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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/docs/Overview.md
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# Framework-Hooks System Overview
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## System Architecture
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The Framework-Hooks system is a pattern-driven intelligence layer that enhances Claude Code's capabilities through lifecycle hooks and shared components. The system operates on a modular architecture consisting of:
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### Core Components
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1. **Lifecycle Hooks** - 7 Python modules that run at specific points in Claude Code execution
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2. **Shared Intelligence Modules** - Common functionality providing pattern detection, learning, and framework logic
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3. **YAML Configuration System** - Dynamic, hot-reloadable configuration files
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4. **Performance Monitoring** - Real-time tracking with sub-50ms bootstrap targets
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5. **Adaptive Learning Engine** - Continuous improvement through pattern recognition and user adaptation
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### Architecture Layers
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```
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┌─────────────────────────────────────────┐
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│ Claude Code Interface │
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├─────────────────────────────────────────┤
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│ Lifecycle Hooks │
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│ ┌─────┬─────┬─────┬─────┬─────┬─────┐ │
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│ │Start│Pre │Post │Pre │Notif│Stop │ │
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│ │ │Tool │Tool │Comp │ │ │ │
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│ └─────┴─────┴─────┴─────┴─────┴─────┘ │
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├─────────────────────────────────────────┤
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│ Shared Intelligence │
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│ ┌────────────┬─────────────┬─────────┐ │
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│ │ Framework │ Pattern │Learning │ │
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│ │ Logic │ Detection │Engine │ │
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│ └────────────┴─────────────┴─────────┘ │
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├─────────────────────────────────────────┤
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│ YAML Configuration │
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│ ┌─────────────┬────────────┬─────────┐ │
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│ │Performance │Modes │Logging │ │
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│ │Targets │Config │Config │ │
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│ └─────────────┴────────────┴─────────┘ │
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└─────────────────────────────────────────┘
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```
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## Purpose
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The Framework-Hooks system solves critical performance and intelligence challenges in AI-assisted development:
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### Primary Problems Solved
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1. **Context Bloat** - Reduces context usage through pattern-driven intelligence instead of loading complete documentation
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2. **Bootstrap Performance** - Achieves <50ms session initialization through intelligent caching and selective loading
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3. **Decision Intelligence** - Provides context-aware routing and MCP server selection based on operation patterns
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4. **Adaptive Learning** - Continuously improves performance through user preference learning and pattern recognition
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5. **Resource Optimization** - Manages memory, CPU, and token usage through real-time monitoring and adaptive compression
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### System Benefits
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- **Performance**: Sub-50ms bootstrap times with intelligent caching
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- **Intelligence**: Pattern-driven decision making without documentation overhead
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- **Adaptability**: Learns user preferences and project patterns over time
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- **Scalability**: Handles complex multi-domain operations through coordination
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- **Reliability**: Graceful degradation with fallback strategies
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## Pattern-Driven Intelligence
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The system differs fundamentally from traditional documentation-driven approaches:
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### Traditional Approach
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```
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Session Start → Load 50KB+ Documentation → Parse → Apply Rules → Execute
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```
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**Problems**: High latency, memory usage, context bloat
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### Pattern-Driven Approach
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```
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User Request → Pattern Detection → Cached Intelligence → Smart Routing → Execute
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```
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**Benefits**: Context reduction, <50ms response, adaptive learning
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### Intelligence Components
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1. **Pattern Detection Engine**
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- Analyzes user input for operation intent
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- Detects complexity indicators and scope
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- Identifies framework patterns and project types
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- Determines optimal routing strategies
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2. **Learning Engine**
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- Records user preferences and successful patterns
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- Adapts recommendations based on effectiveness
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- Maintains project-specific optimizations
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- Provides cross-session learning continuity
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3. **MCP Intelligence Router**
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- Selects optimal MCP servers based on context
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- Coordinates multi-server operations
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- Implements fallback strategies
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- Optimizes activation order and resource usage
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4. **Framework Logic Engine**
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- Applies SuperClaude principles (RULES.md, PRINCIPLES.md)
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- Determines quality gates and validation levels
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- Calculates complexity scores and risk assessments
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- Provides evidence-based decision making
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## Performance Optimization
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The system achieves exceptional performance through multiple optimization strategies:
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### Bootstrap Optimization (<50ms target)
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1. **Selective Loading**
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- Only loads patterns relevant to current operation
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- Caches frequently used intelligence
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- Defers non-critical initialization
