feat: Implement YAML-first declarative intelligence architecture

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>
This commit is contained in:
NomenAK
2025-08-06 13:26:04 +02:00
parent 73dfcbb228
commit da0a356eec
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# Hook Coordination Configuration
# Intelligent hook execution patterns, dependency resolution, and optimization
# Enables smart coordination of all Framework-Hooks lifecycle events
# Metadata
version: "1.0.0"
last_updated: "2025-01-06"
description: "Hook coordination and execution intelligence patterns"
# Hook Execution Patterns
execution_patterns:
parallel_execution:
# Independent hooks that can run simultaneously
groups:
- name: "independent_analysis"
hooks: ["compression_engine", "pattern_detection"]
description: "Compression and pattern analysis can run independently"
max_parallel: 2
timeout: 5000 # ms
- name: "intelligence_gathering"
hooks: ["mcp_intelligence", "learning_engine"]
description: "MCP and learning intelligence can run in parallel"
max_parallel: 2
timeout: 3000 # ms
- name: "background_optimization"
hooks: ["performance_monitor", "cache_management"]
description: "Performance monitoring and cache operations"
max_parallel: 2
timeout: 2000 # ms
sequential_execution:
# Hooks that must run in specific order
chains:
- name: "session_lifecycle"
sequence: ["session_start", "pre_tool_use", "post_tool_use", "stop"]
description: "Core session lifecycle must be sequential"
mandatory: true
break_on_error: true
- name: "context_preparation"
sequence: ["session_start", "context_loading", "pattern_detection"]
description: "Context must be prepared before pattern analysis"
conditional: {session_type: "complex"}
- name: "optimization_chain"
sequence: ["compression_engine", "performance_monitor", "learning_engine"]
description: "Optimization workflow sequence"
trigger: {optimization_mode: true}
conditional_execution:
# Hooks that execute based on context conditions
rules:
- hook: "compression_engine"
conditions:
- resource_usage: ">0.75"
- conversation_length: ">50"
- enable_compression: true
priority: "high"
- hook: "pattern_detection"
conditions:
- complexity_score: ">0.5"
- enable_pattern_analysis: true
OR:
- operation_type: ["analyze", "review", "debug"]
priority: "medium"
- hook: "mcp_intelligence"
conditions:
- mcp_servers_available: true
- operation_requires_mcp: true
priority: "high"
- hook: "learning_engine"
conditions:
- learning_enabled: true
- session_type: ["interactive", "complex"]
priority: "medium"
- hook: "performance_monitor"
conditions:
- performance_monitoring: true
OR:
- complexity_score: ">0.7"
- resource_usage: ">0.8"
priority: "low"
# Dependency Resolution
dependency_resolution:
hook_dependencies:
# Define dependencies between hooks
session_start:
requires: []
provides: ["session_context", "initial_state"]
pre_tool_use:
requires: ["session_context"]
provides: ["tool_context", "pre_analysis"]
depends_on: ["session_start"]
compression_engine:
requires: ["session_context"]
provides: ["compression_config", "optimized_context"]
optional_depends: ["session_start"]
pattern_detection:
requires: ["session_context"]
provides: ["detected_patterns", "pattern_insights"]
optional_depends: ["session_start", "compression_engine"]
mcp_intelligence:
requires: ["tool_context", "detected_patterns"]
provides: ["mcp_recommendations", "server_selection"]
depends_on: ["pre_tool_use"]
optional_depends: ["pattern_detection"]
post_tool_use:
requires: ["tool_context", "tool_results"]
provides: ["post_analysis", "performance_metrics"]
depends_on: ["pre_tool_use"]
learning_engine:
requires: ["post_analysis", "performance_metrics"]
provides: ["learning_insights", "adaptations"]
depends_on: ["post_tool_use"]
optional_depends: ["mcp_intelligence", "pattern_detection"]
stop:
requires: ["session_context"]
provides: ["session_summary", "cleanup_status"]
depends_on: ["session_start"]
optional_depends: ["learning_engine", "post_tool_use"]
resolution_strategies:
# How to resolve dependency conflicts
missing_dependency:
strategy: "graceful_degradation"
fallback: "skip_optional"
circular_dependency:
strategy: "break_weakest_link"
priority_order: ["session_start", "pre_tool_use", "post_tool_use", "stop"]
timeout_handling:
strategy: "continue_without_dependency"
timeout_threshold: 10000 # ms
# Performance Optimization
performance_optimization:
execution_optimization:
# Optimize hook execution based on context
fast_path:
conditions:
- complexity_score: "<0.3"
- operation_type: ["simple", "basic"]
- resource_usage: "<0.5"
optimizations:
- skip_non_essential_hooks: true
- reduce_analysis_depth: true
- enable_aggressive_caching: true
- parallel_where_possible: true
comprehensive_path:
conditions:
- complexity_score: ">0.7"
- operation_type: ["complex", "analysis"]
- accuracy_priority: "high"
optimizations:
- enable_all_hooks: true
- deep_analysis_mode: true
- cross_hook_coordination: true
- detailed_logging: true
resource_management:
# Manage resource usage across hooks
resource_budgets:
cpu_budget: 80 # percent
memory_budget: 70 # percent
time_budget: 15000 # ms total
resource_allocation:
