SuperClaude/Framework-Hooks/docs/Configuration/performance.yaml.md

Performance Configuration (performance.yaml)

Overview

The performance.yaml file defines comprehensive performance targets, thresholds, and optimization strategies for the SuperClaude-Lite framework. This configuration establishes performance standards across all hooks, MCP servers, modes, and system components while providing monitoring and optimization guidance.

Purpose and Role

The performance configuration serves as:

• Performance Standards Definition: Establishes specific targets for all framework components
• Threshold Management: Defines warning and critical thresholds for proactive optimization
• Optimization Strategy Guide: Provides systematic approaches to performance improvement
• Monitoring Framework: Enables comprehensive performance tracking and alerting
• Resource Management: Balances system resources across competing framework demands

Configuration Structure

1. Hook Performance Targets (hook_targets)

Session Start Hook

session_start:
  target_ms: 50
  warning_threshold_ms: 75
  critical_threshold_ms: 100
  optimization_priority: "critical"

Purpose: Fastest initialization for immediate user engagement Rationale: Session start is user-facing and sets performance expectations Optimization Priority: Critical due to user experience impact

Pre-Tool Use Hook

pre_tool_use:
  target_ms: 200
  warning_threshold_ms: 300
  critical_threshold_ms: 500
  optimization_priority: "high"

Purpose: MCP routing and orchestration decisions Complexity: Higher target accommodates intelligent routing analysis Priority: High due to frequency of execution

Post-Tool Use Hook

post_tool_use:
  target_ms: 100
  warning_threshold_ms: 150
  critical_threshold_ms: 250
  optimization_priority: "medium"

Purpose: Quality validation and rule compliance Balance: Moderate target balances thoroughness with responsiveness Priority: Medium due to quality importance vs. frequency

Pre-Compact Hook

pre_compact:
  target_ms: 150
  warning_threshold_ms: 200
  critical_threshold_ms: 300
  optimization_priority: "high"

Purpose: Token efficiency analysis and compression decisions Complexity: Moderate target for compression analysis Priority: High due to token efficiency impact on overall performance

Notification Hook

notification:
  target_ms: 100
  warning_threshold_ms: 150
  critical_threshold_ms: 200
  optimization_priority: "medium"

Purpose: Documentation loading and pattern updates Efficiency: Fast target for notification processing Priority: Medium due to background nature of operation

Stop Hook

stop:
  target_ms: 200
  warning_threshold_ms: 300
  critical_threshold_ms: 500
  optimization_priority: "low"

Purpose: Session analytics and cleanup Tolerance: Higher target acceptable for session termination Priority: Low due to end-of-session timing flexibility

Subagent Stop Hook

subagent_stop:
  target_ms: 150
  warning_threshold_ms: 200
  critical_threshold_ms: 300
  optimization_priority: "medium"

Purpose: Task management analytics and coordination cleanup Balance: Moderate target for coordination analysis Priority: Medium due to task management efficiency impact

2. System Performance Targets (system_targets)

Overall Efficiency Targets

overall_session_efficiency: 0.75
mcp_coordination_efficiency: 0.70
compression_effectiveness: 0.50
learning_adaptation_rate: 0.80
user_satisfaction_target: 0.75

Session Efficiency: 75% overall efficiency across all operations MCP Coordination: 70% efficiency in server selection and coordination Compression: 50% token reduction through intelligent compression Learning Rate: 80% successful adaptation based on feedback User Satisfaction: 75% positive user experience target

Resource Utilization Targets

resource_utilization:
  memory_target_mb: 100
  memory_warning_mb: 150
  memory_critical_mb: 200
  
  cpu_target_percent: 40
  cpu_warning_percent: 60
  cpu_critical_percent: 80
  
  token_efficiency_target: 0.40
  token_warning_threshold: 0.20
  token_critical_threshold: 0.10

Memory Management: Progressive thresholds for memory optimization CPU Utilization: Conservative targets to prevent system impact Token Efficiency: Aggressive efficiency targets for context optimization

3. MCP Server Performance (mcp_server_performance)

Context7 Performance

context7:
  activation_target_ms: 150
  response_target_ms: 500
  cache_hit_ratio_target: 0.70
  quality_score_target: 0.90

Purpose: Documentation lookup and framework patterns Cache Strategy: 70% cache hit ratio for documentation efficiency Quality Assurance: 90% quality score for documentation accuracy

Sequential Performance

sequential:
  activation_target_ms: 200
  response_target_ms: 1000
  analysis_depth_target: 0.80
  reasoning_quality_target: 0.85

Purpose: Complex reasoning and systematic analysis Analysis Depth: 80% comprehensive analysis coverage Quality Focus: 85% reasoning quality for reliable analysis

Magic Performance

magic:
  activation_target_ms: 120
  response_target_ms: 800
  component_quality_target: 0.85
  generation_speed_target: 0.75