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2. **Intelligent Caching**
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- Pattern results cached with smart invalidation
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- Learning data compressed and indexed
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- MCP server configurations pre-computed
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3. **Parallel Processing**
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- Hook execution parallelized where possible
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- Background learning processing
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- Asynchronous pattern updates
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### Runtime Performance Targets
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| Component | Target | Critical Threshold |
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|---------------|--------|--------------------|
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| Session Start | <50ms | 100ms |
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| Tool Routing | <200ms | 500ms |
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| Validation | <100ms | 250ms |
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| Compression | <150ms | 300ms |
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| Notification | <100ms | 200ms |
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### Memory Optimization
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- **Pattern Data**: 5KB typical (vs 50KB+ documentation)
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- **Learning Cache**: Compressed storage with 30% efficiency
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- **Session Data**: Smart cleanup with 70% hit ratio
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- **Total Footprint**: <100MB target, 200MB critical
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## Directory Structure
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```
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Framework-Hooks/
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├── hooks/ # Lifecycle hook implementations
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│ ├── session_start.py # Session initialization & intelligence routing
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│ ├── pre_tool_use.py # MCP server selection & optimization
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│ ├── post_tool_use.py # Validation & learning integration
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│ ├── pre_compact.py # Token efficiency & compression
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│ ├── notification.py # Just-in-time pattern updates
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│ ├── stop.py # Session analytics & persistence
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│ ├── subagent_stop.py # Task management coordination
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│ └── shared/ # Shared intelligence modules
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│ ├── framework_logic.py # SuperClaude principles implementation
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│ ├── pattern_detection.py # Pattern recognition engine
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│ ├── mcp_intelligence.py # MCP server routing logic
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│ ├── learning_engine.py # Adaptive learning system
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│ ├── compression_engine.py # Token optimization algorithms
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│ ├── yaml_loader.py # Configuration management
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│ └── logger.py # Performance & debug logging
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├── config/ # YAML configuration files
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│ ├── performance.yaml # Performance targets & thresholds
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│ ├── modes.yaml # Mode activation patterns
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│ ├── orchestrator.yaml # Routing & coordination rules
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│ ├── session.yaml # Session management settings
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│ ├── logging.yaml # Logging configuration
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│ ├── validation.yaml # Quality gate definitions
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│ └── compression.yaml # Token efficiency settings
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├── patterns/ # Learning & pattern storage
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│ ├── dynamic/ # Runtime pattern detection
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│ ├── learned/ # User preference patterns
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│ └── minimal/ # Project-specific optimizations
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├── cache/ # Performance caching
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│ └── learning_records.json # Adaptive learning data
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├── docs/ # System documentation
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└── superclaude-config.json # Master configuration
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```
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## Key Components
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### 1. Session Start Hook (`session_start.py`)
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**Purpose**: Intelligent session bootstrap with <50ms performance target
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**Responsibilities**:
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- Project context detection and loading
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- Automatic mode activation based on user input patterns
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- MCP server intelligence routing
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- User preference application from learning engine
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- Performance-optimized initialization
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**Key Features**:
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- Pattern-based project type detection (Node.js, Python, Rust, Go)
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- Brainstorming mode auto-activation for ambiguous requests
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- Framework exclusion to prevent context bloat
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- Learning-driven user preference adaptation
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### 2. Pre-Tool Use Hook (`pre_tool_use.py`)
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**Purpose**: Intelligent tool routing and MCP server selection
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**Responsibilities**:
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- MCP server activation planning based on operation type
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- Performance optimization through parallel coordination
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- Context-aware tool selection
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- Fallback strategy implementation
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**Key Features**:
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- Pattern-based MCP server selection
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- Real-time performance monitoring
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- Intelligent caching of routing decisions
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- Cross-server coordination strategies
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### 3. Post-Tool Use Hook (`post_tool_use.py`)
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**Purpose**: Quality validation and learning integration
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**Responsibilities**:
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- RULES.md and PRINCIPLES.md compliance validation
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- Effectiveness measurement and learning
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- Error pattern detection
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- Quality score calculation
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**Key Features**:
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- 8-step quality gate validation
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- Learning opportunity identification
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- Performance effectiveness tracking
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- Adaptive improvement suggestions
