# Validation Intelligence Configuration
# Health scoring, diagnostic patterns, and proactive system validation
# Enables intelligent health monitoring and predictive diagnostics

# Metadata
version: "1.0.0"
last_updated: "2025-01-06"
description: "Validation intelligence and health scoring patterns"

# Health Scoring Framework
health_scoring:
  component_weights:
    # Weighted importance of different system components
    learning_system: 0.25      # 25% - Core intelligence
    performance_system: 0.20   # 20% - System performance
    mcp_coordination: 0.20     # 20% - Server coordination
    hook_system: 0.15          # 15% - Hook execution
    configuration_system: 0.10 # 10% - Configuration management
    cache_system: 0.10         # 10% - Caching and storage
    
  scoring_metrics:
    learning_system:
      pattern_diversity:
        weight: 0.3
        healthy_range: [0.6, 0.95]    # Not too low, not perfect
        critical_threshold: 0.3
        measurement: "pattern_signature_entropy"
        
      effectiveness_consistency:
        weight: 0.3
        healthy_range: [0.7, 0.9]     # Consistent but not perfect
        critical_threshold: 0.5
        measurement: "effectiveness_score_variance"
        
      adaptation_responsiveness:
        weight: 0.2
        healthy_range: [0.6, 1.0]
        critical_threshold: 0.4
        measurement: "adaptation_success_rate"
        
      learning_velocity:
        weight: 0.2
        healthy_range: [0.5, 1.0]
        critical_threshold: 0.3
        measurement: "patterns_learned_per_session"
        
    performance_system:
      response_time_stability:
        weight: 0.4
        healthy_range: [0.7, 1.0]     # Low variance preferred
        critical_threshold: 0.4
        measurement: "response_time_coefficient_variation"
        
      resource_efficiency:
        weight: 0.3
        healthy_range: [0.6, 0.85]    # Efficient but not resource-starved
        critical_threshold: 0.4
        measurement: "resource_utilization_efficiency"
        
      error_rate:
        weight: 0.3
        healthy_range: [0.95, 1.0]    # Low error rate (inverted)
        critical_threshold: 0.8
        measurement: "success_rate"
        
    mcp_coordination:
      server_selection_accuracy:
        weight: 0.4
        healthy_range: [0.8, 1.0]
        critical_threshold: 0.6
        measurement: "optimal_server_selection_rate"
        
      coordination_efficiency:
        weight: 0.3
        healthy_range: [0.7, 1.0]
        critical_threshold: 0.5
        measurement: "coordination_overhead_ratio"
        
      server_availability:
        weight: 0.3
        healthy_range: [0.9, 1.0]
        critical_threshold: 0.7
        measurement: "average_server_availability"

# Proactive Diagnostic Patterns
proactive_diagnostics:
  early_warning_patterns:
    # Detect issues before they become critical
    learning_system_warnings:
      - name: "pattern_overfitting"
        pattern:
          consecutive_perfect_scores: ">15"
          pattern_diversity: "<0.5"
        severity: "medium"
        lead_time: "2-5_days"
        recommendation: "Increase pattern complexity or add noise"
        remediation: "automatic_pattern_diversification"
        
      - name: "learning_stagnation"
        pattern:
          new_patterns_per_day: "<0.1"
          effectiveness_improvement: "<0.01"
          duration: ">7_days"
        severity: "low"
        lead_time: "1-2_weeks"
        recommendation: "Review learning triggers and thresholds"
        
      - name: "adaptation_failure"
        pattern:
          failed_adaptations: ">30%"
          confidence_scores: "decreasing"
          duration: ">3_days"
        severity: "high"
        lead_time: "1-3_days"  
        recommendation: "Review adaptation logic and data quality"
        
    performance_warnings:
      - name: "performance_degradation_trend"
        pattern:
          response_time_trend: "increasing"
          degradation_rate: ">5%_per_week"
          duration: ">10_days"
        severity: "medium"
        lead_time: "1-2_weeks"
        recommendation: "Investigate resource leaks or optimize bottlenecks"
        
      - name: "memory_leak_indication"
        pattern:
          memory_usage_trend: "steadily_increasing"
          memory_cleanup_efficiency: "decreasing"
          duration: ">5_days"
        severity: "high"
        lead_time: "3-7_days"
        recommendation: "Check for memory leaks and optimize garbage collection"
        
      - name: "cache_inefficiency"
        pattern:
          cache_hit_rate: "decreasing"
          cache_size: "growing"
          cache_cleanup_frequency: "increasing"
        severity: "low"
        lead_time: "1_week"
        recommendation: "Optimize cache strategies and cleanup policies"
        
    coordination_warnings:
      - name: "server_selection_degradation"
        pattern:
          suboptimal_selections: "increasing"
          selection_confidence: "decreasing"
          user_satisfaction: "decreasing"
        severity: "medium"
        lead_time: "2-5_days"
        recommendation: "Retrain server selection algorithms"
        
      - name: "coordination_overhead_increase"
        pattern:
          coordination_time: "increasing"
          coordination_complexity: "increasing"
          efficiency_metrics: "decreasing"
        severity: "medium"
        lead_time: "1_week"
        recommendation: "Optimize coordination protocols"

