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

Notification Hook Documentation

Purpose

The notification hook processes notification events from Claude Code and provides just-in-time capability loading and pattern updates. It handles various notification types to trigger appropriate SuperClaude framework responses.

Core Implementation: Responds to Claude Code notifications (errors, performance issues, tool requests, context changes) with intelligent resource loading and pattern updates to minimize context overhead.

Execution Context

The notification hook runs on notification events from Claude Code. According to settings.json, it has a 10-second timeout and executes via: python3 ~/.claude/hooks/notification.py

Notification Types Handled:

• High Priority: error, failure, security_alert, performance_issue, validation_failure
• Medium Priority: tool_request, context_change, resource_constraint
• Low Priority: info, debug, status_update

Actual Processing:

1. Receives notification event via stdin (JSON)
2. Determines notification priority and type
3. Loads appropriate capabilities or patterns on-demand
4. Updates framework intelligence based on notification context
5. Outputs response configuration via stdout (JSON)

Integration Points

• Pre-Tool Use Hook: Coordinates with tool selection intelligence
• Session Start Hook: Integrates with project context initialization
• Post-Tool Use Hook: Shares learning data for effectiveness measurement
• Stop Hook: Contributes to session analytics and learning consolidation

Performance Target

Target: <100ms execution time

The notification hook is designed for ultra-fast execution to maintain Claude Code's responsiveness:

Performance Specifications

• Primary Target: 95% of notifications processed in <100ms
• Cache Hit Target: >80% cache hit rate for repeated documentation requests
• Memory Efficiency: <50KB memory footprint per notification
• CPU Efficiency: <10% CPU utilization during peak notification processing

Performance Monitoring

performance_metrics = {
    'processing_time_ms': execution_time,
    'target_met': execution_time < 100,  # Target compliance
    'cache_hit_rate': cache_hits / total_requests,
    'memory_usage_kb': memory_footprint,
    'cpu_utilization_percent': cpu_usage
}

Performance Optimization Strategies

• Intelligent Caching: Multi-tier caching with different retention periods
• Lazy Loading: Load only essential information during notification processing
• Batch Processing: Group related intelligence updates for efficiency
• Asynchronous Operations: Non-blocking processing for low-priority notifications

Just-in-Time Loading

Core Philosophy

Instead of loading all MCP server documentation upfront (traditional approach consuming 40-60KB context), the notification hook implements demand-driven intelligence loading:

Traditional Approach:          JIT Approach:
┌─────────────────┐            ┌──────────────────┐
│  Load ALL docs  │            │ Detect need      │
│      (60KB)     │     →      │ Load specific    │
│                 │            │    (8-12KB)      │
│ 90% unused      │            │ Cache for reuse  │
└─────────────────┘            └──────────────────┘

JIT Loading Process

1. Intelligence Need Detection

def _analyze_intelligence_needs(self, context: dict) -> dict:
    needs = {
        'mcp_docs_needed': False,
        'mcp_servers': [],
        'reason': ''
    }
    
    # Detect specific capability requirements
    if context.get('tool_requests'):
        needs['mcp_docs_needed'] = True
        needs['mcp_servers'] = self._map_tools_to_servers(tools)
        needs['reason'] = 'Tool requests detected'

2. Dynamic Documentation Loading

def _load_jit_documentation(self, context: dict, analysis: dict) -> dict:
    for doc_type in analysis.get('documentation_needed', []):
        # Check cache first (30min retention)
        if doc_type in self.notification_cache:
            return cached_content
        
        # Load on-demand (8-12KB typical)
        doc_content = self._load_documentation_content(doc_type, context)
        self.notification_cache[doc_type] = doc_content
        
        return fresh_content

3. Context-Aware Loading

The hook analyzes notification context to determine exactly what intelligence is needed:

ui_component_request:
  loads: magic_patterns (UI components, design systems, accessibility)
  size: ~10KB
  cache_duration: 30min

library_integration_request:
  loads: context7_patterns (framework usage, best practices, documentation)
  size: ~12KB
  cache_duration: 30min

complex_analysis_request:
  loads: sequential_patterns (reasoning workflows, debugging strategies)
  size: ~8KB
  cache_duration: 60min

testing_request:
  loads: playwright_patterns (testing strategies, automation, performance)
  size: ~9KB
  cache_duration: 30min

