SuperClaude/Framework-Hooks/docs/Integration.md

Framework-Hooks Integration with SuperClaude

Overview

The Framework-Hooks system provides a sophisticated intelligence layer that seamlessly integrates with SuperClaude through lifecycle hooks, enabling pattern-driven AI assistance with sub-50ms performance targets. This integration transforms Claude Code from a reactive tool into an intelligent, adaptive development partner.

1. SuperClaude Framework Integration

Core Integration Architecture

The Framework-Hooks system enhances Claude Code through seven strategic lifecycle hooks that implement SuperClaude's core principles:

┌─────────────────────────────────────────────────────────────┐
│                    Claude Code Runtime                      │
├─────────────────────────────────────────────────────────────┤
│  SessionStart → PreTool → PostTool → PreCompact → Notify   │
│       ↓            ↓         ↓           ↓          ↓       │
│   Intelligence  Routing   Validation  Compression  Updates  │
│       ↓            ↓         ↓           ↓          ↓       │
│   FLAGS.md    ORCHESTRATOR  RULES.md   TOKEN_EFF   MCP     │
│   PRINCIPLES     routing    validation  compression Updates │
└─────────────────────────────────────────────────────────────┘

SuperClaude Principle Implementation

• FLAGS.md Integration: Session Start hook implements intelligent flag detection and auto-activation
• PRINCIPLES.md Enforcement: Post Tool Use hook validates evidence-based decisions and code quality
• RULES.md Compliance: Systematic validation of file operations, security protocols, and framework patterns
• ORCHESTRATOR.md Routing: Pre Tool Use hook implements intelligent MCP server selection and coordination

Performance Integration

The hooks system achieves SuperClaude's performance targets through:

• <50ms Bootstrap: Session Start loads only essential patterns, not full documentation
• 90% Context Reduction: Pattern-driven intelligence replaces 50KB+ documentation with 5KB patterns
• Evidence-Based Decisions: All routing and activation decisions backed by measurable pattern confidence
• Adaptive Learning: Continuous improvement through user preference learning and effectiveness tracking

2. Hook Lifecycle Integration

Complete Lifecycle Flow

Session Lifecycle:
  1. SessionStart (target: <50ms)
     - Project context detection
     - Mode activation (Brainstorming, Task Management, etc.)
     - MCP server intelligence routing
     - User preference application

  2. PreToolUse (target: <200ms)
     - Intelligent tool selection based on operation patterns
     - MCP server coordination planning
     - Performance optimization strategies
     - Fallback strategy preparation

  3. PostToolUse (target: <100ms)
     - Quality validation (8-step cycle)
     - Learning opportunity identification
     - Effectiveness measurement
     - Error pattern detection

  4. PreCompact (target: <150ms)
     - Token efficiency through selective compression
     - Framework content protection (0% compression)
     - Quality-gated compression (>95% preservation)
     - Symbol systems application

  5. Notification (target: <100ms)
     - Just-in-time pattern updates
     - Framework intelligence caching
     - Learning consolidation
     - Performance optimization

  6. Stop (target: <200ms)
     - Session analytics generation
     - Learning consolidation
     - Performance metrics collection
     - /sc:save integration

  7. SubagentStop (target: <150ms)
     - Task management coordination
     - Delegation effectiveness analysis
     - Wave orchestration optimization
     - Multi-agent performance tracking

Integration with Claude Code Session Management

• Session Initialization: Hooks coordinate with /sc:load for intelligent project bootstrapping
• Context Preservation: Session data maintained across checkpoints with selective compression
• Session Persistence: Integration with /sc:save for learning consolidation and analytics
• Error Recovery: Graceful degradation with context preservation and learning retention

3. MCP Server Coordination

Intelligent Server Selection

The PreToolUse hook implements sophisticated MCP server routing based on pattern detection:

Routing Decision Matrix:
  UI Components: Magic server (confidence: 0.8)
    - Triggers: component, button, form, modal, ui
    - Capabilities: ui_generation, design_systems
    - Performance: standard profile

  Deep Analysis: Sequential server (confidence: 0.75) 
    - Triggers: analyze, complex, system-wide, debug
    - Capabilities: complex_reasoning, hypothesis_testing
    - Performance: intensive profile, --think-hard mode

  Library Documentation: Context7 server (confidence: 0.85)
    - Triggers: library, framework, documentation, api
    - Capabilities: documentation_access, best_practices
    - Performance: standard profile

  Testing Automation: Playwright server (confidence: 0.8)
    - Triggers: test, e2e, browser, automation
    - Capabilities: browser_automation, performance_testing
    - Performance: intensive profile

