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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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# MCP Orchestration Configuration
# Intelligent server selection, coordination, and load balancing patterns
# Enables smart MCP server orchestration based on context and performance
# Metadata
version: "1.0.0"
last_updated: "2025-01-06"
description: "MCP server orchestration intelligence patterns"
# Server Selection Intelligence
server_selection:
decision_tree:
# UI/Design Operations
- name: "ui_component_operations"
conditions:
keywords: ["component", "ui", "design", "frontend", "jsx", "tsx", "css"]
OR:
- operation_type: ["build", "implement", "design"]
- file_extensions: [".jsx", ".tsx", ".vue", ".css", ".scss"]
primary_server: "magic"
support_servers: ["context7"]
coordination_mode: "parallel"
confidence: 0.9
# Analysis and Architecture Operations
- name: "complex_analysis"
conditions:
AND:
- complexity_score: ">0.7"
- operation_type: ["analyze", "review", "debug", "troubleshoot"]
OR:
- file_count: ">10"
- keywords: ["architecture", "system", "complex"]
primary_server: "sequential"
support_servers: ["context7", "serena"]
coordination_mode: "sequential"
confidence: 0.85
# Code Refactoring and Transformation
- name: "code_refactoring"
conditions:
AND:
- operation_type: ["refactor", "transform", "modify"]
OR:
- file_count: ">5"
- complexity_score: ">0.5"
- keywords: ["refactor", "cleanup", "optimize"]
primary_server: "serena"
support_servers: ["morphllm", "sequential"]
coordination_mode: "hybrid"
confidence: 0.8
# Documentation and Learning
- name: "documentation_operations"
conditions:
keywords: ["document", "explain", "guide", "tutorial", "learn"]
OR:
- operation_type: ["document", "explain"]
- file_extensions: [".md", ".rst", ".txt"]
primary_server: "context7"
support_servers: ["sequential"]
coordination_mode: "sequential"
confidence: 0.85
# Testing and Validation
- name: "testing_operations"
conditions:
keywords: ["test", "validate", "check", "verify", "e2e"]
OR:
- operation_type: ["test", "validate"]
- file_patterns: ["*test*", "*spec*", "*e2e*"]
primary_server: "playwright"
support_servers: ["sequential", "magic"]
coordination_mode: "parallel"
confidence: 0.8
# Fast Edits and Transformations
- name: "fast_edits"
conditions:
AND:
- complexity_score: "<0.4"
- file_count: "<5"
operation_type: ["edit", "modify", "fix", "update"]
primary_server: "morphllm"
support_servers: ["serena"]
coordination_mode: "fallback"
confidence: 0.7
# Fallback Strategy
fallback_chain:
default_primary: "sequential"
fallback_sequence: ["context7", "serena", "morphllm", "magic", "playwright"]
fallback_threshold: 3.0 # seconds timeout
# Load Balancing Intelligence
load_balancing:
health_monitoring:
# Server health check configuration
check_interval: 30 # seconds
timeout: 5 # seconds
retry_count: 3
health_metrics:
- response_time
- error_rate
- request_queue_size
- availability_percentage
performance_thresholds:
# Performance-based routing thresholds
response_time:
excellent: 500 # ms
good: 1000 # ms
warning: 2000 # ms
critical: 5000 # ms
error_rate:
excellent: 0.01 # 1%
good: 0.03 # 3%
warning: 0.05 # 5%
critical: 0.15 # 15%
queue_size:
excellent: 0
good: 2
warning: 5
critical: 10
routing_strategies:
# Load balancing algorithms
primary_strategy: "weighted_performance"
strategies:
round_robin:
description: "Distribute requests evenly across healthy servers"
weight_factor: "equal"
weighted_performance:
description: "Route based on server performance metrics"
weight_factors:
response_time: 0.4
error_rate: 0.3
availability: 0.3
least_connections:
description: "Route to server with fewest active connections"
connection_tracking: true
performance_based:
description: "Route to best-performing server"
performance_window: 300 # seconds
# Cross-Server Coordination
coordination_patterns:
sequential_coordination:
# When servers work in sequence
patterns:
- name: "analysis_then_implementation"
sequence: ["sequential", "morphllm"]
trigger: {operation: "implement", analysis_required: true}
- name: "research_then_build"
sequence: ["context7", "magic"]
trigger: {operation: "build", research_required: true}
- name: "plan_then_execute"
sequence: ["sequential", "serena", "morphllm"]
trigger: {complexity: ">0.7", operation: "refactor"}
parallel_coordination:
# When servers work simultaneously
patterns:
- name: "ui_with_docs"
parallel: ["magic", "context7"]
trigger: {operation: "build", component_type: "ui"}
synchronization: "merge_results"
- name: "test_with_validation"
parallel: ["playwright", "sequential"]
trigger: {operation: "test", validation_required: true}
synchronization: "wait_all"
hybrid_coordination:
# Mixed coordination patterns
patterns:
- name: "comprehensive_refactoring"
phases:
- phase: 1
servers: ["sequential"] # Analysis
wait_for_completion: true
- phase: 2
servers: ["serena", "morphllm"] # Parallel execution
synchronization: "coordinate_changes"
# Dynamic Server Capabilities
capability_assessment:
dynamic_capabilities:
# Assess server capabilities in real-time
assessment_interval: 60 # seconds
capability_metrics:
- processing_speed
- accuracy_score
- specialization_match
- current_load
capability_mapping:
# Map operations to server capabilities
magic:
specializations: ["ui", "components", "design", "frontend"]
performance_profile: "medium_latency_high_quality"
optimal_load: 3
sequential:
specializations: ["analysis", "debugging", "complex_reasoning"]
performance_profile: "high_latency_high_quality"
optimal_load: 2
context7:
specializations: ["documentation", "learning", "research"]
performance_profile: "low_latency_medium_quality"
optimal_load: 5
serena:
specializations: ["refactoring", "large_codebases", "semantic_analysis"]
performance_profile: "medium_latency_high_precision"
optimal_load: 3
morphllm:
specializations: ["fast_edits", "transformations", "pattern_matching"]
performance_profile: "low_latency_medium_quality"
optimal_load: 4
playwright:
specializations: ["testing", "validation", "browser_automation"]
performance_profile: "high_latency_specialized"
optimal_load: 2
# Error Handling and Recovery
error_handling:
retry_strategies:
# Server error retry patterns
exponential_backoff:
initial_delay: 1 # seconds
max_delay: 60 # seconds
multiplier: 2
max_retries: 3
graceful_degradation:
# Fallback when servers fail
degradation_levels:
- level: 1
strategy: "use_secondary_server"
performance_impact: "minimal"
- level: 2
strategy: "reduce_functionality"
performance_impact: "moderate"
- level: 3
strategy: "basic_operation_only"
performance_impact: "significant"
circuit_breaker:
# Circuit breaker pattern for failing servers
failure_threshold: 5 # failures before opening circuit
recovery_timeout: 30 # seconds before attempting recovery
half_open_requests: 3 # test requests during recovery
# Performance Optimization
performance_optimization:
caching:
# Server response caching
enable_response_caching: true
cache_duration: 300 # seconds
max_cache_size: 100 # responses
cache_key_strategy: "operation_context_hash"
request_optimization:
# Request batching and optimization
enable_request_batching: true
batch_size: 3
batch_timeout: 1000 # ms
predictive_routing:
# Predict optimal server based on patterns
enable_prediction: true
prediction_model: "pattern_based"
prediction_confidence_threshold: 0.7
# Monitoring and Analytics
monitoring:
metrics_collection:
# Collect orchestration metrics
collect_routing_decisions: true
collect_performance_metrics: true
collect_error_patterns: true
retention_days: 30
analytics:
# Server orchestration analytics
routing_accuracy_tracking: true
performance_trend_analysis: true
optimization_recommendations: true
alerts:
# Alert thresholds
high_error_rate: 0.1 # 10%
slow_response_time: 5000 # ms
server_unavailable: true