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docs: Add comprehensive Framework-Hooks documentation
Complete technical documentation for the SuperClaude Framework-Hooks system: • Overview documentation explaining pattern-driven intelligence architecture • Individual hook documentation for all 7 lifecycle hooks with performance targets • Complete configuration documentation for all YAML/JSON config files • Pattern system documentation covering minimal/dynamic/learned patterns • Shared modules documentation for all core intelligence components • Integration guide showing SuperClaude framework coordination • Performance guide with optimization strategies and benchmarks Key technical features documented: - 90% context reduction through pattern-driven approach (50KB+ → 5KB) - 10x faster bootstrap performance (500ms+ → <50ms) - 7 lifecycle hooks with specific performance targets (50-200ms) - 5-level compression system with quality preservation ≥95% - Just-in-time capability loading with intelligent caching - Cross-hook learning system for continuous improvement - MCP server coordination for all 6 servers - Integration with 4 behavioral modes and 8-step quality gates Documentation provides complete technical reference for developers, system administrators, and users working with the Framework-Hooks system architecture and implementation. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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# Framework-Hooks Integration with SuperClaude
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## Overview
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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.
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## 1. SuperClaude Framework Integration
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### Core Integration Architecture
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The Framework-Hooks system enhances Claude Code through seven strategic lifecycle hooks that implement SuperClaude's core principles:
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Claude Code Runtime │
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├─────────────────────────────────────────────────────────────┤
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│ SessionStart → PreTool → PostTool → PreCompact → Notify │
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│ ↓ ↓ ↓ ↓ ↓ │
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│ Intelligence Routing Validation Compression Updates │
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│ ↓ ↓ ↓ ↓ ↓ │
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│ FLAGS.md ORCHESTRATOR RULES.md TOKEN_EFF MCP │
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│ PRINCIPLES routing validation compression Updates │
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└─────────────────────────────────────────────────────────────┘
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```
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### SuperClaude Principle Implementation
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- **FLAGS.md Integration**: Session Start hook implements intelligent flag detection and auto-activation
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- **PRINCIPLES.md Enforcement**: Post Tool Use hook validates evidence-based decisions and code quality
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- **RULES.md Compliance**: Systematic validation of file operations, security protocols, and framework patterns
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- **ORCHESTRATOR.md Routing**: Pre Tool Use hook implements intelligent MCP server selection and coordination
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### Performance Integration
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The hooks system achieves SuperClaude's performance targets through:
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- **<50ms Bootstrap**: Session Start loads only essential patterns, not full documentation
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- **90% Context Reduction**: Pattern-driven intelligence replaces 50KB+ documentation with 5KB patterns
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- **Evidence-Based Decisions**: All routing and activation decisions backed by measurable pattern confidence
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- **Adaptive Learning**: Continuous improvement through user preference learning and effectiveness tracking
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## 2. Hook Lifecycle Integration
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### Complete Lifecycle Flow
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```yaml
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Session Lifecycle:
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1. SessionStart (target: <50ms)
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- Project context detection
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- Mode activation (Brainstorming, Task Management, etc.)
