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feat: Implement YAML-first declarative intelligence architecture

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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>
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2025-08-06 13:26:04 +02:00
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# Performance Intelligence Configuration
# Adaptive performance patterns, auto-optimization, and resource management
# Enables intelligent performance monitoring and self-optimization
# Metadata
version: "1.0.0"
last_updated: "2025-01-06"
description: "Performance intelligence and auto-optimization patterns"
# Adaptive Performance Targets
adaptive_targets:
baseline_management:
# Dynamic baseline adjustment based on system performance
adjustment_strategy: "rolling_average"
adjustment_window: 50 # operations
adjustment_sensitivity: 0.15 # 15% threshold for adjustment
min_samples: 10 # minimum samples before adjustment
baseline_metrics:
response_time:
initial_target: 500 # ms
acceptable_variance: 0.3
improvement_threshold: 0.1
resource_usage:
initial_target: 0.7 # 70%
acceptable_variance: 0.2
critical_threshold: 0.9
error_rate:
initial_target: 0.02 # 2%
acceptable_variance: 0.01
critical_threshold: 0.1
target_adaptation:
# How targets adapt to system capabilities
adaptation_triggers:
- condition: {performance_improvement: ">20%", duration: ">7_days"}
action: "tighten_targets"
adjustment: 0.15
- condition: {performance_degradation: ">15%", duration: ">3_days"}
action: "relax_targets"
adjustment: 0.2
- condition: {system_upgrade_detected: true}
action: "recalibrate_baselines"
reset_period: "24_hours"
adaptation_limits:
max_target_tightening: 0.5 # Don't make targets too aggressive
max_target_relaxation: 2.0 # Don't make targets too loose
adaptation_cooldown: 3600 # seconds between major adjustments
# Auto-Optimization Engine
auto_optimization:
optimization_triggers:
# Automatic optimization triggers
performance_triggers:
- name: "response_time_degradation"
condition: {avg_response_time: ">target*1.3", samples: ">10"}
urgency: "high"
actions: ["enable_aggressive_caching", "reduce_analysis_depth", "parallel_processing"]
- name: "memory_pressure"
condition: {memory_usage: ">0.85", duration: ">300_seconds"}
urgency: "critical"
actions: ["garbage_collection", "cache_cleanup", "reduce_context_size"]
- name: "cpu_saturation"
condition: {cpu_usage: ">0.9", duration: ">60_seconds"}
urgency: "high"
actions: ["reduce_concurrent_operations", "defer_non_critical", "enable_throttling"]
- name: "error_rate_spike"
condition: {error_rate: ">0.1", recent_window: "5_minutes"}
urgency: "critical"
actions: ["enable_fallback_mode", "increase_timeouts", "reduce_complexity"]
optimization_strategies:
# Available optimization strategies
aggressive_caching:
description: "Enable aggressive caching of results and computations"
performance_impact: 0.3 # Expected improvement
resource_cost: 0.1 # Memory cost
duration: 1800 # seconds
parallel_processing:
description: "Increase parallelization where possible"
performance_impact: 0.25
resource_cost: 0.2
duration: 3600
reduce_analysis_depth:
description: "Reduce depth of analysis to improve speed"
performance_impact: 0.4
quality_impact: -0.1 # Slight quality reduction
duration: 1800
intelligent_batching:
description: "Batch similar operations for efficiency"
performance_impact: 0.2
resource_cost: -0.05 # Reduces resource usage
duration: 3600
# Resource Management Intelligence
resource_management:
resource_zones:
# Performance zones with different strategies
green_zone:
threshold: 0.60 # Below 60% resource usage
strategy: "optimal_performance"
features_enabled: ["full_analysis", "comprehensive_caching", "background_optimization"]
yellow_zone:
threshold: 0.75 # 60-75% resource usage
strategy: "balanced_optimization"
features_enabled: ["standard_analysis", "selective_caching", "reduced_background"]
optimizations: ["defer_non_critical", "reduce_verbosity"]
orange_zone:
threshold: 0.85 # 75-85% resource usage
strategy: "performance_preservation"
features_enabled: ["essential_analysis", "minimal_caching"]
optimizations: ["aggressive_caching", "parallel_where_safe", "reduce_context"]
red_zone:
threshold: 0.95 # 85-95% resource usage
strategy: "resource_conservation"
features_enabled: ["critical_only"]
optimizations: ["emergency_cleanup", "minimal_processing", "fail_fast"]
critical_zone:
threshold: 1.0 # Above 95% resource usage
strategy: "emergency_mode"
features_enabled: []
