SuperClaude/Framework-Hooks/docs/Hooks/stop.md

Stop Hook Documentation

Overview

The Stop Hook is a comprehensive session analytics and persistence engine that runs at the end of each Claude Code session. It implements the /sc:save logic with advanced performance tracking, providing detailed analytics about session effectiveness, learning consolidation, and intelligent session data storage.

Purpose

The Stop Hook serves as the primary session analytics and persistence system for SuperClaude Framework, delivering:

• Session Analytics: Comprehensive performance and effectiveness metrics
• Learning Consolidation: Consolidation of learning events from the entire session
• Session Persistence: Intelligent session data storage with compression
• Performance Optimization: Recommendations for future sessions based on analytics
• Quality Assessment: Session success evaluation and improvement suggestions
• Framework Effectiveness: Measurement of SuperClaude framework impact

Execution Context

When This Hook Runs

• Trigger: Session termination in Claude Code
• Context: End of user session, before final cleanup
• Data Available: Complete session history, operations log, error records
• Timing: After all user operations completed, before session cleanup

Hook Integration Points

• Session Lifecycle: Final stage of session processing
• MCP Intelligence: Coordinates with MCP servers for enhanced analytics
• Learning Engine: Consolidates learning events and adaptations
• Framework Logic: Applies SuperClaude framework patterns for analysis

Performance Target

Primary Target: <200ms execution time for complete session analytics

Performance Benchmarks

• Initialization: <50ms for component loading
• Analytics Generation: <100ms for comprehensive analysis
• Session Persistence: <30ms for data storage
• Learning Consolidation: <20ms for learning events processing
• Total Processing: <200ms end-to-end execution

Performance Monitoring

execution_time = (time.time() - start_time) * 1000
target_met = execution_time < self.performance_target_ms

Session Analytics

Comprehensive Performance Metrics

Overall Score Calculation

overall_score = (
    productivity * 0.4 + 
    effectiveness * 0.4 + 
    (1.0 - error_rate) * 0.2
)

Performance Categories

• Productivity Score: Operations per minute, completion rates
• Quality Score: Error rates, operation success rates
• Intelligence Utilization: MCP server usage, SuperClaude effectiveness
• Resource Efficiency: Memory, CPU, token usage optimization
• User Satisfaction Estimate: Derived from session patterns and outcomes

Analytics Components

performance_metrics:
  overall_score: 0.85           # Combined performance indicator
  productivity_score: 0.78      # Operations efficiency
  quality_score: 0.92          # Error-free execution rate
  efficiency_score: 0.84       # Resource utilization
  satisfaction_estimate: 0.87   # Estimated user satisfaction

Bottleneck Identification

• High Error Rate: >20% operation failure rate
• Low Productivity: <50% productivity score
• Underutilized Intelligence: <30% MCP usage with SuperClaude enabled
• Resource Constraints: Memory/CPU/token usage optimization opportunities

Optimization Opportunities Detection

• Tool Usage Optimization: >10 unique tools suggest coordination improvement
• MCP Server Coordination: <2 servers with >5 operations suggest better orchestration
• Workflow Enhancement: Pattern analysis for efficiency improvements

Learning Consolidation

Learning Events Processing

The hook consolidates all learning events generated during the session:

def _consolidate_learning_events(self, context: dict) -> dict:
    # Generate learning insights from session
    insights = self.learning_engine.generate_learning_insights()
    
    # Session-specific learning metrics
    session_learning = {
        'session_effectiveness': context.get('superclaude_effectiveness', 0),
        'performance_score': context.get('session_productivity', 0),
        'mcp_coordination_effectiveness': min(context.get('mcp_usage_ratio', 0) * 2, 1.0),
        'error_recovery_success': 1.0 - context.get('error_rate', 0)
    }

Learning Categories

• Effectiveness Feedback: Session performance patterns
• User Preferences: Interaction and usage patterns
• Technical Patterns: Tool usage and coordination effectiveness
• Error Recovery: Success patterns for error handling

Adaptation Creation

• Session-Level Adaptations: Immediate session pattern learning
• User-Level Adaptations: Long-term preference learning
• Technical Adaptations: Tool and workflow optimization patterns

Session Persistence

Intelligent Storage Strategy

Data Classification

• Session Analytics: Complete performance and effectiveness data
• Learning Events: Consolidated learning insights and adaptations
• Context Data: Session operational context and metadata
• Recommendations: Generated suggestions for future sessions

Compression Logic

# Apply compression for large session data
if len(analytics_data) > 10000:  # 10KB threshold
    compression_result = self.compression_engine.compress_content(
        analytics_data,
        context,
        {'content_type': 'session_data'}
    )

Storage Optimization

• Session Cleanup: Maintains 50 most recent sessions
• Automatic Pruning: Removes sessions older than retention policy
• Compression: Applied to sessions >10KB for storage efficiency

Persistence Results

persistence_result:
  persistence_enabled: true
  session_data_saved: true
  analytics_saved: true
  learning_data_saved: true
  compression_applied: true
  compression_ratio: 0.65
  storage_optimized: true

Recommendations Generation

Performance Improvements

Generated when overall score <70%:

• Focus on reducing error rate through validation
• Enable more SuperClaude intelligence features
• Optimize tool selection and usage patterns

SuperClaude Optimizations

Based on framework effectiveness analysis:

• Low Effectiveness (<60%): Enable more MCP servers, use delegation features
• Disabled Framework: Recommend SuperClaude enablement for productivity
• Underutilization: Activate compression and intelligence features

Learning Suggestions

• Low Learning Events (<3): Engage with more complex operations
• Pattern Recognition: Suggestions based on successful session patterns
• Workflow Enhancement: Recommendations for process improvements