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- MCP server intelligence routing
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- User preference application
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2. PreToolUse (target: <200ms)
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- Intelligent tool selection based on operation patterns
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- MCP server coordination planning
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- Performance optimization strategies
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- Fallback strategy preparation
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3. PostToolUse (target: <100ms)
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- Quality validation (8-step cycle)
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- Learning opportunity identification
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- Effectiveness measurement
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- Error pattern detection
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4. PreCompact (target: <150ms)
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- Token efficiency through selective compression
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- Framework content protection (0% compression)
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- Quality-gated compression (>95% preservation)
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- Symbol systems application
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5. Notification (target: <100ms)
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- Just-in-time pattern updates
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- Framework intelligence caching
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- Learning consolidation
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- Performance optimization
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6. Stop (target: <200ms)
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- Session analytics generation
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- Learning consolidation
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- Performance metrics collection
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- /sc:save integration
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7. SubagentStop (target: <150ms)
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- Task management coordination
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- Delegation effectiveness analysis
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- Wave orchestration optimization
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- Multi-agent performance tracking
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```
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### Integration with Claude Code Session Management
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- **Session Initialization**: Hooks coordinate with `/sc:load` for intelligent project bootstrapping
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- **Context Preservation**: Session data maintained across checkpoints with selective compression
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- **Session Persistence**: Integration with `/sc:save` for learning consolidation and analytics
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- **Error Recovery**: Graceful degradation with context preservation and learning retention
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## 3. MCP Server Coordination
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### Intelligent Server Selection
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The PreToolUse hook implements sophisticated MCP server routing based on pattern detection:
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```yaml
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Routing Decision Matrix:
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UI Components: Magic server (confidence: 0.8)
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- Triggers: component, button, form, modal, ui
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- Capabilities: ui_generation, design_systems
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- Performance: standard profile
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Deep Analysis: Sequential server (confidence: 0.75)
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- Triggers: analyze, complex, system-wide, debug
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- Capabilities: complex_reasoning, hypothesis_testing
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- Performance: intensive profile, --think-hard mode
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Library Documentation: Context7 server (confidence: 0.85)
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- Triggers: library, framework, documentation, api
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- Capabilities: documentation_access, best_practices
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- Performance: standard profile
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Testing Automation: Playwright server (confidence: 0.8)
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- Triggers: test, e2e, browser, automation
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- Capabilities: browser_automation, performance_testing
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- Performance: intensive profile
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Intelligent Editing: Morphllm vs Serena selection
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- Morphllm: <10 files, <0.6 complexity, token optimization
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- Serena: >5 files, >0.4 complexity, semantic understanding
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- Hybrid: Complex operations with both servers
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Semantic Analysis: Serena server (confidence: 0.8)
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- Triggers: semantic, symbol, reference, find, navigate
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- Capabilities: semantic_understanding, memory_management
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- Performance: standard profile
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```
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### Multi-Server Coordination
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- **Parallel Execution**: Multiple servers activated simultaneously for complex operations
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- **Fallback Strategies**: Automatic failover when primary servers unavailable
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- **Performance Optimization**: Caching and intelligent resource allocation
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- **Learning Integration**: Server effectiveness tracking and adaptation
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### Server Integration Patterns
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1. **Context7 + Sequential**: Documentation-informed analysis for complex problems
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2. **Magic + Playwright**: UI component generation with automated testing
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3. **Morphllm + Serena**: Hybrid editing with semantic understanding
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4. **Sequential + Context7**: Framework-compliant architectural analysis
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5. **All Servers**: Enterprise-scale operations with full coordination
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## 4. Behavioral Mode Integration
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### Mode Detection and Activation
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The Session Start hook implements intelligent mode detection with automatic activation:
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```yaml
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Mode Integration Architecture:
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Brainstorming Mode:
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- Trigger Detection: "not sure", "thinking about", "explore"
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- Hook Integration: SessionStart (activation), Notification (updates)
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- MCP Coordination: Sequential (analysis), Context7 (patterns)
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- Command Integration: /sc:brainstorm automatic execution
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- Performance Target: <50ms detection, collaborative dialogue
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Task Management Mode:
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- Trigger Detection: Multi-file ops, complexity >0.4, "build/implement"
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- Hook Integration: SessionStart, PreTool, SubagentStop, Stop
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- MCP Coordination: Serena (context), Morphllm (execution)
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- Delegation Strategies: Files, folders, auto-detection
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- Performance Target: 40-70% time savings through coordination
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Token Efficiency Mode:
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- Trigger Detection: Resource constraints >75%, "brief/compressed"
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- Hook Integration: PreCompact (compression), SessionStart (activation)
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- MCP Coordination: Morphllm (optimization)
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- Compression Levels: 30-50% reduction, >95% quality preservation