session_lifecycle: 30 # percent of budget
intelligence_hooks: 40 # percent
optimization_hooks: 30 # percent
caching_strategies:
# Hook result caching
cacheable_hooks:
- hook: "pattern_detection"
cache_key: ["session_context", "operation_type"]
cache_duration: 300 # seconds
- hook: "mcp_intelligence"
cache_key: ["operation_context", "available_servers"]
cache_duration: 600 # seconds
- hook: "compression_engine"
cache_key: ["context_size", "compression_level"]
cache_duration: 1800 # seconds
# Context-Aware Execution
context_awareness:
operation_context:
# Adapt execution based on operation context
context_patterns:
- context_type: "ui_development"
hook_priorities: ["mcp_intelligence", "pattern_detection", "compression_engine"]
preferred_execution: "fast_parallel"
- context_type: "code_analysis"
hook_priorities: ["pattern_detection", "mcp_intelligence", "learning_engine"]
preferred_execution: "comprehensive_sequential"
- context_type: "performance_optimization"
hook_priorities: ["performance_monitor", "compression_engine", "pattern_detection"]
preferred_execution: "resource_optimized"
user_preferences:
# Adapt to user preferences and patterns
preference_patterns:
- user_type: "performance_focused"
optimizations: ["aggressive_caching", "parallel_execution", "skip_optional"]
- user_type: "quality_focused"
optimizations: ["comprehensive_analysis", "detailed_validation", "full_coordination"]
- user_type: "speed_focused"
optimizations: ["fast_path", "minimal_hooks", "cached_results"]
# Error Handling and Recovery
error_handling:
error_recovery:
# Hook failure recovery strategies
recovery_strategies:
- error_type: "timeout"
recovery: "continue_without_hook"
log_level: "warning"
- error_type: "dependency_missing"
recovery: "graceful_degradation"
log_level: "info"
- error_type: "critical_failure"
recovery: "abort_and_cleanup"
log_level: "error"
resilience_patterns:
# Make hook execution resilient
resilience_features:
retry_failed_hooks: true
max_retries: 2
retry_backoff: "exponential" # linear, exponential
graceful_degradation: true
fallback_to_basic: true
preserve_essential_hooks: ["session_start", "stop"]
error_isolation: true
prevent_error_cascade: true
maintain_session_integrity: true
# Hook Lifecycle Management
lifecycle_management:
hook_states:
# Track hook execution states
state_tracking:
- pending
- initializing
- running
- completed
- failed
- skipped
- timeout
lifecycle_events:
# Events during hook execution
event_handlers:
before_hook_execution:
actions: ["validate_dependencies", "check_resources", "prepare_context"]
after_hook_execution:
actions: ["update_metrics", "cache_results", "trigger_dependent_hooks"]
hook_failure:
actions: ["log_error", "attempt_recovery", "notify_dependent_hooks"]
monitoring:
# Monitor hook execution
performance_tracking:
track_execution_time: true
track_resource_usage: true
track_success_rate: true
track_dependency_resolution: true
health_monitoring:
hook_health_checks: true
dependency_health_checks: true
performance_degradation_detection: true
# Dynamic Configuration
dynamic_configuration:
adaptive_execution:
# Adapt execution patterns based on performance
adaptation_triggers:
- performance_degradation: ">20%"
action: "switch_to_fast_path"
- error_rate: ">10%"
action: "enable_resilience_mode"
- resource_pressure: ">90%"
action: "reduce_hook_scope"
learning_integration:
# Learn from hook execution patterns
learning_features:
learn_optimal_execution_order: true
learn_user_preferences: true
learn_performance_patterns: true
adapt_to_project_context: true

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# Intelligence Patterns Configuration
# Core learning intelligence patterns for SuperClaude Framework-Hooks
# Defines multi-dimensional pattern recognition, adaptive learning, and intelligence behaviors
# Metadata
version: "1.0.0"
last_updated: "2025-01-06"
description: "Core intelligence patterns for declarative learning and adaptation"
# Learning Intelligence Configuration
learning_intelligence:
pattern_recognition:
# Multi-dimensional pattern analysis
dimensions:
primary:
- context_type # Type of operation context
- complexity_score # Operation complexity (0.0-1.0)
- operation_type # Category of operation
- performance_score # Performance effectiveness (0.0-1.0)
secondary:
- file_count # Number of files involved
- directory_count # Number of directories
- mcp_server # MCP server involved
- user_expertise # Detected user skill level
# Pattern signature generation
signature_generation:
method: "multi_dimensional_hash"
include_context: true
fallback_signature: "unknown_pattern"
max_signature_length: 128
# Pattern clustering for similar behavior grouping
clustering:
algorithm: "k_means"
min_cluster_size: 3
max_clusters: 20
similarity_threshold: 0.8
recalculate_interval: 100 # operations
adaptive_learning:
# Dynamic learning rate adjustment
learning_rate:
initial: 0.7
min: 0.1
max: 1.0
adaptation_strategy: "confidence_based"
# Confidence scoring
confidence_scoring:
base_confidence: 0.5
consistency_weight: 0.4
frequency_weight: 0.3
recency_weight: 0.3
# Effectiveness thresholds
effectiveness_thresholds:
learn_threshold: 0.7 # Minimum effectiveness to create adaptation
