SuperClaude/Framework-Hooks/docs/Hooks/session_start.md

# Session Start Hook Technical Documentation

## Purpose

The session_start hook is the foundational intelligence layer of the SuperClaude-Lite framework that initializes every Claude Code session with intelligent, context-aware configuration. This hook transforms basic Claude Code sessions into SuperClaude-powered experiences by implementing comprehensive project analysis, intelligent mode detection, and optimized MCP server routing.

The hook serves as the entry point for SuperClaude's session lifecycle pattern, establishing the groundwork for all subsequent intelligent behaviors including adaptive learning, performance optimization, and context preservation across sessions.

## Execution Context

The session_start hook executes at the very beginning of every Claude Code session, before any user interactions or tool executions occur. It sits at the critical initialization phase where session context is established and intelligence systems are activated.

**Execution Flow Position:**
```
Claude Code Session Start → session_start Hook → Enhanced Session Configuration → User Interaction
```

**Lifecycle Integration:**
- **Trigger**: Every new Claude Code session initialization
- **Duration**: Target <50ms execution time
- **Dependencies**: Session context data from Claude Code
- **Output**: Enhanced session configuration with SuperClaude intelligence
- **Next Phase**: Active session with intelligent routing and optimization

## Performance Target

**Target: <50ms execution time**

This aggressive performance target is critical for maintaining seamless user experience during session initialization. The hook must complete its comprehensive analysis and configuration within this window to avoid perceptible delays.

**Why 50ms Matters:**
- **User Experience**: Sub-perceptible delay maintains natural interaction flow
- **Session Efficiency**: Fast bootstrap enables immediate intelligent behavior
- **Resource Optimization**: Efficient initialization preserves compute budget for actual work
- **Learning System**: Quick analysis allows for real-time adaptation without latency

**Performance Monitoring:**
- Real-time execution time tracking with detailed metrics
- Efficiency score calculation based on target achievement
- Performance degradation alerts and optimization recommendations
- Historical performance analysis for continuous improvement

## Core Features

### 1. Smart Project Context Loading with Framework Exclusion

**Implementation**: The hook performs intelligent project structure analysis while implementing selective content loading to optimize performance and focus.

**Technical Details:**
- **Rapid Project Scanning**: Limited file enumeration (max 100 files) for performance
- **Technology Stack Detection**: Identifies Node.js, Python, Rust, Go projects via manifest files
- **Framework Recognition**: Detects React, Vue, Angular, Express through dependency analysis
- **Production Environment Detection**: Identifies deployment configurations and CI/CD setup
- **Test Infrastructure Analysis**: Locates test directories and testing frameworks
- **Framework Exclusion Strategy**: Completely excludes SuperClaude framework directories from analysis to prevent recursive processing

**Code Implementation:**
```python
def _analyze_project_structure(self, project_path: Path) -> dict:
    # Quick enumeration with performance limit
    files = list(project_path.rglob('*'))[:100]
    
    # Technology detection via manifest files
    if (project_path / 'package.json').exists():
        analysis['project_type'] = 'nodejs'
        # Framework detection through dependency analysis
        with open(package_json) as f:
            deps = {**pkg_data.get('dependencies', {}), **pkg_data.get('devDependencies', {})}
            if 'react' in deps: analysis['framework_detected'] = 'react'
```

### 2. Automatic Mode Detection and Activation

**Implementation**: Uses pattern recognition algorithms to detect user intent and automatically activate appropriate SuperClaude behavioral modes.

**Detection Algorithms:**
- **Intent Analysis**: Natural language processing of user input for operation type detection
- **Complexity Scoring**: Multi-factor analysis including file count, operation type, and complexity indicators
- **Brainstorming Detection**: Identifies uncertainty indicators ("not sure", "maybe", "thinking about")
- **Task Management Triggers**: Detects multi-step operations and delegation opportunities
- **Token Efficiency Needs**: Identifies resource constraints and optimization requirements

**Mode Activation Logic:**
```python
def _activate_intelligent_modes(self, context: dict, recommendations: dict) -> list:
    activated_modes = []
    
    # Brainstorming mode activation
    if context.get('brainstorming_likely', False):
        activated_modes.append({'name': 'brainstorming', 'trigger': 'user input'})
    
    # Task management mode activation
    if 'task_management' in recommendations.get('recommended_modes', []):
        activated_modes.append({'name': 'task_management', 'trigger': 'pattern detection'})
```

### 3. MCP Server Intelligence Routing

**Implementation**: Intelligent analysis of project context and user intent to determine optimal MCP server activation strategy.