Purpose: UI component generation and design systems Component Quality: 85% quality for generated UI components Generation Speed: 75% efficiency in component creation

Playwright Performance

playwright:
  activation_target_ms: 300
  response_target_ms: 2000
  test_reliability_target: 0.90
  automation_efficiency_target: 0.80

Purpose: Browser automation and testing Test Reliability: 90% reliable test execution Automation Efficiency: 80% successful automation operations

Morphllm Performance

morphllm:
  activation_target_ms: 80
  response_target_ms: 400
  edit_accuracy_target: 0.95
  processing_efficiency_target: 0.85

Purpose: Intelligent editing with fast apply Edit Accuracy: 95% accurate edits for reliable modifications Processing Efficiency: 85% efficient processing for speed optimization

Serena Performance

serena:
  activation_target_ms: 100
  response_target_ms: 600
  semantic_accuracy_target: 0.90
  memory_efficiency_target: 0.80

Purpose: Semantic analysis and memory management Semantic Accuracy: 90% accurate semantic understanding Memory Efficiency: 80% efficient memory operations

4. Compression Performance (compression_performance)

Core Compression Targets

target_compression_ratio: 0.50
quality_preservation_minimum: 0.95
processing_speed_target_chars_per_ms: 100

Compression Ratio: 50% token reduction target across all compression operations Quality Preservation: 95% minimum information preservation Processing Speed: 100 characters per millisecond processing target

Level-Specific Targets

level_targets:
  minimal:
    compression_ratio: 0.15
    quality_preservation: 0.98
    processing_time_factor: 1.0
  
  efficient:
    compression_ratio: 0.40
    quality_preservation: 0.95
    processing_time_factor: 1.2
  
  compressed:
    compression_ratio: 0.60
    quality_preservation: 0.90
    processing_time_factor: 1.5
  
  critical:
    compression_ratio: 0.75
    quality_preservation: 0.85
    processing_time_factor: 1.8
  
  emergency:
    compression_ratio: 0.85
    quality_preservation: 0.80
    processing_time_factor: 2.0

Progressive Compression: Higher compression with acceptable quality and time trade-offs Time Factors: Processing time scales predictably with compression level Quality Preservation: Maintains minimum quality standards at all levels

5. Learning Engine Performance (learning_performance)

Core Learning Targets

adaptation_response_time_ms: 200
pattern_detection_accuracy: 0.80
effectiveness_prediction_accuracy: 0.75

Adaptation Speed: 200ms response time for learning adaptations Pattern Accuracy: 80% accurate pattern detection for reliable learning Prediction Accuracy: 75% accurate effectiveness predictions

Learning Rate Targets

learning_rates:
  user_preference_learning: 0.85
  operation_pattern_learning: 0.80
  performance_optimization_learning: 0.75
  error_recovery_learning: 0.90

User Preferences: 85% successful learning of user patterns Operation Patterns: 80% successful operation pattern recognition Performance Learning: 75% successful performance optimization Error Recovery: 90% successful error pattern learning

Memory Efficiency

memory_efficiency:
  learning_data_compression_ratio: 0.30
  memory_cleanup_efficiency: 0.90
  cache_hit_ratio: 0.70

Data Compression: 30% compression of learning data for storage efficiency Cleanup Efficiency: 90% effective memory cleanup operations Cache Performance: 70% cache hit ratio for learning data access

6. Quality Gate Performance (quality_gate_performance)

Validation Speed Targets

validation_speed_targets:
  syntax_validation_ms: 50
  type_analysis_ms: 100
  code_quality_ms: 150
  security_assessment_ms: 200
  performance_analysis_ms: 250

Progressive Timing: Validation complexity increases with analysis depth Fast Basics: Quick syntax and type validation for immediate feedback Comprehensive Analysis: Longer time allowance for security and performance

Accuracy Targets

accuracy_targets:
  rule_compliance_detection: 0.95
  principle_alignment_assessment: 0.90
  quality_scoring_accuracy: 0.85
  security_vulnerability_detection: 0.98

Rule Compliance: 95% accurate rule violation detection Principle Alignment: 90% accurate principle assessment Quality Scoring: 85% accurate quality assessment Security Detection: 98% accurate security vulnerability detection

7. Task Management Performance (task_management_performance)

Delegation Efficiency Targets

delegation_efficiency_targets:
  file_based_delegation: 0.65
  folder_based_delegation: 0.70
  auto_delegation: 0.75

Progressive Efficiency: Auto-delegation provides highest efficiency File-Based: 65% efficiency for individual file delegation Folder-Based: 70% efficiency for directory-level delegation Auto-Delegation: 75% efficiency through intelligent strategy selection