optimizations: ["immediate_cleanup", "operation_rejection", "system_protection"]
dynamic_allocation:
# Intelligent resource allocation
allocation_strategies:
workload_based:
description: "Allocate based on current workload patterns"
factors: ["operation_complexity", "expected_duration", "priority"]
predictive:
description: "Allocate based on predicted resource needs"
factors: ["historical_patterns", "operation_type", "context_size"]
adaptive:
description: "Adapt allocation based on real-time performance"
factors: ["current_performance", "resource_availability", "optimization_goals"]
# Performance Regression Detection
regression_detection:
detection_algorithms:
# Algorithms for detecting performance regression
statistical_analysis:
algorithm: "t_test"
confidence_level: 0.95
minimum_samples: 20
window_size: 100 # operations
trend_analysis:
algorithm: "linear_regression"
trend_threshold: 0.1 # 10% degradation trend
analysis_window: 168 # hours (1 week)
anomaly_detection:
algorithm: "isolation_forest"
contamination: 0.1 # Expected anomaly rate
sensitivity: 0.8
regression_patterns:
# Common regression patterns to detect
gradual_degradation:
pattern: {performance_trend: "decreasing", duration: ">5_days"}
severity: "medium"
investigation: "check_for_memory_leaks"
sudden_degradation:
pattern: {performance_drop: ">30%", timeframe: "<1_hour"}
severity: "high"
investigation: "check_recent_changes"
periodic_degradation:
pattern: {performance_cycles: "detected", frequency: "regular"}
severity: "low"
investigation: "analyze_periodic_patterns"
# Intelligent Resource Optimization
intelligent_optimization:
predictive_optimization:
# Predict and prevent performance issues
prediction_models:
resource_exhaustion:
model_type: "time_series"
prediction_horizon: 3600 # seconds
accuracy_threshold: 0.8
performance_degradation:
model_type: "pattern_matching"
pattern_library: "historical_degradations"
confidence_threshold: 0.7
proactive_actions:
- prediction: "memory_exhaustion"
lead_time: 1800 # seconds
actions: ["preemptive_cleanup", "cache_optimization", "context_reduction"]
- prediction: "cpu_saturation"
lead_time: 600 # seconds
actions: ["reduce_parallelism", "defer_background_tasks", "enable_throttling"]
optimization_recommendations:
# Generate optimization recommendations
recommendation_engine:
analysis_depth: "comprehensive"
recommendation_confidence: 0.8
implementation_difficulty: "user_friendly"
recommendation_types:
configuration_tuning:
description: "Suggest configuration changes for better performance"
impact_assessment: "quantified"
resource_allocation:
description: "Recommend better resource allocation strategies"
cost_benefit_analysis: true
workflow_optimization:
description: "Suggest workflow improvements"
user_experience_impact: "minimal"
# Performance Monitoring Intelligence
monitoring_intelligence:
intelligent_metrics:
# Smart metric collection and analysis
adaptive_sampling:
base_sampling_rate: 1.0 # Sample every operation
high_load_rate: 0.5 # Reduce sampling under load
critical_load_rate: 0.1 # Minimal sampling in critical situations
contextual_metrics:
# Collect different metrics based on context
ui_operations:
focus_metrics: ["response_time", "render_time", "user_interaction_delay"]
analysis_operations:
focus_metrics: ["processing_time", "memory_usage", "accuracy_score"]
batch_operations:
focus_metrics: ["throughput", "resource_efficiency", "completion_rate"]
performance_insights:
# Generate performance insights
insight_generation:
pattern_recognition: true
correlation_analysis: true
root_cause_analysis: true
improvement_suggestions: true
insight_types:
bottleneck_identification:
description: "Identify performance bottlenecks"
priority: "high"
optimization_opportunities:
description: "Find optimization opportunities"
priority: "medium"
capacity_planning:
description: "Predict capacity requirements"
priority: "low"
# Performance Validation
performance_validation:
validation_framework:
# Validate performance improvements
a_b_testing:
enable_automatic_testing: true
test_duration: 3600 # seconds
statistical_significance: 0.95
performance_benchmarking:
benchmark_frequency: "weekly"
regression_threshold: 0.05 # 5% regression tolerance
continuous_improvement:
# Continuous performance improvement
improvement_tracking:
track_optimization_effectiveness: true
measure_user_satisfaction: true
monitor_system_health: true
feedback_loops:
performance_feedback: "real_time"
user_feedback_integration: true
system_learning_integration: true