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

Session Start Hook Technical Documentation

Purpose

The session_start hook initializes Claude Code sessions with SuperClaude framework intelligence. It analyzes project context, detects patterns, and configures appropriate modes and MCP servers based on the actual session requirements.

Core Implementation: A 704-line Python implementation that performs lazy loading, pattern detection, MCP intelligence routing, compression configuration, and learning adaptations with a target execution time of <50ms.

Execution Context

The session_start hook runs automatically at the beginning of every Claude Code session. According to settings.json, it has a 10-second timeout and executes via: python3 ~/.claude/hooks/session_start.py

Actual Execution Flow:

1. Receives session data from Claude Code via stdin (JSON)
2. Initializes SessionStartHook class with lazy loading of components
3. Processes session initialization with project analysis and pattern detection
4. Outputs enhanced session configuration via stdout (JSON)
5. Falls back gracefully on errors with basic session configuration

Performance: Target <50ms execution time (configurable via superclaude-config.json)

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. Project Structure Analysis

Implementation: The _analyze_project_structure() method performs quick project analysis by examining key files and directories.

What it actually does:

• Enumerates up to 100 files for performance (limits via files[:100])
• Detects project type by checking for manifest files:
  • package.json → nodejs
  • pyproject.toml or setup.py → python
  • Cargo.toml → rust
  • go.mod → go
• Identifies frameworks by parsing package.json dependencies (React, Vue, Angular, Express)
• Checks for test directories and production indicators (Dockerfile, .env.production)
• Returns analysis dict with project_type, framework_detected, has_tests, is_production, etc.

2. User Intent Analysis and Mode Detection

Implementation: The _analyze_user_intent() method examines user input to determine operation type and complexity.

What it actually does:

• Analyzes user input text for operation keywords:
  • "build", "create", "implement" → BUILD operation (complexity +0.3)
  • "fix", "debug", "troubleshoot" → ANALYZE operation (complexity +0.2)
  • "refactor", "restructure" → REFACTOR operation (complexity +0.4)
  • "test", "validate" → TEST operation (complexity +0.1)
• Detects brainstorming needs via keywords: "not sure", "thinking about", "maybe", "brainstorm"
• Calculates complexity score (0.0-1.0) based on operation type and complexity indicators
• The _activate_intelligent_modes() method activates modes based on detected patterns:
  • brainstorming mode if brainstorming_likely is True
  • task_management mode if recommended by pattern detection
  • token_efficiency mode if recommended by pattern detection

3. MCP Server Configuration

Implementation: The _create_mcp_activation_plan() and _configure_mcp_servers() methods determine which MCP servers to activate.

What it actually does:

• Uses MCPIntelligence class to create activation plans based on:
  • User intent analysis
  • Context characteristics (file count, complexity score, operation type)
  • Project analysis results
• Returns MCP plan with:
  • servers_to_activate: List of servers to enable
  • activation_order: Sequence for server activation
  • coordination_strategy: How servers should work together
  • estimated_cost_ms: Performance impact estimate
  • fallback_strategy: Backup plan if servers fail

4. Learning Engine Integration

Implementation: The _apply_learning_adaptations() method applies learned patterns to improve session configuration.

What it actually does:

• Uses LearningEngine (initialized with ~/.claude/cache directory) to:
  • Apply previous adaptations to current recommendations
  • Store user preferences (preferred tools per operation type)
  • Update project-specific information (project type, framework)
  • Record learning events for future sessions
• The _record_session_learning() method stores session initialization patterns for continuous improvement

5. Lazy Loading Architecture

Implementation: The hook uses lazy loading via Python properties to minimize initialization time.

What it actually does:

• Core components are loaded immediately: FrameworkLogic()
• Other components use lazy loading properties:
  • pattern_detector property loads PatternDetector() only when first accessed
  • mcp_intelligence property loads MCPIntelligence() only when needed
  • compression_engine property loads CompressionEngine() only when used
  • learning_engine property loads LearningEngine() only when required
• This reduces initialization overhead and improves the <50ms performance target

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: Provides SuperClaude framework decision-making capabilities.

Used in session_start.py:

• FrameworkLogic class for quality gate configuration
• OperationContext dataclass for structured context management
• OperationType enum for operation classification (READ, WRITE, BUILD, etc.)
• RiskLevel enum for risk assessment
• Used in _configure_quality_gates() method to determine appropriate quality gates based on operation context

pattern_detection.py

Purpose: Analyzes patterns in user input and context for intelligent routing.

Used in session_start.py:

• PatternDetector class (lazy loaded)
• detect_patterns() method for analyzing user intent, context, and operation data
• Returns pattern matches, recommended modes, recommended MCP servers, and confidence scores

mcp_intelligence.py

Purpose: Provides MCP server activation planning and coordination strategies.

Used in session_start.py:

• MCPIntelligence class (lazy loaded)
• create_activation_plan() method for determining optimal MCP server coordination
• Returns activation plans with servers, order, cost estimates, and coordination strategies

compression_engine.py

Purpose: Handles compression strategy selection for token efficiency.

Used in session_start.py:

• CompressionEngine class (lazy loaded)
• determine_compression_level() method for context-based compression decisions
• Used in _configure_compression() to set session compression strategy

learning_engine.py

Purpose: Provides learning and adaptation capabilities for continuous improvement.

Used in session_start.py:

• LearningEngine class (lazy loaded, initialized with ~/.claude/cache directory)
• apply_adaptations() method for applying learned patterns
• record_learning_event() method for storing session initialization data
• update_project_info() and preference tracking methods

yaml_loader.py

Purpose: Configuration loading with fallback strategies.

Used in session_start.py:

• config_loader.get_hook_config() for hook-specific configuration
• config_loader.load_config() for YAML configuration files with FileNotFoundError handling
• Fallback to hook configuration when YAML files are missing

logger.py

Purpose: Structured logging for hook execution tracking.

Used in session_start.py:

• log_hook_start() and log_hook_end() for execution timing
• log_decision() for mode activation and MCP server selection decisions
• log_error() for error context preservation

Error Handling

Implementation: The main initialize_session() method includes comprehensive error handling with graceful fallback.

What actually happens on errors:

1. Exception Handling: All errors are caught in the main try-except block
2. Error Logging: Errors are logged via log_error() with context
3. Fallback Configuration: _create_fallback_session_config() returns:

   {
       'session_id': session_context.get('session_id', 'unknown'),
       'superclaude_enabled': False,
       'fallback_mode': True,
       'error': error,
       'basic_config': {
           'compression_level': 'minimal',
           'mcp_servers_enabled': False,
           'learning_disabled': True
       }
   }
   

4. Session Continuity: Basic Claude Code functionality is preserved even when SuperClaude features fail

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.