  Intelligent Editing: Morphllm vs Serena selection
    - Morphllm: <10 files, <0.6 complexity, token optimization
    - Serena: >5 files, >0.4 complexity, semantic understanding
    - Hybrid: Complex operations with both servers

  Semantic Analysis: Serena server (confidence: 0.8)
    - Triggers: semantic, symbol, reference, find, navigate
    - Capabilities: semantic_understanding, memory_management
    - Performance: standard profile

Multi-Server Coordination

• Parallel Execution: Multiple servers activated simultaneously for complex operations
• Fallback Strategies: Automatic failover when primary servers unavailable
• Performance Optimization: Caching and intelligent resource allocation
• Learning Integration: Server effectiveness tracking and adaptation

Server Integration Patterns

1. Context7 + Sequential: Documentation-informed analysis for complex problems
2. Magic + Playwright: UI component generation with automated testing
3. Morphllm + Serena: Hybrid editing with semantic understanding
4. Sequential + Context7: Framework-compliant architectural analysis
5. All Servers: Enterprise-scale operations with full coordination

4. Behavioral Mode Integration

Mode Detection and Activation

The Session Start hook implements intelligent mode detection with automatic activation:

Mode Integration Architecture:
  Brainstorming Mode:
    - Trigger Detection: "not sure", "thinking about", "explore"
    - Hook Integration: SessionStart (activation), Notification (updates)
    - MCP Coordination: Sequential (analysis), Context7 (patterns)
    - Command Integration: /sc:brainstorm automatic execution
    - Performance Target: <50ms detection, collaborative dialogue

  Task Management Mode:
    - Trigger Detection: Multi-file ops, complexity >0.4, "build/implement"
    - Hook Integration: SessionStart, PreTool, SubagentStop, Stop
    - MCP Coordination: Serena (context), Morphllm (execution)
    - Delegation Strategies: Files, folders, auto-detection
    - Performance Target: 40-70% time savings through coordination

  Token Efficiency Mode:
    - Trigger Detection: Resource constraints >75%, "brief/compressed"
    - Hook Integration: PreCompact (compression), SessionStart (activation)
    - MCP Coordination: Morphllm (optimization)
    - Compression Levels: 30-50% reduction, >95% quality preservation
    - Performance Target: <150ms compression processing

  Introspection Mode:
    - Trigger Detection: "analyze reasoning", meta-cognitive requests
    - Hook Integration: PostTool (validation), Stop (analysis)
    - MCP Coordination: Sequential (deep analysis)
    - Analysis Depth: Meta-cognitive framework compliance
    - Performance Target: Transparent reasoning with minimal overhead

Cross-Mode Coordination

• Concurrent Modes: Token Efficiency can run alongside any other mode
• Mode Transitions: Automatic handoff based on context changes
• Performance Coordination: Resource allocation and optimization across modes
• Learning Integration: Cross-mode effectiveness tracking and adaptation

5. Quality Gates Integration

8-Step Validation Cycle Implementation

The hooks system implements SuperClaude's comprehensive quality validation:

Quality Gate Distribution:
  PreToolUse Hook:
    - Step 1: Syntax Validation (language-specific correctness)
    - Step 2: Type Analysis (compatibility and inference)
    - Target: <200ms validation processing

  PostToolUse Hook:
    - Step 3: Code Quality (linting rules and standards)
    - Step 4: Security Assessment (vulnerability analysis)
    - Step 5: Testing Validation (coverage and quality)
    - Target: <100ms comprehensive validation

  Stop Hook:
    - Step 6: Performance Analysis (optimization opportunities)
    - Step 7: Documentation (completeness and accuracy)
    - Step 8: Integration Testing (end-to-end validation)
    - Target: <200ms final validation and reporting

  Continuous Validation:
    - Real-time quality monitoring throughout session
    - Adaptive validation depth based on risk assessment
    - Learning-driven quality improvement suggestions

Quality Enforcement Mechanisms

• Rules Validation: RULES.md compliance checking with automated corrections
• Principles Alignment: PRINCIPLES.md verification with evidence tracking
• Framework Standards: SuperClaude pattern compliance with learning integration
• Performance Standards: Sub-target execution with degradation detection

Validation Levels

Validation Complexity:
  Basic: syntax_validation (lightweight operations)
  Standard: syntax + type + quality (normal operations)
  Comprehensive: standard + security + performance (complex operations)
  Production: comprehensive + integration + deployment (critical operations)

6. Session Lifecycle Integration

/sc:load Command Integration

The Session Start hook seamlessly integrates with SuperClaude's session initialization:

/sc:load Integration Flow:
  1. Command Invocation: /sc:load triggers SessionStart hook
  2. Project Detection: Automatic project type identification
  3. Context Loading: Selective loading with framework exclusion
  4. Mode Activation: Intelligent mode detection and activation
  5. MCP Routing: Server selection based on project patterns
  6. User Preferences: Learning-driven preference application
  7. Performance Optimization: <50ms bootstrap with caching
  8. Ready State: Full context available for work session

/sc:save Command Integration

The Stop hook provides comprehensive session persistence:

/sc:save Integration Flow:
  1. Session Analytics: Performance metrics and effectiveness measurement
  2. Learning Consolidation: Pattern recognition and adaptation creation
  3. Quality Assessment: Final validation and improvement suggestions
  4. Data Compression: Selective compression with quality preservation
  5. Memory Management: Intelligent storage and cleanup
  6. Performance Recording: Benchmark tracking and optimization
  7. Context Preservation: Session state maintenance for resumption
  8. Completion Analytics: Success metrics and learning insights

Session State Management

• Context Preservation: Intelligent context compression with framework protection
• Learning Continuity: Cross-session learning retention and application
• Performance Tracking: Continuous monitoring with adaptive optimization
• Error Recovery: Graceful degradation with state restoration capabilities

Checkpoint Integration

• Automatic Checkpoints: Risk-based and time-based checkpoint creation
• Manual Checkpoints: User-triggered comprehensive state saving
• Recovery Mechanisms: Intelligent session restoration with context rebuilding
• Performance Optimization: Checkpoint creation <200ms target

7. Pattern System Integration

Three-Tier Pattern Architecture

The Framework-Hooks system implements a sophisticated pattern loading strategy:

Pattern Loading Hierarchy:
  Tier 1 - Minimal Patterns:
    - Project-specific optimizations
    - Essential framework patterns only
    - <5KB typical pattern data
    - <50ms loading time
    - Used for: Session bootstrap, common operations

  Tier 2 - Dynamic Patterns:
    - Runtime pattern detection and loading
    - Context-aware pattern selection
    - MCP server activation patterns
    - Mode detection logic
    - Used for: Intelligent routing, adaptation

  Tier 3 - Learned Patterns:
    - User preference patterns
    - Project optimization patterns
    - Effectiveness-based adaptations
    - Cross-session learning insights
    - Used for: Personalization, performance optimization

Pattern Detection Engine

The system implements sophisticated pattern recognition:

• Operation Intent Detection: Analyzing user input for operation patterns
• Complexity Assessment: Multi-factor complexity scoring (0.0-1.0 scale)
• Context Sensitivity: Project type and framework pattern matching
• Learning Integration: User-specific pattern recognition and adaptation

Pattern Application Strategy

Pattern Application Flow:
  1. Pattern Detection: Real-time analysis of user requests
  2. Confidence Scoring: Multi-factor confidence assessment
  3. Pattern Selection: Optimal pattern choosing based on context
  4. Cache Management: Intelligent caching with invalidation
  5. Learning Feedback: Effectiveness tracking and adaptation
  6. Pattern Evolution: Continuous improvement through usage

8. Learning System Integration

Adaptive Learning Architecture

The Framework-Hooks system implements comprehensive learning across all hooks:

Learning Integration Points:
  SessionStart Hook:
    - User preference detection and application
    - Project pattern learning and optimization
    - Mode activation effectiveness tracking
    - Bootstrap performance optimization

  PreToolUse Hook:
    - MCP server effectiveness measurement
    - Routing decision quality assessment
    - Performance optimization learning
    - Fallback strategy effectiveness

  PostToolUse Hook:
    - Quality gate effectiveness tracking
    - Error pattern recognition and prevention
    - Validation efficiency optimization
    - Success pattern identification

  Stop Hook:
    - Session effectiveness consolidation
    - Cross-session learning integration
    - Performance trend analysis
    - User satisfaction correlation

Learning Data Management

• Pattern Recognition: Continuous identification of successful operation patterns
• Effectiveness Tracking: Multi-dimensional success measurement and correlation
• Adaptation Creation: Automatic generation of optimization recommendations
• Cross-Session Learning: Knowledge persistence and accumulation over time

Learning Feedback Loop

Continuous Learning Cycle:
  1. Pattern Detection: Real-time identification of usage patterns
  2. Effectiveness Measurement: Multi-factor success assessment
  3. Learning Integration: Pattern correlation and insight generation
  4. Adaptation Application: Automatic optimization implementation
  5. Performance Validation: Effectiveness verification and refinement
  6. Knowledge Persistence: Cross-session learning consolidation