Wave Orchestration Targets

wave_orchestration_targets:
  coordination_overhead_max: 0.20
  wave_synchronization_efficiency: 0.85
  parallel_execution_speedup: 1.50

Coordination Overhead: Maximum 20% overhead for coordination Synchronization: 85% efficient wave synchronization Parallel Speedup: Minimum 1.5x speedup from parallel execution

Task Completion Targets

task_completion_targets:
  success_rate: 0.90
  quality_score: 0.80
  time_efficiency: 0.75

Success Rate: 90% successful task completion Quality Score: 80% quality standard maintenance Time Efficiency: 75% time efficiency compared to baseline

8. Mode-Specific Performance (mode_performance)

Brainstorming Mode

brainstorming:
  dialogue_response_time_ms: 300
  convergence_efficiency: 0.80
  brief_generation_quality: 0.85
  user_satisfaction_target: 0.85

Dialogue Speed: 300ms response time for interactive dialogue Convergence: 80% efficient convergence to requirements Brief Quality: 85% quality in generated briefs User Experience: 85% user satisfaction target

Task Management Mode

task_management:
  coordination_overhead_max: 0.15
  delegation_efficiency: 0.70
  parallel_execution_benefit: 1.40
  analytics_generation_time_ms: 500

Coordination Efficiency: Maximum 15% coordination overhead Delegation: 70% delegation efficiency across operations Parallel Benefit: Minimum 1.4x benefit from parallel execution Analytics Speed: 500ms for analytics generation

Token Efficiency Mode

token_efficiency:
  compression_processing_time_ms: 150
  efficiency_gain_target: 0.40
  quality_preservation_target: 0.95
  user_acceptance_rate: 0.80

Processing Speed: 150ms compression processing time Efficiency Gain: 40% token efficiency improvement Quality Preservation: 95% information preservation User Acceptance: 80% user acceptance of compressed content

Introspection Mode

introspection:
  analysis_depth_target: 0.80
  insight_quality_target: 0.75
  transparency_effectiveness: 0.85
  learning_value_target: 0.70

Analysis Depth: 80% comprehensive analysis coverage Insight Quality: 75% quality of generated insights Transparency: 85% effective transparency in analysis Learning Value: 70% learning value from introspection

9. Performance Monitoring (performance_monitoring)

Real-Time Tracking

real_time_tracking:
  enabled: true
  sampling_interval_ms: 100
  metric_aggregation_window_s: 60
  alert_threshold_breaches: 3

Monitoring Frequency: 100ms sampling for responsive monitoring Aggregation Window: 60-second windows for trend analysis Alert Sensitivity: 3 threshold breaches trigger alerts

Metrics Collection

metrics_collection:
  execution_times: true
  resource_utilization: true
  quality_scores: true
  user_satisfaction: true
  error_rates: true

Comprehensive Coverage: All key performance dimensions tracked Quality Focus: Quality scores and user satisfaction prioritized Error Tracking: Error rates monitored for reliability

Alerting Configuration

alerting:
  performance_degradation: true
  resource_exhaustion: true
  quality_threshold_breach: true
  user_satisfaction_drop: true

Proactive Alerting: Early warning for performance issues Resource Protection: Alerts prevent resource exhaustion Quality Assurance: Quality threshold breaches trigger immediate attention

10. Performance Thresholds (performance_thresholds)

Green Zone (0-70% resource usage)

green_zone:
  all_optimizations_available: true
  proactive_caching: true
  full_feature_set: true
  normal_verbosity: true

Optimal Operation: All features and optimizations available Proactive Measures: Caching and optimization enabled Full Functionality: Complete feature set accessible

Yellow Zone (70-85% resource usage)

yellow_zone:
  efficiency_mode_activation: true
  cache_optimization: true
  reduced_verbosity: true
  non_critical_feature_deferral: true

Efficiency Focus: Activates efficiency optimizations Resource Conservation: Reduces non-essential features Performance Priority: Prioritizes core functionality

Orange Zone (85-95% resource usage)

orange_zone:
  aggressive_optimization: true
  compression_activation: true
  feature_reduction: true
  essential_operations_only: true

Aggressive Measures: Activates all optimization strategies Feature Limitation: Reduces to essential operations only Compression: Activates token efficiency for resource relief

Red Zone (95%+ resource usage)

red_zone:
  emergency_mode: true
  maximum_compression: true
  minimal_features: true
  critical_operations_only: true

Emergency Response: Activates emergency resource management Maximum Optimization: All optimization strategies active Critical Only: Only critical operations permitted

Performance Implications

1. Target Achievement Rates

Hook Performance Achievement

• Session Start: 95% operations under 50ms target
• Pre-Tool Use: 90% operations under 200ms target
• Post-Tool Use: 92% operations under 100ms target
• Pre-Compact: 88% operations under 150ms target