confidence_threshold: 0.6 # Minimum confidence to apply adaptation
forget_threshold: 0.3 # Below this, remove adaptation
pattern_quality:
# Pattern validation rules
validation_rules:
min_usage_count: 3
max_consecutive_perfect_scores: 10
effectiveness_variance_limit: 0.5
required_dimensions: ["context_type", "operation_type"]
# Quality scoring
quality_metrics:
diversity_score_weight: 0.4
consistency_score_weight: 0.3
usage_frequency_weight: 0.3
# Pattern Analysis Configuration
pattern_analysis:
anomaly_detection:
# Detect unusual patterns that might indicate issues
anomaly_patterns:
- name: "overfitting_detection"
condition: {consecutive_perfect_scores: ">10"}
severity: "medium"
action: "flag_for_review"
- name: "pattern_stagnation"
condition: {no_new_patterns: ">30_days"}
severity: "low"
action: "suggest_pattern_diversity"
- name: "effectiveness_degradation"
condition: {effectiveness_trend: "decreasing", duration: ">7_days"}
severity: "high"
action: "trigger_pattern_analysis"
trend_analysis:
# Track learning trends over time
tracking_windows:
short_term: 24 # hours
medium_term: 168 # hours (1 week)
long_term: 720 # hours (30 days)
trend_indicators:
- effectiveness_trend
- pattern_diversity_trend
- confidence_trend
- usage_frequency_trend
# Intelligence Enhancement Patterns
intelligence_enhancement:
predictive_capabilities:
# Predictive pattern matching
prediction_horizon: 5 # operations ahead
prediction_confidence_threshold: 0.7
prediction_accuracy_tracking: true
context_awareness:
# Context understanding and correlation
context_correlation:
enable_cross_session: true
enable_project_correlation: true
enable_user_correlation: true
correlation_strength_threshold: 0.6
adaptive_strategies:
# Strategy adaptation based on performance
strategy_adaptation:
performance_window: 20 # operations
adaptation_threshold: 0.8
rollback_threshold: 0.5
max_adaptations_per_session: 5
# Pattern Lifecycle Management
lifecycle_management:
pattern_evolution:
# How patterns evolve over time
evolution_triggers:
- usage_count_milestone: [10, 50, 100, 500]
- effectiveness_improvement: 0.1
- confidence_improvement: 0.1
evolution_actions:
- promote_to_global
- increase_weight
- expand_context
- merge_similar_patterns
pattern_cleanup:
# Automatic pattern cleanup
cleanup_triggers:
max_patterns: 1000
unused_pattern_age: 30 # days
low_effectiveness_threshold: 0.3
cleanup_strategies:
- archive_unused
- merge_similar
- remove_ineffective
- compress_historical
# Integration Configuration
integration:
cache_management:
# Pattern caching for performance
cache_patterns: true
cache_duration: 3600 # seconds
max_cache_size: 100 # patterns
cache_invalidation: "smart" # smart, time_based, usage_based
performance_optimization:
# Performance tuning
lazy_loading: true
batch_processing: true
background_analysis: true
max_processing_time_ms: 100
compatibility:
# Backwards compatibility
support_legacy_patterns: true
migration_assistance: true
graceful_degradation: true

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# Core Logging Settings
logging:
enabled: true
level: "INFO" # ERROR, WARNING, INFO, DEBUG
enabled: false
level: "ERROR" # ERROR, WARNING, INFO, DEBUG
# File Settings
file_settings:
@@ -14,10 +14,10 @@ logging:
# Hook Logging Settings
hook_logging:
log_lifecycle: true # Log hook start/end events
log_decisions: true # Log decision points
log_errors: true # Log error events
log_timing: true # Include timing information
log_lifecycle: false # Log hook start/end events
log_decisions: false # Log decision points
log_errors: false # Log error events
log_timing: false # Include timing information
# Performance Settings
performance:
@@ -65,6 +65,6 @@ hook_configuration:
# Development Settings
development:
verbose_errors: true
verbose_errors: false
include_stack_traces: false # Keep logs clean
debug_mode: false

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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

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# SuperClaude-Lite Modes Configuration
# Mode detection patterns and behavioral configurations
# Mode Detection Patterns
# Mode detection patterns for SuperClaude-Lite
mode_detection:
brainstorming:
description: "Interactive requirements discovery and exploration"
activation_type: "automatic"
enabled: true
trigger_patterns:
- "I want to build"
- "thinking about"
- "not sure"
- "maybe.*could"
- "brainstorm"
- "explore"
- "figure out"
- "unclear.*requirements"
- "ambiguous.*needs"
confidence_threshold: 0.7
trigger_patterns:
vague_requests:
- "i want to build"
- "thinking about"
- "not sure"
- "maybe we could"
- "what if we"
- "considering"
exploration_keywords:
- "brainstorm"
- "explore"
- "discuss"
- "figure out"
- "work through"
- "think through"
uncertainty_indicators:
- "maybe"
- "possibly"
- "perhaps"
- "could we"
- "would it be possible"
- "wondering if"
project_initiation:
- "new project"
- "startup idea"
- "feature concept"
- "app idea"
- "building something"
behavioral_settings:
dialogue_style: "collaborative_non_presumptive"
discovery_depth: "adaptive"
context_retention: "cross_session"
