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

Notification Hook Documentation

Overview

The notification hook implements just-in-time capability loading and pattern updates for the SuperClaude-Lite framework. This hook runs on notification events in Claude Code and provides intelligent, on-demand resource loading instead of upfront documentation loading, enabling a pattern-driven approach that reduces context usage by 90%.

Purpose

Just-in-Time Capability Loading and Pattern Updates

The notification hook transforms the SuperClaude framework from a static documentation loader into a dynamic, intelligent system that loads capabilities precisely when needed. Key purposes include:

• On-Demand Resource Loading: Load MCP server documentation and patterns only when specific capabilities are required
• Dynamic Pattern Updates: Update framework patterns in real-time based on operation context and usage effectiveness
• Intelligence Caching: Implement performance-optimized caching strategies to minimize repeated loading overhead
• Real-Time Learning: Adapt framework behavior based on notification patterns and operational effectiveness
• Context Optimization: Reduce framework context overhead by 90% through selective, just-in-time loading

Execution Context

When This Hook Runs

The notification hook executes on notification events from Claude Code, specifically:

Primary Triggers

• Tool Request Notifications: When Claude Code requests specific tools or capabilities
• Context Change Notifications: When the operational context shifts (project type, complexity, domain)
• Performance Issue Notifications: When resource constraints or performance issues are detected
• Error/Failure Notifications: When operations fail and recovery intelligence is needed
• Operation Start/Complete Notifications: When major operations begin or conclude

Notification Types Processed

high_priority:
  - error                    # System errors requiring immediate intelligence loading
  - failure                  # Operation failures needing recovery patterns
  - security_alert          # Security issues requiring specialized documentation
  - performance_issue        # Performance problems needing optimization patterns
  - validation_failure       # Validation errors requiring compliance patterns

medium_priority:
  - tool_request            # Tool usage requiring MCP documentation
  - context_change          # Context shifts requiring pattern updates
  - resource_constraint     # Resource limitations requiring optimization

low_priority:
  - info                    # Informational notifications
  - debug                   # Debug notifications
  - status_update           # Status change notifications

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.