Benefits of JIT Loading

Context Reduction

• Traditional: 60KB upfront documentation loading
• JIT: 8-12KB on-demand loading
• Savings: 80-90% context reduction

Performance Improvement

• Faster Initialization: No upfront documentation loading
• Reduced Memory: Only active capabilities consume memory
• Better Caching: Targeted caching of actually-used patterns

Adaptive Intelligence

• Usage-Based: Load documentation based on actual usage patterns
• Context-Sensitive: Load relevant patterns for current operation context
• Learning-Driven: Improve loading decisions based on effectiveness data

Pattern Update Mechanism

Dynamic Pattern Management

The notification hook implements real-time pattern updates based on operational context and effectiveness measurement:

1. Pattern Detection

def _update_patterns_if_needed(self, context: dict, intelligence_needs: dict) -> dict:
    pattern_updates = {
        'updated_patterns': [],
        'new_patterns_detected': [],
        'pattern_effectiveness': {}
    }
    
    # Update operation-specific patterns
    operation_type = context.get('operation_type')
    self._update_operation_patterns(operation_type, pattern_updates)
    
    # Update context-specific patterns  
    session_context = context.get('session_context', {})
    self._update_context_patterns(session_context, pattern_updates)

2. Pattern Types Updated

Operation Patterns

build_operations:
  patterns: [dependency_resolution, build_optimization, error_handling]
  update_trigger: build/implement notifications
  effectiveness_metric: build_success_rate

analysis_operations:
  patterns: [systematic_investigation, hypothesis_testing, validation]
  update_trigger: analyze/debug notifications
  effectiveness_metric: issue_resolution_rate

testing_operations:
  patterns: [test_generation, coverage_analysis, performance_testing]
  update_trigger: test/validate notifications
  effectiveness_metric: test_effectiveness_score

Context Patterns

frontend_context:
  patterns: [ui_components, responsive_design, accessibility_compliance]
  update_trigger: frontend_project_detection
  effectiveness_metric: ui_quality_score

backend_context:
  patterns: [api_design, database_optimization, security_patterns]
  update_trigger: backend_project_detection
  effectiveness_metric: api_performance_score

fullstack_context:
  patterns: [integration_patterns, deployment_strategies, monitoring]
  update_trigger: fullstack_project_detection
  effectiveness_metric: integration_success_rate

3. Pattern Effectiveness Tracking

def _record_pattern_effectiveness(self, pattern_type: str, context: dict, outcome: dict):
    effectiveness_data = {
        'pattern_type': pattern_type,
        'usage_context': context,
        'outcome_quality': outcome.get('quality_score', 0.0),
        'performance_impact': outcome.get('performance_delta', 0.0),
        'user_satisfaction': outcome.get('user_rating', 0.0),
        'timestamp': time.time()
    }
    
    self.learning_engine.record_pattern_effectiveness(effectiveness_data)

Pattern Update Process

1. Trigger Detection

• Context Change: Project type, complexity, or domain shifts
• Performance Issues: Patterns not meeting effectiveness thresholds
• Usage Patterns: Frequently used patterns need optimization
• Error Patterns: Failed operations require pattern updates

2. Pattern Analysis

• Effectiveness Measurement: Track pattern success rates and performance impact
• Usage Frequency: Identify most commonly used patterns for optimization
• Context Relevance: Ensure patterns match current operational context
• Performance Impact: Measure pattern loading and execution overhead

3. Intelligent Updates

• Selective Updates: Only update patterns that need improvement
• Context-Aware Updates: Patterns updated based on current operational context
• Performance-Optimized: Updates prioritize high-impact, frequently-used patterns
• Learning-Driven: Updates based on accumulated effectiveness data

Intelligence Caching

Multi-Tier Caching Strategy

The notification hook implements sophisticated caching to minimize repeated loading overhead:

Cache Hierarchy

L1_Cache: # In-memory, immediate access
  retention: session_duration
  capacity: 20_patterns
  access_time: <1ms
  use_case: active_patterns

L2_Cache: # Process cache, fast access  
  retention: 60_minutes
  capacity: 100_patterns
  access_time: <5ms
  use_case: recently_used_patterns

L3_Cache: # Disk cache, persistent
  retention: 24_hours
  capacity: 500_patterns
  access_time: <20ms
  use_case: historical_patterns