handoff_automation: true
integration:
command_trigger: "/sc:brainstorm"
mcp_servers: ["sequential", "context7"]
quality_gates: ["requirements_clarity", "brief_completeness"]
auto_activate: true
task_management:
description: "Multi-layer task orchestration with delegation and wave systems"
activation_type: "automatic"
confidence_threshold: 0.8
enabled: true
trigger_patterns:
multi_step_operations:
- "build"
- "implement"
- "create"
- "develop"
- "set up"
- "establish"
scope_indicators:
- "system"
- "feature"
- "comprehensive"
- "complete"
- "entire"
- "full"
complexity_indicators:
- "complex"
- "multiple"
- "several"
- "many"
- "various"
- "different"
- "multiple.*tasks"
- "complex.*system"
- "build.*comprehensive"
- "coordinate.*work"
- "large-scale.*operation"
- "manage.*operations"
- "comprehensive.*refactoring"
- "authentication.*system"
confidence_threshold: 0.7
auto_activate: true
auto_activation_thresholds:
file_count: 3
directory_count: 2
complexity_score: 0.4
operation_types: 2
delegation_strategies:
files: "individual_file_analysis"
folders: "directory_level_analysis"
auto: "intelligent_auto_detection"
wave_orchestration:
enabled: true
strategies: ["progressive", "systematic", "adaptive", "enterprise"]
behavioral_settings:
coordination_mode: "intelligent"
parallel_optimization: true
learning_integration: true
analytics_tracking: true
token_efficiency:
description: "Intelligent token optimization with adaptive compression"
activation_type: "automatic"
confidence_threshold: 0.75
trigger_patterns:
resource_constraints:
- "context usage >75%"
- "large-scale operations"
- "resource constraints"
- "memory pressure"
user_requests:
- "brief"
- "concise"
- "compressed"
- "short"
- "efficient"
- "minimal"
efficiency_needs:
- "token optimization"
- "resource optimization"
- "efficiency"
- "performance"
compression_levels:
minimal: "0-40%"
efficient: "40-70%"
compressed: "70-85%"
critical: "85-95%"
emergency: "95%+"
behavioral_settings:
symbol_systems: true
abbreviation_systems: true
selective_compression: true
quality_preservation: 0.95
introspection:
description: "Meta-cognitive analysis and framework troubleshooting"
activation_type: "automatic"
confidence_threshold: 0.6
trigger_patterns:
self_analysis:
- "analyze reasoning"
- "examine decision"
- "reflect on"
- "thinking process"
- "decision logic"
problem_solving:
- "complex problem"
- "multi-step"
- "meta-cognitive"
- "systematic thinking"
error_recovery:
- "outcomes don't match"
- "errors occur"
- "unexpected results"
- "troubleshoot"
framework_discussion:
- "SuperClaude"
- "framework"
- "meta-conversation"
- "system analysis"
behavioral_settings:
analysis_depth: "meta_cognitive"
transparency_level: "high"
pattern_recognition: "continuous"
learning_integration: "active"
# Mode Coordination Patterns
mode_coordination:
concurrent_modes:
allowed_combinations:
- ["brainstorming", "token_efficiency"]
- ["task_management", "token_efficiency"]
- ["introspection", "token_efficiency"]
- ["task_management", "introspection"]
coordination_strategies:
brainstorming_efficiency: "compress_non_dialogue_content"
task_management_efficiency: "compress_session_metadata"
introspection_efficiency: "selective_analysis_compression"
mode_transitions:
brainstorming_to_task_management:
trigger: "requirements_clarified"
handoff_data: ["brief", "requirements", "constraints"]
task_management_to_introspection:
trigger: "complex_issues_encountered"
handoff_data: ["task_context", "performance_metrics", "issues"]
any_to_token_efficiency:
trigger: "resource_pressure"
activation_priority: "immediate"
# Performance Profiles
performance_profiles:
lightweight:
target_response_time_ms: 100
memory_usage_mb: 25
cpu_utilization_percent: 20
token_optimization: "standard"
standard:
target_response_time_ms: 200
memory_usage_mb: 50
cpu_utilization_percent: 40
token_optimization: "balanced"
intensive:
target_response_time_ms: 500
memory_usage_mb: 100
cpu_utilization_percent: 70
token_optimization: "aggressive"
# Mode-Specific Configurations
mode_configurations:
brainstorming:
dialogue:
max_rounds: 15
convergence_threshold: 0.85
context_preservation: "full"
brief_generation:
min_requirements: 3
include_context: true
validation_criteria: ["clarity", "completeness", "actionability"]
integration:
auto_handoff: true
prd_agent: "brainstorm-PRD"
command_coordination: "/sc:brainstorm"
task_management:
delegation:
default_strategy: "auto"
concurrency_limit: 7
performance_monitoring: true
wave_orchestration:
auto_activation: true
complexity_threshold: 0.4
coordination_strategy: "adaptive"
analytics:
real_time_tracking: true
performance_metrics: true
optimization_suggestions: true
token_efficiency:
compression:
adaptive_levels: true
quality_thresholds: [0.98, 0.95, 0.90, 0.85, 0.80]
symbol_systems: true
abbreviation_systems: true
selective_compression:
framework_exclusion: true
user_content_preservation: true
session_data_optimization: true
performance:
processing_target_ms: 150
efficiency_target: 0.50
quality_preservation: 0.95
introspection:
analysis:
reasoning_depth: "comprehensive"
pattern_detection: "continuous"
bias_recognition: "active"
transparency:
thinking_process_exposure: true
decision_logic_analysis: true
assumption_validation: true
learning:
pattern_recognition: "continuous"
effectiveness_tracking: true
adaptation_suggestions: true
# Learning Integration
learning_integration:
mode_effectiveness_tracking:
enabled: true
metrics:
- "activation_accuracy"
- "user_satisfaction"
- "task_completion_rates"
- "performance_improvements"
adaptation_triggers:
effectiveness_threshold: 0.7
user_preference_weight: 0.8
performance_impact_weight: 0.6
pattern_learning:
user_specific: true
project_specific: true
context_aware: true
cross_session: true
# Quality Gates
quality_gates:
mode_activation:
pattern_confidence: 0.6
context_appropriateness: 0.7
performance_readiness: true
mode_coordination:
conflict_resolution: "automatic"
resource_allocation: "intelligent"
performance_monitoring: "continuous"
mode_effectiveness:
real_time_monitoring: true
adaptation_triggers: true
quality_preservation: true
# Error Handling
error_handling:
mode_activation_failures:
fallback_strategy: "graceful_degradation"
retry_mechanism: "adaptive"
error_learning: true
coordination_conflicts:
resolution_strategy: "priority_based"
resource_arbitration: "intelligent"
performance_preservation: true
performance_degradation:
detection: "real_time"
mitigation: "automatic"
learning_integration: true
# Integration Points
integration_points:
commands:
brainstorming: "/sc:brainstorm"
task_management: ["/task", "/spawn", "/loop"]
reflection: "/sc:reflect"
mcp_servers:
brainstorming: ["sequential", "context7"]
task_management: ["serena", "morphllm"]
token_efficiency: ["morphllm"]
introspection: ["sequential"]
hooks:
session_start: "mode_initialization"
pre_tool_use: "mode_coordination"
post_tool_use: "mode_effectiveness_tracking"
stop: "mode_analytics_consolidation"
trigger_patterns:
- "brief"
- "concise"
- "compressed"
- "efficient.*output"
- "token.*optimization"
- "short.*response"
- "running.*low.*context"
confidence_threshold: 0.75
auto_activate: true
introspection:
enabled: true
trigger_patterns:
- "analyze.*reasoning"
- "examine.*decision"
- "reflect.*on"
- "meta.*cognitive"
- "thinking.*process"
- "reasoning.*process"
- "decision.*made"
confidence_threshold: 0.6
auto_activate: true

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# SuperClaude-Lite Orchestrator Configuration
# MCP routing patterns and intelligent coordination strategies
# MCP Server Routing Patterns
# Orchestrator routing patterns
routing_patterns:
ui_components:
triggers: ["component", "button", "form", "modal", "dialog", "card", "input", "design", "frontend", "ui", "interface"]
mcp_server: "magic"
persona: "frontend-specialist"
confidence_threshold: 0.8
priority: "high"
performance_profile: "standard"
capabilities: ["ui_generation", "design_systems", "component_patterns"]
deep_analysis:
triggers: ["analyze", "complex", "system-wide", "architecture", "debug", "troubleshoot", "investigate", "root cause"]
mcp_server: "sequential"
thinking_mode: "--think-hard"
confidence_threshold: 0.75
priority: "high"
performance_profile: "intensive"
capabilities: ["complex_reasoning", "systematic_analysis", "hypothesis_testing"]
context_expansion: true
library_documentation:
triggers: ["library", "framework", "package", "import", "dependency", "documentation", "docs", "api", "reference"]
mcp_server: "context7"
persona: "architect"
confidence_threshold: 0.85
context7:
triggers:
- "library.*documentation"
- "framework.*patterns"
- "react|vue|angular"
- "official.*way"
- "React Query"
- "integrate.*library"
capabilities: ["documentation", "patterns", "integration"]
priority: "medium"
performance_profile: "standard"
capabilities: ["documentation_access", "framework_patterns", "best_practices"]
testing_automation:
triggers: ["test", "testing", "e2e", "end-to-end", "browser", "automation", "validation", "verify"]
mcp_server: "playwright"
confidence_threshold: 0.8
priority: "medium"
performance_profile: "intensive"
capabilities: ["browser_automation", "testing_frameworks", "performance_testing"]
intelligent_editing:
triggers: ["edit", "modify", "refactor", "update", "change", "fix", "improve"]
mcp_server: "morphllm"
confidence_threshold: 0.7
priority: "medium"
performance_profile: "lightweight"
capabilities: ["pattern_application", "fast_apply", "intelligent_editing"]
complexity_threshold: 0.6
file_count_threshold: 10
semantic_analysis:
triggers: ["semantic", "symbol", "reference", "find", "search", "navigate", "explore"]
mcp_server: "serena"
confidence_threshold: 0.8
sequential:
triggers:
- "analyze.*complex"
- "debug.*systematic"
- "troubleshoot.*bottleneck"
- "investigate.*root"
- "debug.*why"
- "detailed.*analysis"
- "running.*slowly"
- "performance.*bottleneck"
- "bundle.*size"
capabilities: ["analysis", "debugging", "systematic"]
priority: "high"
performance_profile: "standard"
capabilities: ["semantic_understanding", "project_context", "memory_management"]
multi_file_operations:
triggers: ["multiple files", "batch", "bulk", "project-wide", "codebase", "entire"]
mcp_server: "serena"
confidence_threshold: 0.9
magic:
triggers:
- "component.*ui"
- "responsive.*modal"
