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:

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:

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:

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

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

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

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.

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:

# 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)

{
  "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

# 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:

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:

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

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:

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:

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.