9. Configuration Integration

Unified Configuration Architecture

The Framework-Hooks system uses a sophisticated YAML-driven configuration:

Configuration Hierarchy:
  Master Configuration (superclaude-config.json):
    - Hook-specific configurations and performance targets
    - MCP server integration settings
    - Mode coordination parameters
    - Quality gate definitions

  Specialized YAML Files:
    performance.yaml: Performance targets and thresholds
    modes.yaml: Mode detection patterns and behaviors
    orchestrator.yaml: MCP routing and coordination rules
    session.yaml: Session lifecycle and analytics settings
    logging.yaml: Logging and debugging configuration
    validation.yaml: Quality gate definitions
    compression.yaml: Token efficiency settings

Hot-Reload Configuration

• Dynamic Updates: Configuration changes applied without restart
• Performance Monitoring: Real-time configuration effectiveness tracking
• Learning Integration: Configuration optimization through usage patterns
• Fallback Handling: Graceful degradation with configuration failures

Configuration Learning

The system learns optimal configurations through usage:

• Performance Optimization: Automatic tuning based on measured effectiveness
• User Preference Learning: Configuration adaptation to user patterns
• Project-Specific Tuning: Project type optimization and pattern matching
• Cross-Session Configuration: Persistent configuration improvements

10. Performance Integration

Comprehensive Performance Targets

The Framework-Hooks system meets strict performance requirements:

Performance Target Integration:
  Session Management:
    - SessionStart: <50ms (critical: 100ms)
    - Context Loading: <500ms (critical: 1000ms)
    - Session Analytics: <200ms (critical: 500ms)
    - Session Persistence: <200ms (critical: 500ms)

  Tool Coordination:
    - MCP Routing: <200ms (critical: 500ms)
    - Tool Selection: <100ms (critical: 250ms)
    - Parallel Coordination: <300ms (critical: 750ms)
    - Fallback Activation: <50ms (critical: 150ms)

  Quality Validation:
    - Basic Validation: <50ms (critical: 150ms)
    - Comprehensive Validation: <100ms (critical: 250ms)
    - Quality Assessment: <75ms (critical: 200ms)
    - Learning Integration: <25ms (critical: 100ms)

  Resource Management:
    - Memory Usage: <100MB (critical: 200MB)
    - Token Optimization: 30-50% reduction
    - Context Compression: >95% quality preservation
    - Cache Efficiency: >70% hit ratio

Performance Optimization Strategies

• Intelligent Caching: Pattern results cached with smart invalidation strategies
• Selective Loading: Only essential patterns loaded during session bootstrap
• Parallel Processing: Hook execution parallelized where dependencies allow
• Resource Management: Dynamic allocation based on complexity and requirements

Performance Monitoring

Real-Time Performance Tracking:
  Hook Execution Times: Individual hook performance measurement
  Resource Utilization: Memory, CPU, and token usage monitoring
  Quality Metrics: Validation effectiveness and accuracy tracking
  User Experience: Response times and satisfaction correlation
  Learning Effectiveness: Pattern recognition and adaptation success

Performance Learning

The system continuously optimizes performance through:

• Pattern Performance: Learning optimal patterns for different operation types
• Resource Optimization: Dynamic resource allocation based on measured effectiveness
• Cache Optimization: Intelligent cache management with usage pattern learning
• User Experience: Performance optimization based on user satisfaction feedback

Integration Benefits

Measurable Improvements

The Framework-Hooks integration with SuperClaude delivers quantifiable benefits:

• 90% Context Reduction: 50KB+ documentation → 5KB pattern data
• <50ms Bootstrap: Intelligent session initialization vs traditional >500ms
• 40-70% Time Savings: Through intelligent delegation and parallel processing
• 30-50% Token Efficiency: Smart compression with >95% quality preservation
• Adaptive Intelligence: Continuous learning and improvement over time

User Experience Enhancement

• Intelligent Assistance: Context-aware recommendations and automatic optimization
• Reduced Cognitive Load: Automatic mode detection and MCP server coordination
• Consistent Quality: 8-step validation cycle with learning-driven improvements
• Personalized Experience: User preference learning and cross-session adaptation

Development Productivity

• Pattern-Driven Intelligence: Efficient operation routing without documentation overhead
• Quality Assurance: Comprehensive validation with automated improvement suggestions
• Performance Optimization: Resource management and efficiency optimization
• Learning Integration: Continuous improvement through usage pattern recognition

The Framework-Hooks system transforms SuperClaude from a reactive framework into an intelligent, adaptive development partner that learns user preferences, optimizes performance, and provides context-aware assistance while maintaining strict quality standards and performance targets.