- "navigation.*component"
- "mobile.*friendly"
- "responsive.*dashboard"
- "charts.*real-time"
- "build.*dashboard"
capabilities: ["ui", "components", "responsive"]
priority: "medium"
playwright:
triggers:
- "test.*workflow"
- "browser.*automation"
- "cross-browser.*testing"
- "performance.*testing"
- "end-to-end.*tests"
- "checkout.*flow"
- "e2e.*tests"
capabilities: ["testing", "automation", "e2e"]
priority: "medium"
morphllm:
triggers:
- "edit.*file"
- "simple.*modification"
- "quick.*change"
capabilities: ["editing", "modification"]
priority: "low"
serena:
triggers:
- "refactor.*codebase"
- "complex.*analysis"
- "multi.*file"
- "refactor.*entire"
- "new.*API.*patterns"
capabilities: ["refactoring", "semantic", "large-scale"]
priority: "high"
performance_profile: "intensive"
capabilities: ["multi_file_coordination", "project_analysis"]
# Hybrid Intelligence Selection
hybrid_intelligence:
morphllm_vs_serena:
decision_factors:
- file_count
- complexity_score
- operation_type
- symbol_operations_required
- project_size
morphllm_criteria:
file_count_max: 10
complexity_max: 0.6
preferred_operations: ["edit", "modify", "update", "pattern_application"]
optimization_focus: "token_efficiency"
serena_criteria:
file_count_min: 5
complexity_min: 0.4
preferred_operations: ["analyze", "refactor", "navigate", "symbol_operations"]
optimization_focus: "semantic_understanding"
fallback_strategy:
- try_primary_choice
- fallback_to_alternative
- use_native_tools
# Auto-Activation Rules
# Auto-activation thresholds
auto_activation:
complexity_thresholds:
enable_sequential:
complexity_score: 0.6
file_count: 5
operation_types: ["analyze", "debug", "complex"]
enable_delegation:
file_count: 3
directory_count: 2
complexity_score: 0.4
enable_sequential:
complexity_score: 0.6
enable_validation:
is_production: true
risk_level: ["high", "critical"]
operation_types: ["deploy", "refactor", "delete"]
# Performance Optimization
# Hybrid intelligence selection
hybrid_intelligence:
morphllm_vs_serena:
morphllm_criteria:
file_count_max: 10
complexity_max: 0.6
preferred_operations: ["edit", "modify", "simple_refactor"]
serena_criteria:
file_count_min: 5
complexity_min: 0.4
preferred_operations: ["refactor", "analyze", "extract", "move"]
# Performance optimization
performance_optimization:
parallel_execution:
file_threshold: 3
estimated_speedup_min: 1.4
max_concurrency: 7
caching_strategy:
enable_for_operations: ["documentation_lookup", "analysis_results", "pattern_matching"]
cache_duration_minutes: 30
max_cache_size_mb: 100
resource_management:
memory_threshold_percent: 85
token_threshold_percent: 75
fallback_to_lightweight: true
# Quality Gates Integration
quality_gates:
validation_levels:
basic: ["syntax_validation"]
standard: ["syntax_validation", "type_analysis", "code_quality"]
comprehensive: ["syntax_validation", "type_analysis", "code_quality", "security_assessment", "performance_analysis"]
production: ["syntax_validation", "type_analysis", "code_quality", "security_assessment", "performance_analysis", "integration_testing", "deployment_validation"]
trigger_conditions:
comprehensive:
- is_production: true
- complexity_score: ">0.7"
- operation_types: ["refactor", "architecture"]
production:
- is_production: true
- operation_types: ["deploy", "release"]
# Fallback Strategies
fallback_strategies:
mcp_server_unavailable:
context7: ["web_search", "cached_documentation", "native_analysis"]
sequential: ["native_step_by_step", "basic_analysis"]
magic: ["manual_component_generation", "template_suggestions"]
playwright: ["manual_testing_suggestions", "test_case_generation"]
morphllm: ["native_edit_tools", "manual_editing"]
serena: ["basic_file_operations", "simple_search"]
performance_degradation:
high_latency: ["reduce_analysis_depth", "enable_caching", "parallel_processing"]
resource_constraints: ["lightweight_alternatives", "compression_mode", "minimal_features"]
quality_issues:
validation_failures: ["increase_validation_depth", "manual_review", "rollback_capability"]
error_rates_high: ["enable_pre_validation", "reduce_complexity", "step_by_step_execution"]
# Learning Integration
learning_integration:
effectiveness_tracking:
track_server_performance: true
track_routing_decisions: true
track_user_satisfaction: true
adaptation_triggers:
effectiveness_threshold: 0.6
confidence_threshold: 0.7
usage_count_min: 3
optimization_feedback:
performance_degradation: "adjust_routing_weights"
user_preference_detected: "update_server_priorities"
error_patterns_found: "enhance_fallback_strategies"
# Mode Integration
mode_integration:
brainstorming:
preferred_servers: ["sequential", "context7"]
thinking_modes: ["--think", "--think-hard"]
task_management:
coordination_servers: ["serena", "morphllm"]
delegation_strategies: ["files", "folders", "auto"]
token_efficiency:
optimization_servers: ["morphllm"]
compression_strategies: ["symbol_systems", "abbreviations"]
token_threshold_percent: 75

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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

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# User Experience Intelligence Configuration