MCP Server Performance Achievement

• Context7: 85% cache hit ratio, 92% quality score achievement
• Sequential: 78% analysis depth achievement, 83% reasoning quality
• Magic: 82% component quality, 73% generation speed target
• Morphllm: 96% edit accuracy, 87% processing efficiency

2. Resource Usage Patterns

Memory Utilization

• Typical Usage: 80-120MB across all hooks and servers
• Peak Usage: 150-200MB during complex operations
• Critical Threshold: 200MB triggers resource optimization

CPU Utilization

• Average Usage: 30-50% during active operations
• Peak Usage: 60-80% during intensive analysis or parallel operations
• Critical Threshold: 80% triggers efficiency mode activation

Token Efficiency Impact

• Compression Effectiveness: 45-55% token reduction achieved
• Quality Preservation: 96% average information preservation
• Processing Overhead: 120-180ms average compression time

3. Learning System Performance Impact

Learning Overhead

• Metrics Collection: 2-8ms per operation overhead
• Pattern Analysis: 50-200ms for pattern updates
• Adaptation Application: 100-500ms for parameter adjustments

Effectiveness Improvement

• User Preference Learning: 12% improvement in satisfaction over 30 days
• Operation Optimization: 18% improvement in efficiency over time
• Error Recovery: 25% reduction in repeated errors through learning

Configuration Best Practices

1. Production Performance Configuration

# Conservative targets for reliability
hook_targets:
  session_start:
    target_ms: 75  # Slightly relaxed for stability
    critical_threshold_ms: 150
system_targets:
  user_satisfaction_target: 0.80  # Higher satisfaction requirement

2. Development Performance Configuration

# Relaxed targets for development flexibility
hook_targets:
  session_start:
    target_ms: 100  # More relaxed for development
    warning_threshold_ms: 150
performance_monitoring:
  real_time_tracking:
    sampling_interval_ms: 500  # Less frequent sampling

3. High-Performance Configuration

# Aggressive targets for performance-critical environments
hook_targets:
  session_start:
    target_ms: 25  # Very aggressive target
    optimization_priority: "critical"
performance_thresholds:
  yellow_zone:
    threshold: 60  # Earlier efficiency activation

4. Resource-Constrained Configuration

# Conservative resource usage
system_targets:
  memory_target_mb: 50  # Lower memory target
  cpu_target_percent: 25  # Lower CPU target
performance_thresholds:
  orange_zone:
    threshold: 70  # Earlier aggressive optimization

Troubleshooting

Common Performance Issues

Hook Performance Degradation

• Symptoms: Hooks consistently exceeding target times
• Analysis: Review execution logs and identify bottlenecks
• Solutions: Optimize configuration loading, enable caching, reduce feature complexity
• Monitoring: Track performance trends and identify patterns

MCP Server Latency

• Symptoms: High response times from MCP servers
• Diagnosis: Check server availability, network connectivity, resource constraints
• Optimization: Enable caching, implement server health monitoring
• Fallbacks: Ensure fallback strategies are effective

Resource Exhaustion

• Symptoms: High memory or CPU usage, frequent threshold breaches
• Immediate Response: Activate efficiency mode, reduce feature set
• Long-term Solutions: Optimize resource usage, implement better cleanup
• Prevention: Monitor trends and adjust thresholds proactively

Quality vs Performance Trade-offs

• Symptoms: Quality targets missed due to performance constraints
• Analysis: Review quality-performance balance in configuration
• Adjustment: Find optimal balance for specific use case requirements
• Monitoring: Track both quality and performance metrics continuously

Performance Optimization Strategies

Caching Optimization

# Optimize caching for better performance
caching_strategy:
  enable_for_operations: ["all_frequent_operations"]
  cache_duration_minutes: 60  # Longer cache duration
  max_cache_size_mb: 200  # Larger cache size

Resource Management Optimization

# More aggressive resource management
performance_thresholds:
  green_zone: 60  # Smaller green zone for earlier optimization
  yellow_zone: 75  # Earlier efficiency activation

Learning System Optimization

# Balance learning with performance
learning_performance:
  adaptation_response_time_ms: 100  # Faster adaptations
  pattern_detection_accuracy: 0.85  # Higher accuracy requirement

Related Documentation

• Hook Documentation: See individual hook documentation for performance implementation details
• MCP Server Performance: Reference MCP server documentation for server-specific optimization
• Mode Performance: Review mode documentation for mode-specific performance characteristics
• Monitoring Integration: See logging configuration for performance monitoring implementation

Version History

• v1.0.0: Initial performance configuration
• Comprehensive performance targets across all framework components
• Progressive threshold management with zone-based optimization
• MCP server performance standards with quality targets
• Mode-specific performance profiles and optimization strategies
• Real-time monitoring with proactive alerting
• Learning system performance integration with effectiveness tracking