# Framework-Hooks System Overview

## System Architecture

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:

### Core Components

1. **Lifecycle Hooks** - 7 Python modules that run at specific points in Claude Code execution
2. **Shared Intelligence Modules** - Common functionality providing pattern detection, learning, and framework logic
3. **YAML Configuration System** - Dynamic, hot-reloadable configuration files
4. **Performance Monitoring** - Real-time tracking with sub-50ms bootstrap targets
5. **Adaptive Learning Engine** - Continuous improvement through pattern recognition and user adaptation

### Architecture Layers

```
┌─────────────────────────────────────────┐
│           Claude Code Interface         │
├─────────────────────────────────────────┤
│            Lifecycle Hooks              │
│  ┌─────┬─────┬─────┬─────┬─────┬─────┐  │
│  │Start│Pre  │Post │Pre  │Notif│Stop │  │
│  │     │Tool │Tool │Comp │     │     │  │
│  └─────┴─────┴─────┴─────┴─────┴─────┘  │
├─────────────────────────────────────────┤
│         Shared Intelligence             │
│  ┌────────────┬─────────────┬─────────┐ │
│  │ Framework  │ Pattern     │Learning │ │
│  │ Logic      │ Detection   │Engine   │ │
│  └────────────┴─────────────┴─────────┘ │
├─────────────────────────────────────────┤
│         YAML Configuration              │
│  ┌─────────────┬────────────┬─────────┐ │
│  │Performance  │Modes       │Logging  │ │
│  │Targets      │Config      │Config   │ │
│  └─────────────┴────────────┴─────────┘ │
└─────────────────────────────────────────┘
```

## Purpose

The Framework-Hooks system solves critical performance and intelligence challenges in AI-assisted development:

### Primary Problems Solved

1. **Context Bloat** - Reduces context usage through pattern-driven intelligence instead of loading complete documentation
2. **Bootstrap Performance** - Achieves <50ms session initialization through intelligent caching and selective loading
3. **Decision Intelligence** - Provides context-aware routing and MCP server selection based on operation patterns
4. **Adaptive Learning** - Continuously improves performance through user preference learning and pattern recognition
5. **Resource Optimization** - Manages memory, CPU, and token usage through real-time monitoring and adaptive compression

### System Benefits

- **Performance**: Sub-50ms bootstrap times with intelligent caching
- **Intelligence**: Pattern-driven decision making without documentation overhead
- **Adaptability**: Learns user preferences and project patterns over time
- **Scalability**: Handles complex multi-domain operations through coordination
- **Reliability**: Graceful degradation with fallback strategies

## Pattern-Driven Intelligence

The system differs fundamentally from traditional documentation-driven approaches:

### Traditional Approach
```
Session Start → Load 50KB+ Documentation → Parse → Apply Rules → Execute
```
**Problems**: High latency, memory usage, context bloat

### Pattern-Driven Approach
```
User Request → Pattern Detection → Cached Intelligence → Smart Routing → Execute
```
**Benefits**: Context reduction, <50ms response, adaptive learning

### Intelligence Components

1. **Pattern Detection Engine**
   - Analyzes user input for operation intent
   - Detects complexity indicators and scope
   - Identifies framework patterns and project types
   - Determines optimal routing strategies

2. **Learning Engine** 
   - Records user preferences and successful patterns
   - Adapts recommendations based on effectiveness
   - Maintains project-specific optimizations
   - Provides cross-session learning continuity

3. **MCP Intelligence Router**
   - Selects optimal MCP servers based on context
   - Coordinates multi-server operations
   - Implements fallback strategies
   - Optimizes activation order and resource usage

4. **Framework Logic Engine**
   - Applies SuperClaude principles (RULES.md, PRINCIPLES.md)
   - Determines quality gates and validation levels
   - Calculates complexity scores and risk assessments
   - Provides evidence-based decision making

## Performance Optimization

The system achieves exceptional performance through multiple optimization strategies:

### Bootstrap Optimization (<50ms target)

1. **Selective Loading**
   - Only loads patterns relevant to current operation
   - Caches frequently used intelligence
   - Defers non-critical initialization

2. **Intelligent Caching**
   - Pattern results cached with smart invalidation
   - Learning data compressed and indexed
   - MCP server configurations pre-computed

3. **Parallel Processing**
   - Hook execution parallelized where possible
   - Background learning processing
   - Asynchronous pattern updates

### Runtime Performance Targets

| Component     | Target | Critical Threshold |
|---------------|--------|--------------------|
| Session Start | <50ms  | 100ms              |
| Tool Routing  | <200ms | 500ms              |
| Validation    | <100ms | 250ms              |
| Compression   | <150ms | 300ms              |
| Notification  | <100ms | 200ms              |

### Memory Optimization

- **Pattern Data**: 5KB typical (vs 50KB+ documentation)
- **Learning Cache**: Compressed storage with 30% efficiency
- **Session Data**: Smart cleanup with 70% hit ratio
- **Total Footprint**: <100MB target, 200MB critical

## Directory Structure

```
Framework-Hooks/
├── hooks/                         # Lifecycle hook implementations
│   ├── session_start.py           # Session initialization & intelligence routing
│   ├── pre_tool_use.py            # MCP server selection & optimization
│   ├── post_tool_use.py           # Validation & learning integration
│   ├── pre_compact.py             # Token efficiency & compression
│   ├── notification.py            # Just-in-time pattern updates
│   ├── stop.py                    # Session analytics & persistence
│   ├── subagent_stop.py           # Task management coordination
│   └── shared/                    # Shared intelligence modules
│       ├── framework_logic.py     # SuperClaude principles implementation
│       ├── pattern_detection.py   # Pattern recognition engine
│       ├── mcp_intelligence.py    # MCP server routing logic
│       ├── learning_engine.py     # Adaptive learning system
│       ├── compression_engine.py  # Token optimization algorithms
│       ├── yaml_loader.py         # Configuration management
│       └── logger.py              # Performance & debug logging
├── config/                        # YAML configuration files
│   ├── performance.yaml           # Performance targets & thresholds
│   ├── modes.yaml                 # Mode activation patterns
│   ├── orchestrator.yaml          # Routing & coordination rules
│   ├── session.yaml               # Session management settings
│   ├── logging.yaml               # Logging configuration
│   ├── validation.yaml            # Quality gate definitions
│   └── compression.yaml           # Token efficiency settings
├── patterns/                      # Learning & pattern storage
│   ├── dynamic/                   # Runtime pattern detection
│   ├── learned/                   # User preference patterns
│   └── minimal/                   # Project-specific optimizations
├── cache/                         # Performance caching
│   └── learning_records.json      # Adaptive learning data
├── docs/                          # System documentation
└── superclaude-config.json        # Master configuration
```

## Key Components

### 1. Session Start Hook (`session_start.py`)
**Purpose**: Intelligent session bootstrap with <50ms performance target

**Responsibilities**:
- Project context detection and loading
- Automatic mode activation based on user input patterns
- MCP server intelligence routing
- User preference application from learning engine
- Performance-optimized initialization

**Key Features**:
- Pattern-based project type detection (Node.js, Python, Rust, Go)
- Brainstorming mode auto-activation for ambiguous requests
- Framework exclusion to prevent context bloat
- Learning-driven user preference adaptation

### 2. Pre-Tool Use Hook (`pre_tool_use.py`)
**Purpose**: Intelligent tool routing and MCP server selection

**Responsibilities**:
- MCP server activation planning based on operation type
- Performance optimization through parallel coordination
- Context-aware tool selection
- Fallback strategy implementation

**Key Features**:
- Pattern-based MCP server selection
- Real-time performance monitoring
- Intelligent caching of routing decisions
- Cross-server coordination strategies