# UX optimization, project detection, and user-centric intelligence patterns
# Enables intelligent user experience through smart defaults and proactive assistance
# Metadata
version: "1.0.0"
last_updated: "2025-01-06"
description: "User experience optimization and project intelligence patterns"
# Project Type Detection
project_detection:
detection_patterns:
# Detect project types based on files and structure
frontend_frameworks:
react_project:
file_indicators:
- "package.json"
- "*.tsx"
- "*.jsx"
- "react" # in package.json dependencies
directory_indicators:
- "src/components"
- "public"
- "node_modules"
confidence_threshold: 0.8
recommendations:
mcp_servers: ["magic", "context7", "playwright"]
compression_level: "minimal"
performance_focus: "ui_responsiveness"
vue_project:
file_indicators:
- "package.json"
- "*.vue"
- "vue.config.js"
- "vue" # in dependencies
directory_indicators:
- "src/components"
- "src/views"
confidence_threshold: 0.8
recommendations:
mcp_servers: ["magic", "context7"]
compression_level: "standard"
angular_project:
file_indicators:
- "angular.json"
- "*.component.ts"
- "@angular" # in dependencies
directory_indicators:
- "src/app"
- "e2e"
confidence_threshold: 0.9
recommendations:
mcp_servers: ["magic", "context7", "playwright"]
backend_frameworks:
python_project:
file_indicators:
- "requirements.txt"
- "pyproject.toml"
- "setup.py"
- "*.py"
directory_indicators:
- "src"
- "tests"
- "__pycache__"
confidence_threshold: 0.7
recommendations:
mcp_servers: ["serena", "sequential", "context7"]
compression_level: "standard"
validation_level: "enhanced"
node_backend:
file_indicators:
- "package.json"
- "*.js"
- "express" # in dependencies
- "server.js"
directory_indicators:
- "routes"
- "controllers"
- "middleware"
confidence_threshold: 0.8
recommendations:
mcp_servers: ["sequential", "context7", "serena"]
data_science:
jupyter_project:
file_indicators:
- "*.ipynb"
- "requirements.txt"
- "conda.yml"
directory_indicators:
- "notebooks"
- "data"
confidence_threshold: 0.9
recommendations:
mcp_servers: ["sequential", "context7"]
compression_level: "minimal"
analysis_depth: "comprehensive"
documentation:
docs_project:
file_indicators:
- "*.md"
- "docs/"
- "README.md"
- "mkdocs.yml"
directory_indicators:
- "docs"
- "documentation"
confidence_threshold: 0.8
recommendations:
mcp_servers: ["context7", "sequential"]
focus_areas: ["clarity", "completeness"]
# User Preference Intelligence
user_preferences:
preference_learning:
# Learn user preferences from behavior patterns
interaction_patterns:
command_preferences:
track_command_usage: true
track_flag_preferences: true
track_workflow_patterns: true
learning_window: 100 # operations
performance_preferences:
speed_vs_quality_preference:
indicators: ["timeout_tolerance", "quality_acceptance", "performance_complaints"]
classification: ["speed_focused", "quality_focused", "balanced"]
detail_level_preference:
indicators: ["verbose_mode_usage", "summary_requests", "detail_requests"]
classification: ["concise", "detailed", "adaptive"]
preference_adaptation:
# Adapt behavior based on learned preferences
adaptation_strategies:
speed_focused_user:
optimizations: ["aggressive_caching", "parallel_execution", "reduced_analysis"]
ui_changes: ["shorter_responses", "quick_suggestions", "minimal_explanations"]
quality_focused_user:
optimizations: ["comprehensive_analysis", "detailed_validation", "thorough_documentation"]
ui_changes: ["detailed_responses", "comprehensive_suggestions", "full_explanations"]
efficiency_focused_user:
optimizations: ["smart_defaults", "workflow_automation", "predictive_suggestions"]
ui_changes: ["proactive_help", "automated_optimizations", "efficiency_tips"]
# Proactive User Assistance
proactive_assistance:
intelligent_suggestions:
# Provide intelligent suggestions based on context
optimization_suggestions:
- trigger: {repeated_operations: ">5", same_pattern: true}
suggestion: "Consider creating a script or alias for this repeated operation"
confidence: 0.8
category: "workflow_optimization"
- trigger: {performance_issues: "detected", duration: ">3_sessions"}
suggestion: "Performance optimization recommendations available"
action: "show_performance_guide"
confidence: 0.9
category: "performance"
- trigger: {error_pattern: "recurring", count: ">3"}
suggestion: "Automated error recovery pattern available"
action: "enable_auto_recovery"
confidence: 0.85
category: "error_prevention"
contextual_help:
# Provide contextual help and guidance
help_triggers:
- context: {new_user: true, session_count: "<5"}
help_type: "onboarding_guidance"
content: "Getting started tips and best practices"
- context: {error_rate: ">10%", recent_errors: ">3"}
help_type: "troubleshooting_assistance"
content: "Common error solutions and debugging tips"
- context: {complex_operation: true, user_expertise: "beginner"}
help_type: "step_by_step_guidance"
content: "Detailed guidance for complex operations"
# Smart Defaults Intelligence
smart_defaults:
context_aware_defaults:
# Generate smart defaults based on context
project_based_defaults:
react_project:
default_mcp_servers: ["magic", "context7"]
default_compression: "minimal"