**Routing Intelligence:**
- **Context-Aware Selection**: Matches MCP server capabilities to detected project needs
- **Performance Optimization**: Considers server resource profiles and coordination costs
- **Fallback Strategy Planning**: Establishes backup activation patterns for server failures
- **Coordination Strategy**: Determines optimal server interaction patterns (parallel vs sequential)

**Server Selection Matrix:**
- **Context7**: Activated for external library dependencies and framework integration needs
- **Sequential**: Enabled for complex analysis requirements and multi-step reasoning
- **Magic**: Triggered by UI component requests and design system needs
- **Playwright**: Activated for testing requirements and browser automation
- **Morphllm**: Enabled for pattern-based editing and token optimization scenarios
- **Serena**: Activated for semantic analysis and project memory management

### 4. User Preference Adaptation

**Implementation**: Applies machine learning-based adaptations from previous sessions to personalize the session configuration.

**Learning Integration:**
- **Historical Pattern Analysis**: Analyzes successful configurations from previous sessions
- **User Expertise Detection**: Infers user skill level from interaction patterns and terminology
- **Preference Extraction**: Identifies consistent user choices and optimization preferences
- **Adaptive Configuration**: Applies learned preferences to current session setup

**Learning Engine Integration:**
```python
def _apply_learning_adaptations(self, context: dict, detection_result: dict) -> dict:
    enhanced_recommendations = self.learning_engine.apply_adaptations(
        context, base_recommendations
    )
    return enhanced_recommendations
```

### 5. Performance-Optimized Initialization

**Implementation**: Comprehensive performance optimization strategy that balances intelligence with speed.

**Optimization Techniques:**
- **Lazy Loading**: Defers non-critical analysis until actual usage
- **Intelligent Caching**: Reuses previous analysis results when project context unchanged
- **Parallel Processing**: Concurrent execution of independent analysis components
- **Resource-Aware Configuration**: Adapts initialization depth based on available resources
- **Progressive Enhancement**: Enables additional features as resource budget allows

## Implementation Details

### Architecture Pattern

The session_start hook implements a layered architecture with clear separation of concerns:

**Layer 1: Context Extraction**
```python
def _extract_session_context(self, session_data: dict) -> dict:
    # Enriches basic session data with project analysis and user intent detection
    context = {
        'session_id': session_data.get('session_id', 'unknown'),
        'project_path': session_data.get('project_path', ''),
        'user_input': session_data.get('user_input', ''),
        # ... additional context enrichment
    }
```

**Layer 2: Intelligence Analysis**
```python
def _detect_session_patterns(self, context: dict) -> dict:
    # Pattern detection using SuperClaude's pattern recognition algorithms
    detection_result = self.pattern_detector.detect_patterns(
        context.get('user_input', ''),
        context,
        operation_data
    )
```

**Layer 3: Configuration Generation**
```python
def _generate_session_config(self, context: dict, recommendations: dict, 
                           mcp_plan: dict, compression_config: dict) -> dict:
    # Comprehensive session configuration assembly
    return comprehensive_session_configuration
```

### Error Handling Strategy

**Graceful Degradation**: The hook implements comprehensive error handling that ensures session functionality even when intelligence systems fail.

```python
def initialize_session(self, session_context: dict) -> dict:
    try:
        # Full intelligence initialization
        return enhanced_session_config
    except Exception as e:
        # Graceful fallback
        return self._create_fallback_session_config(session_context, str(e))
```

**Fallback Configuration:**
- Disables SuperClaude intelligence features
- Maintains basic Claude Code functionality
- Provides error context for debugging
- Enables recovery for subsequent sessions

### Performance Measurement

**Real-Time Metrics:**
```python
# Performance tracking integration
execution_time = (time.time() - start_time) * 1000
session_config['performance_metrics'] = {
    'initialization_time_ms': execution_time,
    'target_met': execution_time < self.performance_target_ms,
    'efficiency_score': self._calculate_initialization_efficiency(execution_time)
}
```

## Configuration

### Hook-Specific Configuration (superclaude-config.json)

```json
{
  "hook_configurations": {
    "session_start": {
      "enabled": true,
      "description": "SESSION_LIFECYCLE + FLAGS logic with intelligent bootstrap",
      "performance_target_ms": 50,
      "features": [
        "smart_project_context_loading",
        "automatic_mode_detection",
        "mcp_server_intelligence_routing",
        "user_preference_adaptation",
        "performance_optimized_initialization"
      ],
      "configuration": {
        "auto_project_detection": true,
        "framework_exclusion_enabled": true,
        "intelligence_activation": true,
        "learning_integration": true,
        "performance_monitoring": true
      },
      "error_handling": {
        "graceful_fallback": true,
        "preserve_user_context": true,
        "error_learning": true
      }
    }
  }
}
```