Caching Durations by Content Type

documentation_cache:
  duration: 30_minutes
  reason: "Documentation changes infrequently, but needs periodic refresh"
  invalidation: version_change_or_timeout

pattern_cache:
  duration: 60_minutes  
  reason: "Patterns evolve slowly, benefit from longer retention"
  invalidation: effectiveness_threshold_or_timeout

intelligence_cache:
  duration: 15_minutes
  reason: "Intelligence updates frequently, needs fresh data"
  invalidation: context_change_or_timeout

framework_cache:
  duration: 120_minutes
  reason: "Framework patterns stable, infrequent updates"
  invalidation: framework_version_change

Cache Management

class IntelligenceCache:
    def __init__(self):
        self.documentation_cache = {}  # 30min retention
        self.pattern_cache = {}        # 60min retention  
        self.intelligence_cache = {}   # 15min retention
        self.framework_cache = {}      # 120min retention
    
    def get_cached_content(self, content_type: str, key: str) -> Optional[dict]:
        cache = self._get_cache_for_type(content_type)
        entry = cache.get(key)
        
        if entry and not self._is_expired(entry):
            self._record_cache_hit(content_type, key)
            return entry['content']
        
        self._record_cache_miss(content_type, key)
        return None
    
    def cache_content(self, content_type: str, key: str, content: dict):
        cache = self._get_cache_for_type(content_type)
        retention = self._get_retention_for_type(content_type)
        
        cache[key] = {
            'content': content,
            'timestamp': time.time(),
            'retention_seconds': retention,
            'access_count': 0
        }

Cache Performance Optimization

Intelligent Prefetching

def _prefetch_likely_patterns(self, context: dict):
    """Prefetch patterns likely to be needed based on context."""
    project_type = context.get('session_context', {}).get('project_type')
    
    if project_type == 'frontend':
        self._prefetch_patterns(['magic_patterns', 'ui_optimization'])
    elif project_type == 'backend':
        self._prefetch_patterns(['context7_patterns', 'api_optimization'])

Cache Eviction Strategy

def _evict_least_effective_patterns(self):
    """Evict patterns with lowest effectiveness scores."""
    for cache in [self.documentation_cache, self.pattern_cache]:
        if len(cache) > self.max_cache_size:
            # Sort by effectiveness score and access frequency
            sorted_entries = sorted(cache.items(), 
                                  key=lambda x: (x[1].get('effectiveness', 0.0), 
                                               x[1].get('access_count', 0)))
            
            # Evict bottom 20%
            evict_count = len(cache) // 5
            for key, _ in sorted_entries[:evict_count]:
                del cache[key]

Configuration

Configuration Sources

The notification hook reads configuration from multiple sources with priority hierarchy:

1. superclaude-config.json (Primary)

{
  "hook_configurations": {
    "notification": {
      "enabled": true,
      "description": "Just-in-time MCP documentation loading and pattern updates",
      "performance_target_ms": 100,
      "features": [
        "just_in_time_documentation_loading",
        "dynamic_pattern_updates", 
        "framework_intelligence_updates",
        "real_time_learning",
        "performance_optimization_through_caching"
      ],
      "configuration": {
        "jit_documentation_loading": true,
        "pattern_updates": true,
        "intelligence_caching": true,
        "learning_integration": true,
        "performance_optimization": true
      },
      "caching": {
        "documentation_cache_minutes": 30,
        "pattern_cache_minutes": 60,
        "intelligence_cache_minutes": 15
      }
    }
  }
}

2. YAML Configuration Files (Secondary)

# config/session.yaml - Session-specific notification settings
notifications:
  enabled: true
  priority_processing: true
  batch_processing: false
  async_processing: true
  
# config/performance.yaml - Performance targets
performance:
  notification_hook:
    target_ms: 100
    cache_hit_target: 0.80
    memory_limit_kb: 50
    cpu_limit_percent: 10

3. Environment Variables (Override)

SUPERCLAUDE_NOTIFICATION_ENABLED=true
SUPERCLAUDE_NOTIFICATION_PERFORMANCE_TARGET=100
SUPERCLAUDE_NOTIFICATION_CACHE_DURATION=30
SUPERCLAUDE_NOTIFICATION_LEARNING_ENABLED=true

Configuration Loading

def __init__(self):
    # Load hook-specific configuration
    self.hook_config = config_loader.get_hook_config('notification')
    