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- Performance Target: <150ms compression processing
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Introspection Mode:
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- Trigger Detection: "analyze reasoning", meta-cognitive requests
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- Hook Integration: PostTool (validation), Stop (analysis)
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- MCP Coordination: Sequential (deep analysis)
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- Analysis Depth: Meta-cognitive framework compliance
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- Performance Target: Transparent reasoning with minimal overhead
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```
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### Cross-Mode Coordination
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- **Concurrent Modes**: Token Efficiency can run alongside any other mode
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- **Mode Transitions**: Automatic handoff based on context changes
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- **Performance Coordination**: Resource allocation and optimization across modes
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- **Learning Integration**: Cross-mode effectiveness tracking and adaptation
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## 5. Quality Gates Integration
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### 8-Step Validation Cycle Implementation
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The hooks system implements SuperClaude's comprehensive quality validation:
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```yaml
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Quality Gate Distribution:
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PreToolUse Hook:
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- Step 1: Syntax Validation (language-specific correctness)
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- Step 2: Type Analysis (compatibility and inference)
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- Target: <200ms validation processing
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PostToolUse Hook:
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- Step 3: Code Quality (linting rules and standards)
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- Step 4: Security Assessment (vulnerability analysis)
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- Step 5: Testing Validation (coverage and quality)
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- Target: <100ms comprehensive validation
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Stop Hook:
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- Step 6: Performance Analysis (optimization opportunities)
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- Step 7: Documentation (completeness and accuracy)
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- Step 8: Integration Testing (end-to-end validation)
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- Target: <200ms final validation and reporting
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Continuous Validation:
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- Real-time quality monitoring throughout session
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- Adaptive validation depth based on risk assessment
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- Learning-driven quality improvement suggestions
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```
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### Quality Enforcement Mechanisms
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- **Rules Validation**: RULES.md compliance checking with automated corrections
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- **Principles Alignment**: PRINCIPLES.md verification with evidence tracking
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- **Framework Standards**: SuperClaude pattern compliance with learning integration
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- **Performance Standards**: Sub-target execution with degradation detection
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### Validation Levels
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```yaml
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Validation Complexity:
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Basic: syntax_validation (lightweight operations)
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Standard: syntax + type + quality (normal operations)
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Comprehensive: standard + security + performance (complex operations)
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Production: comprehensive + integration + deployment (critical operations)
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```
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## 6. Session Lifecycle Integration
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### /sc:load Command Integration
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The Session Start hook seamlessly integrates with SuperClaude's session initialization:
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```yaml
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/sc:load Integration Flow:
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1. Command Invocation: /sc:load triggers SessionStart hook
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2. Project Detection: Automatic project type identification
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3. Context Loading: Selective loading with framework exclusion
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4. Mode Activation: Intelligent mode detection and activation
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5. MCP Routing: Server selection based on project patterns
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6. User Preferences: Learning-driven preference application
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7. Performance Optimization: <50ms bootstrap with caching
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8. Ready State: Full context available for work session
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```
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### /sc:save Command Integration
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The Stop hook provides comprehensive session persistence:
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```yaml
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/sc:save Integration Flow:
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1. Session Analytics: Performance metrics and effectiveness measurement
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2. Learning Consolidation: Pattern recognition and adaptation creation
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3. Quality Assessment: Final validation and improvement suggestions
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4. Data Compression: Selective compression with quality preservation
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5. Memory Management: Intelligent storage and cleanup
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6. Performance Recording: Benchmark tracking and optimization
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7. Context Preservation: Session state maintenance for resumption
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8. Completion Analytics: Success metrics and learning insights
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```
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### Session State Management
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- **Context Preservation**: Intelligent context compression with framework protection
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- **Learning Continuity**: Cross-session learning retention and application
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- **Performance Tracking**: Continuous monitoring with adaptive optimization
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- **Error Recovery**: Graceful degradation with state restoration capabilities
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### Checkpoint Integration
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- **Automatic Checkpoints**: Risk-based and time-based checkpoint creation
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- **Manual Checkpoints**: User-triggered comprehensive state saving
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- **Recovery Mechanisms**: Intelligent session restoration with context rebuilding
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- **Performance Optimization**: Checkpoint creation <200ms target
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## 7. Pattern System Integration
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### Three-Tier Pattern Architecture
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The Framework-Hooks system implements a sophisticated pattern loading strategy:
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```yaml
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Pattern Loading Hierarchy:
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Tier 1 - Minimal Patterns:
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- Project-specific optimizations
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- Essential framework patterns only
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- <5KB typical pattern data
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- <50ms loading time
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- Used for: Session bootstrap, common operations
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Tier 2 - Dynamic Patterns:
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- Runtime pattern detection and loading
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- Context-aware pattern selection