### Configuration Loading Strategy

**Primary Configuration Source**: superclaude-config.json hook_configurations.session_start
**Fallback Strategy**: YAML configuration files in config/ directory
**Runtime Adaptation**: Learning engine modifications applied during execution

```python
# Configuration loading with fallback
self.hook_config = config_loader.get_hook_config('session_start')

try:
    self.session_config = config_loader.load_config('session')
except FileNotFoundError:
    self.session_config = self.hook_config.get('configuration', {})
```

## Pattern Loading Strategy

### Minimal Pattern Bootstrap

The hook implements a strategic pattern loading approach that loads only essential patterns during initialization to meet the 50ms performance target.

**Pattern Loading Phases:**

**Phase 1: Critical Patterns (Target: 3-5KB)**
- Core operation type detection patterns
- Basic project structure recognition
- Essential mode activation triggers
- Primary MCP server routing logic

**Phase 2: Context-Specific Patterns (Lazy Loaded)**
- Framework-specific intelligence patterns
- Advanced optimization strategies
- Historical learning adaptations
- Complex coordination algorithms

**Implementation Strategy:**
```python
def _detect_session_patterns(self, context: dict) -> dict:
    # Load minimal patterns for fast detection
    detection_result = self.pattern_detector.detect_patterns(
        context.get('user_input', ''),
        context,
        operation_data  # Contains only essential pattern data
    )
```

**Pattern Optimization Techniques:**
- **Compressed Pattern Storage**: Use efficient data structures for pattern representation
- **Selective Pattern Loading**: Load only patterns relevant to detected project type
- **Cached Pattern Results**: Reuse pattern analysis for similar contexts
- **Progressive Pattern Enhancement**: Enable additional patterns as session progresses

## Shared Modules Used

### framework_logic.py

**Purpose**: Implements core SuperClaude decision-making algorithms from RULES.md, PRINCIPLES.md, and ORCHESTRATOR.md.

**Key Components Used:**
- `OperationType` enum for operation classification
- `OperationContext` dataclass for structured context management
- `RiskLevel` assessment for quality gate determination
- Quality gate configuration based on operation context

**Usage in session_start:**
```python
from framework_logic import FrameworkLogic, OperationContext, OperationType, RiskLevel

# Quality gate configuration
operation_context = OperationContext(
    operation_type=context.get('operation_type', OperationType.READ),
    file_count=context.get('file_count_estimate', 1),
    complexity_score=context.get('complexity_score', 0.0),
    risk_level=RiskLevel.LOW
)
return self.framework_logic.get_quality_gates(operation_context)
```

### pattern_detection.py

**Purpose**: Provides intelligent pattern recognition for session configuration.

**Key Components Used:**
- Pattern matching algorithms for user intent detection
- Mode recommendation logic based on detected patterns
- MCP server selection recommendations
- Confidence scoring for pattern matches

### mcp_intelligence.py

**Purpose**: Implements intelligent MCP server selection and coordination.

**Key Components Used:**
- MCP activation plan generation
- Server coordination strategy determination
- Performance cost estimation
- Fallback strategy planning

### compression_engine.py

**Purpose**: Provides intelligent compression strategy selection for token efficiency.

**Key Components Used:**
- Compression level determination based on context
- Quality impact estimation
- Compression savings calculation
- Selective compression configuration

### learning_engine.py

**Purpose**: Enables adaptive learning and preference application.

**Key Components Used:**
- Learning event recording for session patterns
- Adaptation application from previous sessions
- Effectiveness measurement and feedback loops
- Pattern recognition and improvement suggestions

### yaml_loader.py

**Purpose**: Provides configuration loading and management capabilities.

**Key Components Used:**
- Hook-specific configuration loading
- YAML configuration file management
- Fallback configuration strategies
- Hot-reload configuration support

### logger.py

**Purpose**: Provides comprehensive logging and metrics collection.

**Key Components Used:**
- Hook execution logging with timing
- Decision logging for audit trails
- Error logging with context preservation
- Performance metrics collection