    # Load notification configuration from session.yaml
    self.notification_config = config_loader.get_section('session', 'notifications', {})
    
    # Performance settings with fallback
    self.performance_target_ms = config_loader.get_hook_config(
        'notification', 'performance_target_ms', 100
    )
    
    # Caching configuration
    self.cache_config = self.hook_config.get('caching', {})
    self.doc_cache_minutes = self.cache_config.get('documentation_cache_minutes', 30)
    self.pattern_cache_minutes = self.cache_config.get('pattern_cache_minutes', 60)
    self.intelligence_cache_minutes = self.cache_config.get('intelligence_cache_minutes', 15)

Runtime Configuration Adaptation

def _adapt_configuration_to_context(self, context: dict):
    """Adapt configuration based on runtime context."""
    resource_usage = context.get('session_context', {}).get('resource_usage', 0)
    
    # Adjust caching for resource-constrained scenarios
    if resource_usage > 75:
        self.doc_cache_minutes = min(self.doc_cache_minutes, 15)  # Reduce cache retention
        self.performance_target_ms = 50  # Tighter performance target
        
    # Adjust for high-priority notifications
    if context.get('priority') == 'high':
        self.performance_target_ms = 50  # Faster processing for critical notifications

Dynamic Pattern Types

MCP Activation Patterns

The notification hook manages dynamic patterns for MCP server activation based on notification context:

Magic Patterns (UI/Component Generation)

activation_triggers:
  - ui_component_requests
  - design_system_queries
  - accessibility_requirements
  - responsive_design_needs

loaded_patterns:
  ui_components: [button, form, modal, card, navigation, layout]
  design_systems: [theme_tokens, spacing_system, color_palette, typography]
  accessibility: [aria_labels, keyboard_navigation, screen_readers, contrast]
  responsive: [breakpoints, fluid_layouts, mobile_first, touch_interfaces]

cache_strategy:
  duration: 30_minutes
  invalidation: design_system_updates
  prefetch: project_type_frontend

Context7 Patterns (Library/Framework Documentation)

activation_triggers:
  - library_integration_requests
  - framework_usage_queries
  - documentation_access_needs
  - best_practices_requirements

loaded_patterns:
  library_integration: [import_patterns, configuration_management, version_compatibility]
  framework_usage: [react_hooks, vue_composition, angular_services, node_modules]
  documentation_access: [api_references, code_examples, migration_guides]
  best_practices: [coding_standards, security_patterns, performance_optimization]

cache_strategy:
  duration: 60_minutes
  invalidation: library_version_updates
  prefetch: dependency_analysis

Sequential Patterns (Complex Analysis)

activation_triggers:
  - complex_analysis_requests
  - debugging_scenarios
  - systematic_investigation_needs
  - multi_step_reasoning_requirements

loaded_patterns:
  analysis_workflows: [hypothesis_formation, evidence_gathering, validation_cycles]
  debugging_strategies: [systematic_investigation, root_cause_analysis, verification]
  reasoning_patterns: [decomposition_strategies, synthesis_methods, optimization_approaches]
  problem_solving: [constraint_identification, solution_generation, trade_off_analysis]

cache_strategy:
  duration: 45_minutes
  invalidation: analysis_method_updates
  prefetch: complexity_indicators

Playwright Patterns (Testing/Automation)

activation_triggers:
  - testing_requests
  - automation_needs
  - performance_testing_requirements
  - cross_browser_validation_needs

loaded_patterns:
  testing_strategies: [unit_testing, integration_testing, e2e_testing, visual_testing]
  automation_patterns: [page_objects, test_data_management, assertion_strategies]
  performance_testing: [load_testing, stress_testing, performance_monitoring]
  browser_automation: [element_selection, event_simulation, state_management]

cache_strategy:
  duration: 30_minutes
  invalidation: testing_framework_updates
  prefetch: testing_requirements_detection

Mode Detection Patterns

Dynamic patterns for SuperClaude mode detection and activation:

Brainstorming Mode Patterns

detection_triggers:
  - vague_project_requests
  - exploration_keywords
  - uncertainty_indicators
  - interactive_discovery_needs

loaded_patterns:
  dialogue_patterns: [socratic_questioning, requirement_discovery, consensus_building]
  exploration_strategies: [possibility_analysis, constraint_identification, solution_space_mapping]
  decision_frameworks: [criteria_development, option_evaluation, consensus_formation]

activation_logic:
  confidence_threshold: 0.7
  keyword_matching: [brainstorm, explore, discuss, figure_out, not_sure]
  context_analysis: project_ambiguity_detection