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- MCP server activation patterns
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- Mode detection logic
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- Used for: Intelligent routing, adaptation
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Tier 3 - Learned Patterns:
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- User preference patterns
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- Project optimization patterns
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- Effectiveness-based adaptations
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- Cross-session learning insights
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- Used for: Personalization, performance optimization
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```
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### Pattern Detection Engine
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The system implements sophisticated pattern recognition:
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- **Operation Intent Detection**: Analyzing user input for operation patterns
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- **Complexity Assessment**: Multi-factor complexity scoring (0.0-1.0 scale)
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- **Context Sensitivity**: Project type and framework pattern matching
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- **Learning Integration**: User-specific pattern recognition and adaptation
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### Pattern Application Strategy
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```yaml
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Pattern Application Flow:
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1. Pattern Detection: Real-time analysis of user requests
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2. Confidence Scoring: Multi-factor confidence assessment
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3. Pattern Selection: Optimal pattern choosing based on context
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4. Cache Management: Intelligent caching with invalidation
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5. Learning Feedback: Effectiveness tracking and adaptation
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6. Pattern Evolution: Continuous improvement through usage
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```
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## 8. Learning System Integration
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### Adaptive Learning Architecture
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The Framework-Hooks system implements comprehensive learning across all hooks:
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```yaml
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Learning Integration Points:
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SessionStart Hook:
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- User preference detection and application
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- Project pattern learning and optimization
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- Mode activation effectiveness tracking
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- Bootstrap performance optimization
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PreToolUse Hook:
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- MCP server effectiveness measurement
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- Routing decision quality assessment
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- Performance optimization learning
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- Fallback strategy effectiveness
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PostToolUse Hook:
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- Quality gate effectiveness tracking
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- Error pattern recognition and prevention
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- Validation efficiency optimization
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- Success pattern identification
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Stop Hook:
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- Session effectiveness consolidation
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- Cross-session learning integration
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- Performance trend analysis
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- User satisfaction correlation
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```
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### Learning Data Management
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- **Pattern Recognition**: Continuous identification of successful operation patterns
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- **Effectiveness Tracking**: Multi-dimensional success measurement and correlation
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- **Adaptation Creation**: Automatic generation of optimization recommendations
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- **Cross-Session Learning**: Knowledge persistence and accumulation over time
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### Learning Feedback Loop
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```yaml
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Continuous Learning Cycle:
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1. Pattern Detection: Real-time identification of usage patterns
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2. Effectiveness Measurement: Multi-factor success assessment
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3. Learning Integration: Pattern correlation and insight generation
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4. Adaptation Application: Automatic optimization implementation
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5. Performance Validation: Effectiveness verification and refinement
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6. Knowledge Persistence: Cross-session learning consolidation
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```
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## 9. Configuration Integration
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### Unified Configuration Architecture
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The Framework-Hooks system uses a sophisticated YAML-driven configuration:
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```yaml
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Configuration Hierarchy:
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Master Configuration (superclaude-config.json):
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- Hook-specific configurations and performance targets
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- MCP server integration settings
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- Mode coordination parameters
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- Quality gate definitions
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Specialized YAML Files:
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performance.yaml: Performance targets and thresholds
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modes.yaml: Mode detection patterns and behaviors
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orchestrator.yaml: MCP routing and coordination rules
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session.yaml: Session lifecycle and analytics settings
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logging.yaml: Logging and debugging configuration
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||||
validation.yaml: Quality gate definitions
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||||
compression.yaml: Token efficiency settings
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||||
```
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||||
|
||||
### Hot-Reload Configuration
|
||||
|
||||
- **Dynamic Updates**: Configuration changes applied without restart
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||||
- **Performance Monitoring**: Real-time configuration effectiveness tracking
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||||
- **Learning Integration**: Configuration optimization through usage patterns
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||||
- **Fallback Handling**: Graceful degradation with configuration failures
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||||
|
||||
### Configuration Learning
|
||||
|
||||
The system learns optimal configurations through usage:
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||||
|
||||
- **Performance Optimization**: Automatic tuning based on measured effectiveness
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||||
- **User Preference Learning**: Configuration adaptation to user patterns
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||||
- **Project-Specific Tuning**: Project type optimization and pattern matching
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||||
- **Cross-Session Configuration**: Persistent configuration improvements
|
||||
|
||||
## 10. Performance Integration
|
||||
|
||||
### Comprehensive Performance Targets
|
||||
|
||||
The Framework-Hooks system meets strict performance requirements:
|
||||
|
||||
```yaml
|
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Performance Target Integration:
|
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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)
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||||
- Comprehensive Validation: <100ms (critical: 250ms)
|
||||
- Quality Assessment: <75ms (critical: 200ms)
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||||
- 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.
|
||||
Reference in New Issue
Block a user