# Predictive Health Analysis
predictive_analysis:
  health_prediction:
    # Predict future health issues
    prediction_models:
      trend_analysis:
        model_type: "linear_regression"
        prediction_horizon: 14  # days
        confidence_threshold: 0.8
        
      pattern_matching:
        model_type: "similarity_search"
        historical_window: 90   # days
        pattern_similarity_threshold: 0.85
        
      anomaly_prediction:
        model_type: "isolation_forest"
        anomaly_threshold: 0.1
        prediction_accuracy_target: 0.75
        
  health_forecasting:
    # Forecast health scores
    forecasting_metrics:
      - metric: "overall_health_score"
        horizon: [1, 7, 14, 30]  # days
        accuracy_target: 0.8
        
      - metric: "component_health_scores"  
        horizon: [1, 7, 14]      # days
        accuracy_target: 0.75
        
      - metric: "critical_issue_probability"
        horizon: [1, 3, 7]       # days
        accuracy_target: 0.85

# Diagnostic Intelligence
diagnostic_intelligence:
  intelligent_diagnosis:
    # Smart diagnostic capabilities
    symptom_analysis:
      symptom_correlation: true
      root_cause_analysis: true
      multi_component_diagnosis: true
      
    diagnostic_algorithms:
      decision_tree:
        algorithm: "gradient_boosted_trees"
        feature_importance_threshold: 0.1
        
      pattern_matching:
        algorithm: "k_nearest_neighbors"
        similarity_metric: "cosine"
        k_value: 5
        
      statistical_analysis:
        algorithm: "hypothesis_testing"
        confidence_level: 0.95
        
  automated_remediation:
    # Automated remediation suggestions
    remediation_patterns:
      - symptom: "high_error_rate"
        diagnosis: "configuration_issue"
        remediation: "reset_to_known_good_config"
        automation_level: "suggest"
        
      - symptom: "memory_leak"
        diagnosis: "cache_overflow"
        remediation: "aggressive_cache_cleanup"
        automation_level: "auto_with_approval"
        
      - symptom: "performance_degradation"
        diagnosis: "resource_exhaustion"
        remediation: "resource_optimization_mode"
        automation_level: "automatic"

# Validation Rules
validation_rules:
  system_consistency:
    # Validate system consistency
    consistency_checks:
      configuration_coherence:
        check_type: "cross_reference"
        validation_frequency: "on_change"
        error_threshold: 0
        
      data_integrity:
        check_type: "checksum_validation"  
        validation_frequency: "hourly"
        error_threshold: 0
        
      dependency_resolution:
        check_type: "graph_validation"
        validation_frequency: "on_startup"
        error_threshold: 0
        
  performance_validation:
    # Validate performance expectations
    performance_checks:
      response_time_validation:
        expected_range: [100, 2000]  # ms
        validation_window: 20        # operations
        failure_threshold: 0.2       # 20% failures allowed
        
      resource_usage_validation:
        expected_range: [0.1, 0.9]   # utilization
        validation_frequency: "continuous"
        alert_threshold: 0.85
        
      throughput_validation:
        expected_minimum: 0.5        # operations per second
        validation_window: 60        # seconds
        degradation_threshold: 0.3   # 30% degradation

# Health Monitoring Intelligence
monitoring_intelligence:
  adaptive_monitoring:
    # Adapt monitoring based on system state
    monitoring_intensity:
      healthy_state:
        sampling_rate: 0.1          # 10% sampling
        check_frequency: 300        # seconds
        
      warning_state:
        sampling_rate: 0.5          # 50% sampling
        check_frequency: 60         # seconds
        
      critical_state:
        sampling_rate: 1.0          # 100% sampling  
        check_frequency: 10         # seconds
        
  intelligent_alerting:
    # Smart alerting to reduce noise
    alert_intelligence:
      alert_correlation: true      # Correlate related alerts
      alert_suppression: true     # Suppress duplicate alerts
      alert_escalation: true      # Escalate based on severity
      
    alert_thresholds:
      health_score_critical: 0.6
      health_score_warning: 0.8
      component_failure: true
      performance_degradation: 0.3  # 30% degradation

# Continuous Validation
continuous_validation:
  validation_cycles:
    # Continuous validation cycles
    real_time_validation:
      validation_frequency: "per_operation"
      validation_scope: "critical_path"
      performance_impact: "minimal"
      
    periodic_validation:
      validation_frequency: "hourly"
      validation_scope: "comprehensive"
      performance_impact: "low"
      
    deep_validation:
      validation_frequency: "daily"
      validation_scope: "exhaustive"
      performance_impact: "acceptable"
      
  validation_evolution:
    # Evolve validation based on findings
    learning_from_failures: true
    adaptive_validation_rules: true
    validation_effectiveness_tracking: true
    
# Quality Assurance Integration  
quality_assurance:
  quality_gates:
    # Integration with quality gates
    gate_validation:
      syntax_validation: "automatic"
      performance_validation: "threshold_based"
      integration_validation: "comprehensive"
      
  continuous_improvement:
    # Continuous quality improvement
    quality_metrics_tracking: true
    validation_accuracy_tracking: true
    false_positive_reduction: true
    diagnostic_accuracy_improvement: true