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### 4. Pre-Compact Hook (`pre_compact.py`)
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**Purpose**: Token efficiency through intelligent compression
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**Responsibilities**:
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- Selective content compression (framework exclusion)
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- Symbol systems and abbreviation application
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- Quality-gated compression with >95% preservation
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- Adaptive compression level selection
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**Key Features**:
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- 5-level compression strategy (minimal to emergency)
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- Framework content protection (0% compression)
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- Real-time quality preservation monitoring
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- Context-aware compression selection
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### 5. Notification Hook (`notification.py`)
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**Purpose**: Just-in-time pattern updates and intelligence caching
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**Responsibilities**:
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- Dynamic pattern loading based on operation context
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- Framework intelligence updates
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- Performance optimization through selective caching
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- Real-time learning integration
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**Key Features**:
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- Context-sensitive documentation loading
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- Intelligent cache management with 30-60 minute TTL
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- Pattern update coordination
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- Learning-driven optimization
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### 6. Stop Hook (`stop.py`)
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**Purpose**: Session analytics and learning consolidation
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**Responsibilities**:
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- Comprehensive session performance analytics
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- Learning consolidation and persistence
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- Session quality assessment
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- Optimization recommendations generation
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**Key Features**:
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- End-to-end performance measurement
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- Learning effectiveness tracking
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- Session summary generation
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- Quality improvement suggestions
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### 7. Sub-Agent Stop Hook (`subagent_stop.py`)
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**Purpose**: Task management delegation coordination
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**Responsibilities**:
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- Sub-agent performance analytics
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- Delegation effectiveness measurement
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- Wave orchestration optimization
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- Parallel execution performance tracking
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**Key Features**:
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- Multi-agent coordination analytics
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- Delegation strategy optimization
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- Performance gain measurement
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- Resource utilization tracking
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## Integration with SuperClaude
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The Framework-Hooks system enhances Claude Code capabilities through deep integration with SuperClaude framework components:
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### Mode Integration
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1. **Brainstorming Mode**
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- Auto-activation through session_start pattern detection
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- Interactive requirements discovery
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- Brief generation and PRD handoff
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2. **Task Management Mode**
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- Wave orchestration through delegation patterns
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- Multi-layer task coordination (TodoWrite → /task → /spawn → /loop)
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- Performance analytics and optimization
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3. **Token Efficiency Mode**
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- Selective compression with framework protection
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- Symbol systems and abbreviation optimization
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- Quality-gated compression with preservation targets
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4. **Introspection Mode**
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- Meta-cognitive analysis integration
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- Framework compliance validation
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- Pattern recognition and learning
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### MCP Server Coordination
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- **Context7**: Library documentation and framework patterns
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- **Sequential**: Multi-step reasoning and complex analysis
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- **Magic**: UI component generation and design systems
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- **Playwright**: Browser automation and testing
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- **Morphllm**: Intelligent editing with pattern application
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- **Serena**: Semantic analysis and memory management
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### Quality Gates Integration
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The system implements the SuperClaude 8-step quality validation:
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1. **Syntax Validation** - Language-specific correctness
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2. **Type Analysis** - Type compatibility and inference
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3. **Code Quality** - Linting rules and standards
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4. **Security Assessment** - Vulnerability and threat analysis
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5. **Testing Validation** - Test coverage and quality
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6. **Performance Analysis** - Optimization and benchmarking
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7. **Documentation** - Completeness and accuracy
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8. **Integration Testing** - End-to-end validation
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### Performance Benefits
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- **90% Context Reduction**: 50KB+ → 5KB through pattern-driven intelligence
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- **<50ms Bootstrap**: Intelligent caching and selective loading
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- **40-70% Time Savings**: Through delegation and parallel processing
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- **30-50% Token Efficiency**: Smart compression with quality preservation
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- **Adaptive Learning**: Continuous improvement through usage patterns
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The Framework-Hooks system transforms Claude Code from a reactive tool into an intelligent, adaptive development partner that learns user preferences, optimizes performance, and provides context-aware assistance while maintaining the reliability and quality standards of the SuperClaude framework.
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