Workflow Enhancements

Based on error patterns and efficiency analysis:

• High Error Rate (>10%): Use validation hooks, enable pre-tool intelligence
• Resource Optimization: Memory, CPU, token usage improvements
• Coordination Enhancement: Better MCP server and tool coordination

Configuration

Hook Configuration

Loaded from superclaude-config.json hook configuration:

stop_hook:
  performance_target_ms: 200
  analytics:
    comprehensive_metrics: true
    learning_consolidation: true
    recommendation_generation: true
  persistence:
    enabled: true
    compression_threshold_bytes: 10000
    session_retention_count: 50
  learning:
    session_adaptations: true
    user_preference_tracking: true
    technical_pattern_learning: true

Session Configuration

Falls back to session.yaml configuration when available:

session:
  analytics_enabled: true
  learning_consolidation: true
  performance_tracking: true
  recommendation_generation: true
  persistence_optimization: true

Integration with /sc:save

Command Implementation

The Stop Hook directly implements the /sc:save command logic:

Core /sc:save Features

• Session Analytics: Complete session performance analysis
• Learning Consolidation: All learning events processed and stored
• Intelligent Persistence: Session data saved with optimization
• Recommendation Generation: Actionable suggestions for improvement
• Performance Tracking: <200ms execution time monitoring

/sc:save Workflow Integration

def process_session_stop(self, session_data: dict) -> dict:
    # 1. Extract session context
    context = self._extract_session_context(session_data)
    
    # 2. Analyze session performance (/sc:save analytics)
    performance_analysis = self._analyze_session_performance(context)
    
    # 3. Consolidate learning events (/sc:save learning)
    learning_consolidation = self._consolidate_learning_events(context)
    
    # 4. Generate session analytics (/sc:save metrics)
    session_analytics = self._generate_session_analytics(...)
    
    # 5. Perform session persistence (/sc:save storage)
    persistence_result = self._perform_session_persistence(...)
    
    # 6. Generate recommendations (/sc:save recommendations)
    recommendations = self._generate_recommendations(...)

/sc:save Output Format

session_report:
  session_id: "session_2025-01-31_14-30-00"
  session_completed: true
  completion_timestamp: 1704110400
  
  analytics:
    session_summary: {...}
    performance_metrics: {...}
    superclaude_effectiveness: {...}
    quality_analysis: {...}
    learning_summary: {...}
  
  persistence:
    persistence_enabled: true
    analytics_saved: true
    compression_applied: true
  
  recommendations:
    performance_improvements: [...]
    superclaude_optimizations: [...]
    learning_suggestions: [...]
    workflow_enhancements: [...]

Quality Assessment

Session Success Criteria

A session is considered successful when:

• Overall Score: >60% performance score
• SuperClaude Effectiveness: >60% when framework enabled
• Learning Achievement: >0 insights generated
• Recommendations: Actionable suggestions provided

Quality Metrics

quality_analysis:
  error_rate: 0.05                    # 5% error rate
  operation_success_rate: 0.95        # 95% success rate
  bottlenecks: ["low_productivity"]   # Identified issues
  optimization_opportunities: [...]    # Improvement areas

Success Indicators

• Session Success: overall_score > 0.6
• SuperClaude Effective: effectiveness_score > 0.6
• Learning Achieved: insights_generated > 0
• Recommendations Generated: total_recommendations > 0

User Satisfaction Estimation

def _estimate_user_satisfaction(self, context: dict) -> float:
    satisfaction_factors = []
    
    # Error rate impact
    satisfaction_factors.append(1.0 - error_rate)
    
    # Productivity impact
    satisfaction_factors.append(productivity)
    
    # SuperClaude effectiveness impact
    if superclaude_enabled:
        satisfaction_factors.append(effectiveness)
    
    # Session duration optimization (15-60 minutes optimal)
    satisfaction_factors.append(duration_satisfaction)
    
    return statistics.mean(satisfaction_factors)

Error Handling

Graceful Degradation

When errors occur during hook execution:

except Exception as e:
    log_error("stop", str(e), {"session_data": session_data})
    return self._create_fallback_report(session_data, str(e))

Fallback Reporting

fallback_report:
  session_completed: false
  error: "Analysis engine failure"
  fallback_mode: true
  analytics:
    performance_metrics:
      overall_score: 0.0
  persistence:
    persistence_enabled: false

Recovery Strategies

• Analytics Failure: Provide basic session summary
• Persistence Failure: Continue with recommendations generation
• Learning Engine Error: Skip learning consolidation, continue with core analytics
• Complete Failure: Return minimal session completion report

Performance Optimization

Efficiency Strategies

• Lazy Loading: Components initialized only when needed
• Batch Processing: Multiple analytics operations combined
• Compression: Large session data automatically compressed
• Caching: Learning insights cached for reuse

Resource Management

• Memory Optimization: Session cleanup after processing
• Storage Efficiency: Old sessions automatically pruned
• Processing Time: <200ms target with continuous monitoring
• Token Efficiency: Compressed analytics data when appropriate

Future Enhancements

Planned Features

• Cross-Session Analytics: Performance trends across multiple sessions
• Predictive Recommendations: ML-based optimization suggestions
• Real-Time Monitoring: Live session analytics during execution
• Collaborative Learning: Shared learning patterns across users
• Advanced Compression: Context-aware compression algorithms

Integration Opportunities

• Dashboard Integration: Real-time analytics visualization
• Notification System: Alerts for performance degradation
• API Endpoints: Session analytics via REST API
• Export Capabilities: Analytics data export for external analysis

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The Stop Hook represents the culmination of session management in SuperClaude Framework, providing comprehensive analytics, learning consolidation, and intelligent persistence to enable continuous improvement and optimization of user productivity.