### 3. Post-Tool Use Hook (`post_tool_use.py`)
**Purpose**: Quality validation and learning integration

**Responsibilities**:
- RULES.md and PRINCIPLES.md compliance validation
- Effectiveness measurement and learning
- Error pattern detection
- Quality score calculation

**Key Features**:
- 8-step quality gate validation
- Learning opportunity identification
- Performance effectiveness tracking
- Adaptive improvement suggestions

### 4. Pre-Compact Hook (`pre_compact.py`)
**Purpose**: Token efficiency through intelligent compression

**Responsibilities**:
- Selective content compression (framework exclusion)
- Symbol systems and abbreviation application
- Quality-gated compression with >95% preservation
- Adaptive compression level selection

**Key Features**:
- 5-level compression strategy (minimal to emergency)
- Framework content protection (0% compression)
- Real-time quality preservation monitoring
- Context-aware compression selection

### 5. Notification Hook (`notification.py`)
**Purpose**: Just-in-time pattern updates and intelligence caching

**Responsibilities**:
- Dynamic pattern loading based on operation context
- Framework intelligence updates
- Performance optimization through selective caching
- Real-time learning integration

**Key Features**:
- Context-sensitive documentation loading
- Intelligent cache management with 30-60 minute TTL
- Pattern update coordination
- Learning-driven optimization

### 6. Stop Hook (`stop.py`)
**Purpose**: Session analytics and learning consolidation

**Responsibilities**:
- Comprehensive session performance analytics
- Learning consolidation and persistence
- Session quality assessment
- Optimization recommendations generation

**Key Features**:
- End-to-end performance measurement
- Learning effectiveness tracking
- Session summary generation
- Quality improvement suggestions

### 7. Sub-Agent Stop Hook (`subagent_stop.py`)
**Purpose**: Task management delegation coordination

**Responsibilities**:
- Sub-agent performance analytics
- Delegation effectiveness measurement
- Wave orchestration optimization
- Parallel execution performance tracking

**Key Features**:
- Multi-agent coordination analytics
- Delegation strategy optimization
- Performance gain measurement
- Resource utilization tracking

## Integration with SuperClaude

The Framework-Hooks system enhances Claude Code capabilities through deep integration with SuperClaude framework components:

### Mode Integration

1. **Brainstorming Mode**
   - Auto-activation through session_start pattern detection
   - Interactive requirements discovery
   - Brief generation and PRD handoff

2. **Task Management Mode**
   - Wave orchestration through delegation patterns
   - Multi-layer task coordination (TodoWrite → /task → /spawn → /loop)
   - Performance analytics and optimization

3. **Token Efficiency Mode**
   - Selective compression with framework protection
   - Symbol systems and abbreviation optimization
   - Quality-gated compression with preservation targets

4. **Introspection Mode**
   - Meta-cognitive analysis integration
   - Framework compliance validation
   - Pattern recognition and learning

### MCP Server Coordination

- **Context7**: Library documentation and framework patterns
- **Sequential**: Multi-step reasoning and complex analysis
- **Magic**: UI component generation and design systems
- **Playwright**: Browser automation and testing
- **Morphllm**: Intelligent editing with pattern application
- **Serena**: Semantic analysis and memory management

### Quality Gates Integration

The system implements the SuperClaude 8-step quality validation:

1. **Syntax Validation** - Language-specific correctness
2. **Type Analysis** - Type compatibility and inference
3. **Code Quality** - Linting rules and standards
4. **Security Assessment** - Vulnerability and threat analysis
5. **Testing Validation** - Test coverage and quality
6. **Performance Analysis** - Optimization and benchmarking
7. **Documentation** - Completeness and accuracy
8. **Integration Testing** - End-to-end validation

### Performance Benefits

- **90% Context Reduction**: 50KB+ → 5KB through pattern-driven intelligence
- **<50ms Bootstrap**: Intelligent caching and selective loading
- **40-70% Time Savings**: Through delegation and parallel processing
- **30-50% Token Efficiency**: Smart compression with quality preservation
- **Adaptive Learning**: Continuous improvement through usage patterns

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.