# 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 ```yaml 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: ```yaml 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: ```yaml 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: ```yaml 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 ```yaml 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: ```yaml /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: ```yaml /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: ```yaml 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 ```yaml 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: ```yaml 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 ```yaml 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: ```yaml 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: ```yaml 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 ```yaml 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.