Task Management Mode Patterns

detection_triggers:
  - multi_step_operations
  - complex_project_requirements
  - delegation_opportunities
  - orchestration_needs

loaded_patterns:
  orchestration_strategies: [wave_coordination, sub_agent_delegation, parallel_processing]
  task_decomposition: [epic_breakdown, story_mapping, dependency_analysis]
  resource_management: [capacity_planning, load_balancing, optimization_strategies]

activation_logic:
  complexity_threshold: 0.4
  file_count_threshold: 3
  operation_type_diversity: multiple_domains

Token Efficiency Mode Patterns

detection_triggers:
  - resource_constraints
  - context_usage_high
  - optimization_requests
  - performance_requirements

loaded_patterns:
  compression_strategies: [symbol_systems, abbreviation_frameworks, structural_optimization]
  efficiency_techniques: [selective_compression, quality_preservation, adaptive_compression]
  optimization_methods: [content_classification, priority_ranking, intelligent_caching]

activation_logic:
  resource_usage_threshold: 0.75
  context_size_threshold: large_scale_operations
  optimization_request_detection: explicit_or_implicit

Learning Integration

Real-Time Learning and Adaptation

The notification hook implements continuous learning to optimize intelligence loading decisions:

Learning Event Types

class LearningEventType(Enum):
    PATTERN_EFFECTIVENESS = "pattern_effectiveness"
    DOCUMENTATION_USAGE = "documentation_usage"
    CACHE_PERFORMANCE = "cache_performance"
    LOADING_OPTIMIZATION = "loading_optimization"
    ERROR_RECOVERY = "error_recovery"
    PERFORMANCE_OPTIMIZATION = "performance_optimization"

Learning Data Collection

def _record_notification_learning(self, context: dict, intelligence_analysis: dict):
    """Record learning events for continuous optimization."""
    
    # Error pattern learning
    if context['notification_type'] in ['error', 'failure']:
        self.learning_engine.record_learning_event(
            LearningType.ERROR_RECOVERY,
            AdaptationScope.USER,
            context,
            {
                'error_type': context.get('error_type'),
                'recovery_strategy': intelligence_analysis.get('recovery_patterns'),
                'intelligence_loaded': len(intelligence_analysis.get('documentation_needed', [])),
                'recovery_success': context.get('recovery_outcome', False)
            },
            learning_value=0.7,  # High value from error experiences
            confidence=0.8,
            metadata={'hook': 'notification', 'learning_type': 'error_recovery'}
        )
    
    # Success pattern learning
    elif context['notification_type'] in ['success', 'completion']:
        self.learning_engine.record_learning_event(
            LearningType.OPERATION_PATTERN,
            AdaptationScope.USER,
            context,
            {
                'operation_type': context.get('operation_type'),
                'patterns_used': intelligence_analysis.get('patterns_applied'),
                'effectiveness_score': context.get('effectiveness_score', 0.0),
                'performance_impact': context.get('performance_delta', 0.0)
            },
            learning_value=0.9,  # Very high value from successful operations
            confidence=0.9,
            metadata={'hook': 'notification', 'learning_type': 'success_pattern'}
        )

Adaptive Intelligence Loading

def _adapt_loading_strategy(self, context: dict, historical_data: dict) -> dict:
    """Adapt loading strategy based on learning data."""
    
    # Analyze historical effectiveness
    pattern_effectiveness = historical_data.get('pattern_effectiveness', {})
    usage_frequency = historical_data.get('usage_frequency', {})
    performance_impact = historical_data.get('performance_impact', {})
    
    # Prioritize high-effectiveness, frequently-used patterns
    loading_priority = {}
    for pattern_type, effectiveness in pattern_effectiveness.items():
        frequency = usage_frequency.get(pattern_type, 0.0)
        performance = performance_impact.get(pattern_type, 1.0)  # Lower is better
        
        # Calculate composite priority score
        priority_score = (effectiveness * 0.4) + (frequency * 0.4) + ((1.0 - performance) * 0.2)
        loading_priority[pattern_type] = priority_score
    
    # Adapt loading strategy
    adaptation_strategy = {
        'high_priority_patterns': [p for p, s in loading_priority.items() if s > 0.7],
        'prefetch_patterns': [p for p, s in loading_priority.items() if s > 0.5],
        'lazy_load_patterns': [p for p, s in loading_priority.items() if s < 0.3],
        'cache_duration_adjustment': self._calculate_optimal_cache_duration(historical_data)
    }
    
    return adaptation_strategy

Learning-Driven Optimization

def _optimize_based_on_learning(self, learning_insights: dict):
    """Apply learning insights to optimize notification processing."""
    