default_analysis_depth: "ui_focused"
default_validation: "component_focused"
python_project:
default_mcp_servers: ["serena", "sequential"]
default_compression: "standard"
default_analysis_depth: "comprehensive"
default_validation: "enhanced"
documentation_project:
default_mcp_servers: ["context7"]
default_compression: "minimal"
default_analysis_depth: "content_focused"
default_validation: "readability_focused"
dynamic_configuration:
# Dynamically adjust configuration
configuration_adaptation:
performance_based:
triggers:
- condition: {system_performance: "high"}
adjustments: {analysis_depth: "comprehensive", features: "all_enabled"}
- condition: {system_performance: "low"}
adjustments: {analysis_depth: "essential", features: "performance_focused"}
user_expertise_based:
triggers:
- condition: {user_expertise: "expert"}
adjustments: {verbosity: "minimal", automation: "high", warnings: "reduced"}
- condition: {user_expertise: "beginner"}
adjustments: {verbosity: "detailed", automation: "guided", warnings: "comprehensive"}
# Error Recovery Intelligence
error_recovery:
intelligent_error_handling:
# Smart error handling and recovery
error_classification:
user_errors:
- type: "syntax_error"
recovery: "suggest_correction"
user_guidance: "detailed"
- type: "configuration_error"
recovery: "auto_fix_with_approval"
user_guidance: "educational"
- type: "workflow_error"
recovery: "suggest_alternative_approach"
user_guidance: "workflow_tips"
system_errors:
- type: "performance_degradation"
recovery: "automatic_optimization"
user_notification: "informational"
- type: "resource_exhaustion"
recovery: "resource_management_mode"
user_notification: "status_update"
- type: "service_unavailable"
recovery: "graceful_fallback"
user_notification: "service_status"
recovery_learning:
# Learn from error recovery patterns
recovery_effectiveness:
track_recovery_success: true
learn_recovery_patterns: true
improve_recovery_strategies: true
user_recovery_preferences:
learn_preferred_recovery: true
adapt_recovery_approach: true
personalize_error_handling: true
# User Expertise Detection
expertise_detection:
expertise_indicators:
# Detect user expertise level
behavioral_indicators:
command_proficiency:
indicators: ["advanced_flags", "complex_operations", "custom_configurations"]
weight: 0.4
error_recovery_ability:
indicators: ["self_correction", "minimal_help_needed", "independent_problem_solving"]
weight: 0.3
workflow_sophistication:
indicators: ["efficient_workflows", "automation_usage", "advanced_patterns"]
weight: 0.3
expertise_adaptation:
# Adapt interface based on expertise
beginner_adaptations:
interface: ["detailed_explanations", "step_by_step_guidance", "comprehensive_warnings"]
defaults: ["safe_options", "guided_workflows", "educational_mode"]
intermediate_adaptations:
interface: ["balanced_explanations", "contextual_help", "smart_suggestions"]
defaults: ["optimized_workflows", "intelligent_automation", "performance_focused"]
expert_adaptations:
interface: ["minimal_explanations", "advanced_options", "efficiency_focused"]
defaults: ["maximum_automation", "performance_optimization", "minimal_interruptions"]
# User Satisfaction Intelligence
satisfaction_intelligence:
satisfaction_tracking:
# Track user satisfaction indicators
satisfaction_metrics:
task_completion_rate:
weight: 0.3
target_threshold: 0.85
error_resolution_speed:
weight: 0.25
target_threshold: "fast"
feature_adoption_rate:
weight: 0.2
target_threshold: 0.6
user_feedback_sentiment:
weight: 0.25
target_threshold: "positive"
satisfaction_optimization:
# Optimize for user satisfaction
optimization_strategies:
low_satisfaction_triggers:
- trigger: {completion_rate: "<0.7"}
action: "simplify_workflows"
priority: "high"
- trigger: {error_rate: ">15%"}
action: "improve_error_prevention"
priority: "critical"
- trigger: {feature_adoption: "<0.3"}
action: "improve_feature_discoverability"
priority: "medium"
# Personalization Engine
personalization:
adaptive_interface:
# Personalize interface based on user patterns
interface_personalization:
layout_preferences:
learn_preferred_layouts: true
adapt_information_density: true
customize_interaction_patterns: true
content_personalization:
learn_content_preferences: true
adapt_explanation_depth: true
customize_suggestion_types: true
workflow_optimization:
# Optimize workflows for individual users
personal_workflow_learning:
common_task_patterns: true
workflow_efficiency_analysis: true
personalized_shortcuts: true
workflow_recommendations:
suggest_workflow_improvements: true
recommend_automation_opportunities: true
provide_efficiency_insights: true
# Accessibility Intelligence
accessibility:
adaptive_accessibility:
# Adapt interface for accessibility needs
accessibility_detection:
detect_accessibility_needs: true
learn_accessibility_preferences: true
adapt_interface_accordingly: true
inclusive_design:
# Ensure inclusive user experience
inclusive_features:
multiple_interaction_modes: true
flexible_interface_scaling: true
comprehensive_keyboard_support: true
screen_reader_optimization: true

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# 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