*tasks"
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- "complex.*system"
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- "build.*comprehensive"
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- "coordinate.*work"
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- "large-scale.*operation"
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- "manage.*operations"
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- "comprehensive.*refactoring"
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- "authentication.*system"
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confidence_threshold: 0.7
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auto_activate: true
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auto_activation_thresholds:
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file_count: 3
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complexity_score: 0.4
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token_efficiency:
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enabled: true
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trigger_patterns:
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- "brief"
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- "concise"
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- "compressed"
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- "efficient.*output"
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- "token.*optimization"
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- "short.*response"
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- "running.*low.*context"
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confidence_threshold: 0.75
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auto_activate: true
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introspection:
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enabled: true
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trigger_patterns:
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- "analyze.*reasoning"
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- "examine.*decision"
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- "reflect.*on"
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- "meta.*cognitive"
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- "thinking.*process"
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- "reasoning.*process"
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- "decision.*made"
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confidence_threshold: 0.6
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auto_activate: true |