    # Optimize caching strategy
    if learning_insights.get('cache_miss_rate_high'):
        # Increase cache retention for frequently accessed patterns
        self.doc_cache_minutes *= 1.5
        self.pattern_cache_minutes *= 1.2
        
    # Optimize loading strategy
    if learning_insights.get('loading_overhead_high'):
        # Implement more aggressive prefetching
        self.prefetch_threshold = 0.3  # Lower threshold for prefetching
        
    # Optimize pattern selection
    if learning_insights.get('pattern_effectiveness_low'):
        # Focus on high-effectiveness patterns
        self.effectiveness_threshold = 0.7  # Higher threshold for pattern loading

Cross-Session Learning

Learning Data Persistence

def _persist_learning_insights(self, session_id: str, insights: dict):
    """Persist learning insights across sessions."""
    learning_record = {
        'session_id': session_id,
        'timestamp': time.time(),
        'notification_patterns': insights.get('notification_patterns', {}),
        'loading_effectiveness': insights.get('loading_effectiveness', {}),
        'cache_performance': insights.get('cache_performance', {}),
        'optimization_opportunities': insights.get('optimization_opportunities', [])
    }
    
    self.learning_engine.persist_cross_session_learning(learning_record)

Learning Application

def _apply_cross_session_learning(self, session_context: dict) -> dict:
    """Apply learning from previous sessions."""
    historical_learning = self.learning_engine.get_historical_learning(
        session_context.get('project_type'),
        session_context.get('user_context', {})
    )
    
    # Apply learned optimizations
    optimizations = {
        'preferred_patterns': historical_learning.get('high_effectiveness_patterns', []),
        'cache_strategy': historical_learning.get('optimal_cache_durations', {}),
        'loading_strategy': historical_learning.get('optimal_loading_sequence', []),
        'performance_tuning': historical_learning.get('performance_optimizations', {})
    }
    
    return optimizations

Performance Optimization

Caching Strategies

Multi-Level Caching Architecture

class PerformanceOptimizedCache:
    def __init__(self):
        # L1: In-memory hot cache (immediate access)
        self.hot_cache = {}  # Most frequently accessed patterns
        self.hot_cache_max_size = 20
        
        # L2: In-memory warm cache (fast access)
        self.warm_cache = {}  # Recently accessed patterns
        self.warm_cache_max_size = 100
        
        # L3: Persistent cold cache (disk-based)
        self.cold_cache_path = Path("cache/notification_patterns")
        
        # Cache performance metrics
        self.cache_metrics = {
            'l1_hits': 0, 'l1_misses': 0,
            'l2_hits': 0, 'l2_misses': 0,
            'l3_hits': 0, 'l3_misses': 0,
            'total_requests': 0
        }

Intelligent Cache Management

def _optimize_cache_allocation(self):
    """Optimize cache allocation based on usage patterns."""
    
    # Promote frequently accessed patterns to hot cache
    access_frequency = self._calculate_access_frequency()
    for pattern_id, frequency in access_frequency.items():
        if frequency > self.hot_cache_threshold:
            self._promote_to_hot_cache(pattern_id)
    
    # Demote rarely accessed patterns
    for pattern_id, last_access in self._get_last_access_times().items():
        if time.time() - last_access > self.cold_cache_threshold:
            self._demote_to_cold_cache(pattern_id)

Cache Performance Monitoring

def _monitor_cache_performance(self) -> dict:
    """Monitor and report cache performance metrics."""
    total_requests = self.cache_metrics['total_requests']
    if total_requests == 0:
        return {'cache_hit_rate': 0.0, 'performance_score': 0.0}
    
    l1_hit_rate = self.cache_metrics['l1_hits'] / total_requests
    l2_hit_rate = self.cache_metrics['l2_hits'] / total_requests
    l3_hit_rate = self.cache_metrics['l3_hits'] / total_requests
    
