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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>
347 lines
11 KiB
YAML
347 lines
11 KiB
YAML
# Validation Intelligence Configuration
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# Health scoring, diagnostic patterns, and proactive system validation
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# Enables intelligent health monitoring and predictive diagnostics
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# Metadata
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version: "1.0.0"
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last_updated: "2025-01-06"
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description: "Validation intelligence and health scoring patterns"
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# Health Scoring Framework
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health_scoring:
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component_weights:
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# Weighted importance of different system components
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learning_system: 0.25 # 25% - Core intelligence
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performance_system: 0.20 # 20% - System performance
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mcp_coordination: 0.20 # 20% - Server coordination
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hook_system: 0.15 # 15% - Hook execution
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configuration_system: 0.10 # 10% - Configuration management
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cache_system: 0.10 # 10% - Caching and storage
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scoring_metrics:
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learning_system:
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pattern_diversity:
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weight: 0.3
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healthy_range: [0.6, 0.95] # Not too low, not perfect
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critical_threshold: 0.3
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measurement: "pattern_signature_entropy"
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effectiveness_consistency:
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weight: 0.3
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healthy_range: [0.7, 0.9] # Consistent but not perfect
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critical_threshold: 0.5
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measurement: "effectiveness_score_variance"
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adaptation_responsiveness:
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weight: 0.2
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healthy_range: [0.6, 1.0]
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critical_threshold: 0.4
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measurement: "adaptation_success_rate"
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learning_velocity:
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weight: 0.2
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healthy_range: [0.5, 1.0]
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critical_threshold: 0.3
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measurement: "patterns_learned_per_session"
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performance_system:
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response_time_stability:
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weight: 0.4
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healthy_range: [0.7, 1.0] # Low variance preferred
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critical_threshold: 0.4
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measurement: "response_time_coefficient_variation"
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resource_efficiency:
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weight: 0.3
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healthy_range: [0.6, 0.85] # Efficient but not resource-starved
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critical_threshold: 0.4
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measurement: "resource_utilization_efficiency"
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error_rate:
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weight: 0.3
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healthy_range: [0.95, 1.0] # Low error rate (inverted)
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critical_threshold: 0.8
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measurement: "success_rate"
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mcp_coordination:
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server_selection_accuracy:
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weight: 0.4
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healthy_range: [0.8, 1.0]
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critical_threshold: 0.6
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measurement: "optimal_server_selection_rate"
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coordination_efficiency:
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weight: 0.3
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healthy_range: [0.7, 1.0]
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critical_threshold: 0.5
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measurement: "coordination_overhead_ratio"
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server_availability:
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weight: 0.3
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healthy_range: [0.9, 1.0]
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critical_threshold: 0.7
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measurement: "average_server_availability"
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# Proactive Diagnostic Patterns
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proactive_diagnostics:
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early_warning_patterns:
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# Detect issues before they become critical
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learning_system_warnings:
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- name: "pattern_overfitting"
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pattern:
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consecutive_perfect_scores: ">15"
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pattern_diversity: "<0.5"
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severity: "medium"
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lead_time: "2-5_days"
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recommendation: "Increase pattern complexity or add noise"
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remediation: "automatic_pattern_diversification"
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- name: "learning_stagnation"
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pattern:
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new_patterns_per_day: "<0.1"
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effectiveness_improvement: "<0.01"
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duration: ">7_days"
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severity: "low"
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lead_time: "1-2_weeks"
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recommendation: "Review learning triggers and thresholds"
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- name: "adaptation_failure"
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pattern:
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failed_adaptations: ">30%"
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confidence_scores: "decreasing"
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duration: ">3_days"
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severity: "high"
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lead_time: "1-3_days"
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recommendation: "Review adaptation logic and data quality"
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performance_warnings:
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- name: "performance_degradation_trend"
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pattern:
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response_time_trend: "increasing"
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degradation_rate: ">5%_per_week"
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duration: ">10_days"
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severity: "medium"
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lead_time: "1-2_weeks"
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recommendation: "Investigate resource leaks or optimize bottlenecks"
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- name: "memory_leak_indication"
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pattern:
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memory_usage_trend: "steadily_increasing"
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memory_cleanup_efficiency: "decreasing"
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duration: ">5_days"
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severity: "high"
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lead_time: "3-7_days"
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recommendation: "Check for memory leaks and optimize garbage collection"
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- name: "cache_inefficiency"
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pattern:
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cache_hit_rate: "decreasing"
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cache_size: "growing"
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cache_cleanup_frequency: "increasing"
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severity: "low"
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lead_time: "1_week"
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recommendation: "Optimize cache strategies and cleanup policies"
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coordination_warnings:
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- name: "server_selection_degradation"
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pattern:
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suboptimal_selections: "increasing"
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selection_confidence: "decreasing"
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user_satisfaction: "decreasing"
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severity: "medium"
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lead_time: "2-5_days"
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recommendation: "Retrain server selection algorithms"
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- name: "coordination_overhead_increase"
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pattern:
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coordination_time: "increasing"
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coordination_complexity: "increasing"
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efficiency_metrics: "decreasing"
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severity: "medium"
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lead_time: "1_week"
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recommendation: "Optimize coordination protocols"
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# Predictive Health Analysis
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predictive_analysis:
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health_prediction:
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# Predict future health issues
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prediction_models:
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trend_analysis:
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model_type: "linear_regression"
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prediction_horizon: 14 # days
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confidence_threshold: 0.8
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pattern_matching:
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model_type: "similarity_search"
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historical_window: 90 # days
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pattern_similarity_threshold: 0.85
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anomaly_prediction:
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model_type: "isolation_forest"
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anomaly_threshold: 0.1
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prediction_accuracy_target: 0.75
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health_forecasting:
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# Forecast health scores
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forecasting_metrics:
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- metric: "overall_health_score"
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horizon: [1, 7, 14, 30] # days
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accuracy_target: 0.8
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- metric: "component_health_scores"
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horizon: [1, 7, 14] # days
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accuracy_target: 0.75
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- metric: "critical_issue_probability"
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horizon: [1, 3, 7] # days
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accuracy_target: 0.85
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# Diagnostic Intelligence
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diagnostic_intelligence:
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intelligent_diagnosis:
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# Smart diagnostic capabilities
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symptom_analysis:
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symptom_correlation: true
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root_cause_analysis: true
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multi_component_diagnosis: true
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diagnostic_algorithms:
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decision_tree:
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algorithm: "gradient_boosted_trees"
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feature_importance_threshold: 0.1
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pattern_matching:
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algorithm: "k_nearest_neighbors"
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similarity_metric: "cosine"
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k_value: 5
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statistical_analysis:
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algorithm: "hypothesis_testing"
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confidence_level: 0.95
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automated_remediation:
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# Automated remediation suggestions
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remediation_patterns:
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- symptom: "high_error_rate"
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diagnosis: "configuration_issue"
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remediation: "reset_to_known_good_config"
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automation_level: "suggest"
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- symptom: "memory_leak"
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diagnosis: "cache_overflow"
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remediation: "aggressive_cache_cleanup"
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automation_level: "auto_with_approval"
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- symptom: "performance_degradation"
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diagnosis: "resource_exhaustion"
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remediation: "resource_optimization_mode"
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automation_level: "automatic"
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# Validation Rules
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validation_rules:
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system_consistency:
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# Validate system consistency
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consistency_checks:
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configuration_coherence:
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check_type: "cross_reference"
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validation_frequency: "on_change"
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error_threshold: 0
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data_integrity:
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check_type: "checksum_validation"
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validation_frequency: "hourly"
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error_threshold: 0
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dependency_resolution:
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check_type: "graph_validation"
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validation_frequency: "on_startup"
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error_threshold: 0
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performance_validation:
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# Validate performance expectations
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performance_checks:
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response_time_validation:
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expected_range: [100, 2000] # ms
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validation_window: 20 # operations
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failure_threshold: 0.2 # 20% failures allowed
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resource_usage_validation:
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expected_range: [0.1, 0.9] # utilization
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validation_frequency: "continuous"
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alert_threshold: 0.85
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throughput_validation:
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expected_minimum: 0.5 # operations per second
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validation_window: 60 # seconds
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degradation_threshold: 0.3 # 30% degradation
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# Health Monitoring Intelligence
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monitoring_intelligence:
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adaptive_monitoring:
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# Adapt monitoring based on system state
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monitoring_intensity:
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healthy_state:
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sampling_rate: 0.1 # 10% sampling
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check_frequency: 300 # seconds
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warning_state:
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sampling_rate: 0.5 # 50% sampling
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check_frequency: 60 # seconds
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critical_state:
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sampling_rate: 1.0 # 100% sampling
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check_frequency: 10 # seconds
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intelligent_alerting:
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# Smart alerting to reduce noise
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alert_intelligence:
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alert_correlation: true # Correlate related alerts
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alert_suppression: true # Suppress duplicate alerts
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alert_escalation: true # Escalate based on severity
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alert_thresholds:
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health_score_critical: 0.6
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health_score_warning: 0.8
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component_failure: true
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performance_degradation: 0.3 # 30% degradation
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# Continuous Validation
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continuous_validation:
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validation_cycles:
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# Continuous validation cycles
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real_time_validation:
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validation_frequency: "per_operation"
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validation_scope: "critical_path"
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performance_impact: "minimal"
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periodic_validation:
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validation_frequency: "hourly"
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validation_scope: "comprehensive"
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performance_impact: "low"
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deep_validation:
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validation_frequency: "daily"
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validation_scope: "exhaustive"
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performance_impact: "acceptable"
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validation_evolution:
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# Evolve validation based on findings
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learning_from_failures: true
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adaptive_validation_rules: true
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validation_effectiveness_tracking: true
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# Quality Assurance Integration
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quality_assurance:
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quality_gates:
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# Integration with quality gates
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gate_validation:
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syntax_validation: "automatic"
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performance_validation: "threshold_based"
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integration_validation: "comprehensive"
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continuous_improvement:
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# Continuous quality improvement
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quality_metrics_tracking: true
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validation_accuracy_tracking: true
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false_positive_reduction: true
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diagnostic_accuracy_improvement: true |