# 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 ```python 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 ```python 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 ```python 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: ```yaml 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 ```python 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** ```yaml 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** ```yaml 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 ```python 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 ```yaml 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 ```yaml 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 ```python 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** ```python 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** ```python 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) ```json { "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) ```yaml # 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) ```bash SUPERCLAUDE_NOTIFICATION_ENABLED=true SUPERCLAUDE_NOTIFICATION_PERFORMANCE_TARGET=100 SUPERCLAUDE_NOTIFICATION_CACHE_DURATION=30 SUPERCLAUDE_NOTIFICATION_LEARNING_ENABLED=true ``` ### Configuration Loading ```python 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 ```python 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) ```yaml 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) ```yaml 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) ```yaml 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) ```yaml 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 ```yaml 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 ```yaml 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 ```yaml 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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 ```python 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.