    # Calculate weighted performance score (L1 hits are most valuable)
    performance_score = (l1_hit_rate * 1.0) + (l2_hit_rate * 0.7) + (l3_hit_rate * 0.3)
    
    return {
        'cache_hit_rate': l1_hit_rate + l2_hit_rate + l3_hit_rate,
        'l1_hit_rate': l1_hit_rate,
        'l2_hit_rate': l2_hit_rate,
        'l3_hit_rate': l3_hit_rate,
        'performance_score': performance_score,
        'optimization_needed': performance_score < 0.6
    }

Timing Optimization

Execution Time Profiling

def _profile_execution_timing(self, func, *args, **kwargs):
    """Profile function execution time with detailed breakdown."""
    start_time = time.perf_counter()
    
    # Profile individual components
    component_timings = {}
    
    # Intelligence analysis timing
    analysis_start = time.perf_counter()
    intelligence_analysis = self._analyze_intelligence_opportunities(*args)
    component_timings['intelligence_analysis'] = (time.perf_counter() - analysis_start) * 1000
    
    # Documentation loading timing
    loading_start = time.perf_counter()
    documentation_updates = self._load_jit_documentation(*args, intelligence_analysis)
    component_timings['documentation_loading'] = (time.perf_counter() - loading_start) * 1000
    
    # Pattern update timing
    pattern_start = time.perf_counter()
    pattern_updates = self._update_patterns_if_needed(*args)
    component_timings['pattern_updates'] = (time.perf_counter() - pattern_start) * 1000
    
    total_time = (time.perf_counter() - start_time) * 1000
    
    return {
        'total_time_ms': total_time,
        'component_timings': component_timings,
        'target_met': total_time < self.performance_target_ms,
        'performance_ratio': total_time / self.performance_target_ms
    }

Performance Optimization Strategies

def _apply_performance_optimizations(self, performance_data: dict):
    """Apply performance optimizations based on profiling data."""
    
    # Identify performance bottlenecks
    component_timings = performance_data.get('component_timings', {})
    bottleneck_threshold = self.performance_target_ms * 0.4  # 40% of total budget
    
    for component, timing in component_timings.items():
        if timing > bottleneck_threshold:
            self._optimize_component_performance(component, timing)
    
    # Apply global optimizations
    if performance_data.get('total_time_ms', 0) > self.performance_target_ms:
        self._apply_emergency_optimizations()

Emergency Performance Mode

def _apply_emergency_optimizations(self):
    """Apply emergency optimizations when performance targets are missed."""
    
    # Reduce cache retention to free memory
    self.doc_cache_minutes = min(self.doc_cache_minutes, 10)
    self.pattern_cache_minutes = min(self.pattern_cache_minutes, 20)
    
    # Limit concurrent operations
    self.max_concurrent_loads = 2
    
    # Skip non-essential processing
    self.skip_learning_updates = True
    self.skip_pattern_effectiveness_tracking = True
    
    # Enable aggressive caching
    self.aggressive_caching_enabled = True
    
    # Log performance degradation
    log_decision(
        "notification",
        "emergency_optimization",
        "performance_target_exceeded",
        f"Applying emergency optimizations: target={self.performance_target_ms}ms"
    )

Resource Management

Memory Optimization

def _optimize_memory_usage(self):
    """Optimize memory usage for notification processing."""
    
    # Limit cache size based on available memory
    available_memory = self._get_available_memory_mb()
    if available_memory < 100:  # Less than 100MB available
        self.hot_cache_max_size = min(self.hot_cache_max_size, 10)
        self.warm_cache_max_size = min(self.warm_cache_max_size, 50)
    
    # Implement aggressive garbage collection for cached patterns
    if len(self.notification_cache) > self.cache_size_limit:
        self._evict_least_recently_used_patterns()
    
    # Compress cached data for memory efficiency
    for cache_key, cache_data in self.notification_cache.items():
        if not cache_data.get('compressed', False):
            self.notification_cache[cache_key] = self._compress_cache_data(cache_data)

CPU Optimization

def _optimize_cpu_usage(self):
    """Optimize CPU usage during notification processing."""
    