## Error Handling

### Comprehensive Error Recovery Strategy

**Error Categories and Responses:**

**1. Project Analysis Failures**
```python
def _analyze_project_structure(self, project_path: Path) -> dict:
    try:
        # Full project analysis
        return comprehensive_analysis
    except Exception:
        # Return partial analysis with safe defaults
        return basic_analysis_with_defaults
```

**2. Pattern Detection Failures**
- Fallback to basic mode configuration
- Use cached patterns from previous sessions
- Apply conservative intelligence settings
- Maintain core functionality without advanced features

**3. MCP Server Planning Failures**
- Disable problematic servers
- Use fallback server combinations
- Apply conservative coordination strategies
- Maintain basic tool functionality

**4. Learning System Failures**
- Disable adaptive features temporarily
- Use static configuration defaults
- Log errors for future analysis
- Preserve session functionality

### Error Learning Integration

**Error Pattern Recognition:**
```python
def _record_session_learning(self, context: dict, session_config: dict):
    self.learning_engine.record_learning_event(
        LearningType.OPERATION_PATTERN,
        AdaptationScope.USER,
        context,
        session_config,
        success_score,
        confidence_score,
        metadata
    )
```

**Recovery Optimization:**
- Errors are analyzed for pattern recognition
- Successful recovery strategies are learned and applied
- Error frequency analysis drives system improvements
- Proactive error prevention based on historical patterns

## Session Context Enhancement

### Context Enrichment Process

The session_start hook transforms basic Claude Code session data into rich, intelligent context that enables advanced SuperClaude behaviors throughout the session.

**Input Context (Basic):**
- session_id: Basic session identifier
- project_path: File system path
- user_input: Initial user request
- conversation_length: Basic metrics

**Enhanced Context (SuperClaude):**
- Project analysis with technology stack detection
- User intent analysis with complexity scoring
- Mode activation recommendations
- MCP server routing plans
- Performance optimization settings
- Learning adaptations from previous sessions

### Context Preservation Strategy

**Session Configuration Generation:**
```python
def _generate_session_config(self, context: dict, recommendations: dict, 
                           mcp_plan: dict, compression_config: dict) -> dict:
    return {
        'session_id': context['session_id'],
        'superclaude_enabled': True,
        'active_modes': recommendations.get('recommended_modes', []),
        'mcp_servers': mcp_plan,
        'compression': compression_config,
        'performance': performance_config,
        'learning': learning_config,
        'context': context_preservation,
        'quality_gates': quality_gate_config
    }
```

**Context Utilization Throughout Session:**
- **MCP Server Routing**: Uses project analysis for intelligent server selection
- **Mode Activation**: Applies detected patterns for behavioral mode triggers
- **Performance Optimization**: Uses complexity analysis for resource allocation
- **Quality Gates**: Applies context-appropriate validation levels
- **Learning Integration**: Captures session patterns for future improvement

### Long-Term Context Evolution

**Cross-Session Learning:**
- Session patterns are analyzed and stored for future sessions
- User preferences are extracted and applied automatically
- Project-specific optimizations are learned and reused
- Error patterns are identified and proactively avoided

**Context Continuity:**
- Enhanced context from session_start provides foundation for entire session
- Context elements influence all subsequent hook behaviors
- Learning from current session feeds into future session_start executions
- Continuous improvement cycle maintains and enhances context quality over time

## Integration Points

### SuperClaude Framework Integration

**SESSION_LIFECYCLE.md Compliance:**
- Implements initialization phase of session lifecycle pattern
- Provides foundation for checkpoint and persistence systems
- Enables context continuity across session boundaries

**FLAGS.md Logic Implementation:**
- Automatically detects and applies appropriate flag combinations
- Implements flag precedence and conflict resolution
- Provides intelligent default flag selection based on context

**ORCHESTRATOR.md Pattern Integration:**
- Implements intelligent routing patterns for MCP server selection
- Applies resource management strategies during initialization
- Establishes foundation for quality gate enforcement

### Hook Ecosystem Coordination

**Downstream Hook Preparation:**
- pre_tool_use: Receives enhanced context for intelligent tool routing
- post_tool_use: Gets quality gate configuration for validation
- pre_compact: Receives compression configuration for optimization
- stop: Gets learning configuration for session analytics

**Cross-Hook Data Flow:**
```
session_start → Enhanced Context → All Subsequent Hooks
    ↓
Learning Engine ← Session Analytics ← stop Hook
```

This comprehensive technical documentation provides a complete understanding of how the session_start hook operates as the foundational intelligence layer of the SuperClaude-Lite framework, transforming basic Claude Code sessions into intelligent, adaptive, and optimized experiences.