    # Implement lazy evaluation for non-critical operations
    self.lazy_evaluation_enabled = True
    
    # Use CPU-efficient algorithms for pattern matching
    self.pattern_matching_algorithm = 'bloom_filter'  # Instead of linear search
    
    # Batch similar operations to reduce overhead
    self.operation_batching_enabled = True
    
    # Limit concurrent processing based on CPU availability
    cpu_cores = os.cpu_count()
    self.max_concurrent_operations = min(cpu_cores // 2, 4)

Monitoring and Alerting

Performance Monitoring

def _monitor_performance_metrics(self) -> dict:
    """Continuously monitor performance metrics."""
    return {
        'processing_time_ms': self._get_average_processing_time(),
        'cache_hit_rate': self._calculate_cache_hit_rate(),
        'memory_usage_mb': self._get_memory_usage(),
        'cpu_utilization_percent': self._get_cpu_utilization(),
        'throughput_notifications_per_second': self._calculate_throughput(),
        'error_rate_percent': self._calculate_error_rate(),
        'target_compliance_rate': self._calculate_target_compliance()
    }

Performance Alerting

def _check_performance_alerts(self, metrics: dict):
    """Check performance metrics against thresholds and generate alerts."""
    
    alerts = []
    
    # Processing time alert
    if metrics['processing_time_ms'] > self.performance_target_ms * 1.5:
        alerts.append({
            'type': 'performance_degradation',
            'severity': 'high',
            'message': f"Processing time {metrics['processing_time_ms']}ms exceeds target",
            'recommended_action': 'apply_emergency_optimizations'
        })
    
    # Cache performance alert
    if metrics['cache_hit_rate'] < 0.6:
        alerts.append({
            'type': 'cache_performance_low',
            'severity': 'medium',
            'message': f"Cache hit rate {metrics['cache_hit_rate']:.2%} below optimal",
            'recommended_action': 'optimize_cache_strategy'
        })
    
    # Memory usage alert
    if metrics['memory_usage_mb'] > 50:
        alerts.append({
            'type': 'memory_usage_high',
            'severity': 'medium',
            'message': f"Memory usage {metrics['memory_usage_mb']}MB exceeds target",
            'recommended_action': 'reduce_cache_size'
        })
    
    return alerts

Integration Points

Framework Integration

• SuperClaude-Lite Framework: Core component providing just-in-time intelligence loading
• Hook Coordination: Works with session_start, pre_tool_use, post_tool_use, and stop hooks
• MCP Server Integration: Coordinates with Context7, Sequential, Magic, Playwright, Morphllm, and Serena
• Quality Gates: Contributes to validation steps through intelligence loading and pattern updates

Performance Integration

• Real-Time Monitoring: Integrates with framework performance monitoring system
• Learning System: Contributes to cross-hook learning and adaptation
• Resource Management: Coordinates with global resource management and optimization
• Error Handling: Implements graceful degradation and fallback strategies

Data Flow Integration

• Input: Notification events from Claude Code with operation context
• Processing: Just-in-time intelligence loading, pattern updates, caching optimization
• Output: Enhanced notification responses with loaded intelligence and framework updates
• Learning: Continuous learning data for optimization and adaptation

Related Documentation

• Hook System Overview: docs/Overview.md - Complete hook system architecture
• Configuration Guide: docs/Configuration/ - Configuration management and customization
• Performance Monitoring: docs/Modules/performance_monitor.md - Performance tracking and optimization
• Learning Engine: docs/Modules/learning_engine.md - Learning system integration and adaptation
• Pattern Detection: docs/Patterns/ - Dynamic pattern management and updates

Conclusion

The notification hook represents a paradigm shift from traditional static documentation loading to intelligent, just-in-time capability provisioning. By reducing context usage by 90% while maintaining full framework intelligence, it enables the SuperClaude framework to operate efficiently at scale while providing adaptive, learning-driven optimization for maximum effectiveness.

Key benefits:

• 90% Context Reduction: From 60KB upfront loading to 8-12KB on-demand loading
• <100ms Performance: Ultra-fast notification processing with intelligent caching
• Adaptive Intelligence: Learning-driven optimization based on usage patterns and effectiveness
• Dynamic Patterns: Real-time pattern updates based on operational context
• Performance Optimization: Multi-tier caching and resource management for optimal efficiency

The notification hook enables the SuperClaude framework to be both comprehensive and efficient, providing full capability access while maintaining optimal performance characteristics.