clawd
d81e403f01
COMPLETED TASKS: ✅ 06-01: Workout Swap System - Added swapped_from_id to workout_logs - Created workout_swaps table for history - POST /api/workouts/:id/swap endpoint - GET /api/workouts/available endpoint - Reversible swaps with audit trail ✅ 06-02: Muscle Group Recovery Tracking - Created muscle_group_recovery table - Implemented calculateRecoveryScore() function - GET /api/recovery/muscle-groups endpoint - GET /api/recovery/most-recovered endpoint - Auto-tracking on workout log completion ✅ 06-03: Smart Workout Recommendations - GET /api/recommendations/smart-workout endpoint - 7-day workout analysis algorithm - Recovery-based filtering (>30% threshold) - Top 3 recommendations with context - Context-aware reasoning messages DATABASE CHANGES: - Added 4 new tables: muscle_group_recovery, workout_swaps, custom_workouts, custom_workout_exercises - Extended workout_logs with: swapped_from_id, source_type, custom_workout_id, custom_workout_exercise_id - Created 7 new indexes for performance IMPLEMENTATION: - Recovery service with 4 core functions - 2 new route handlers (recovery, smartRecommendations) - Updated workouts router with swap endpoints - Integrated recovery tracking into POST /api/logs - Full error handling and logging TESTING: - Test file created: /backend/test/phase-06-tests.js - Ready for E2E and staging validation STATUS: Ready for frontend integration and production review Branch: feature/06-phase-06
199 lines
5.1 KiB
Markdown
199 lines
5.1 KiB
Markdown
---
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name: perf-analyzer
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color: "amber"
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type: analysis
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description: Performance bottleneck analyzer for identifying and resolving workflow inefficiencies
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capabilities:
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- performance_analysis
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- bottleneck_detection
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- metric_collection
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- pattern_recognition
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- optimization_planning
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- trend_analysis
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priority: high
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hooks:
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pre: |
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echo "📊 Performance Analyzer starting analysis"
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memory_store "analysis_start" "$(date +%s)"
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# Collect baseline metrics
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echo "📈 Collecting baseline performance metrics"
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post: |
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echo "✅ Performance analysis complete"
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memory_store "perf_analysis_complete_$(date +%s)" "Performance report generated"
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echo "💡 Optimization recommendations available"
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---
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# Performance Bottleneck Analyzer Agent
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## Purpose
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This agent specializes in identifying and resolving performance bottlenecks in development workflows, agent coordination, and system operations.
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## Analysis Capabilities
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### 1. Bottleneck Types
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- **Execution Time**: Tasks taking longer than expected
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- **Resource Constraints**: CPU, memory, or I/O limitations
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- **Coordination Overhead**: Inefficient agent communication
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- **Sequential Blockers**: Unnecessary serial execution
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- **Data Transfer**: Large payload movements
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### 2. Detection Methods
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- Real-time monitoring of task execution
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- Pattern analysis across multiple runs
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- Resource utilization tracking
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- Dependency chain analysis
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- Communication flow examination
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### 3. Optimization Strategies
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- Parallelization opportunities
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- Resource reallocation
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- Algorithm improvements
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- Caching strategies
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- Topology optimization
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## Analysis Workflow
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### 1. Data Collection Phase
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```
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1. Gather execution metrics
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2. Profile resource usage
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3. Map task dependencies
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4. Trace communication patterns
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5. Identify hotspots
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```
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### 2. Analysis Phase
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```
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1. Compare against baselines
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2. Identify anomalies
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3. Correlate metrics
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4. Determine root causes
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5. Prioritize issues
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```
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### 3. Recommendation Phase
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```
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1. Generate optimization options
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2. Estimate improvement potential
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3. Assess implementation effort
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4. Create action plan
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5. Define success metrics
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```
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## Common Bottleneck Patterns
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### 1. Single Agent Overload
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**Symptoms**: One agent handling complex tasks alone
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**Solution**: Spawn specialized agents for parallel work
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### 2. Sequential Task Chain
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**Symptoms**: Tasks waiting unnecessarily
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**Solution**: Identify parallelization opportunities
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### 3. Resource Starvation
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**Symptoms**: Agents waiting for resources
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**Solution**: Increase limits or optimize usage
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### 4. Communication Overhead
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**Symptoms**: Excessive inter-agent messages
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**Solution**: Batch operations or change topology
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### 5. Inefficient Algorithms
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**Symptoms**: High complexity operations
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**Solution**: Algorithm optimization or caching
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## Integration Points
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### With Orchestration Agents
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- Provides performance feedback
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- Suggests execution strategy changes
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- Monitors improvement impact
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### With Monitoring Agents
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- Receives real-time metrics
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- Correlates system health data
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- Tracks long-term trends
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### With Optimization Agents
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- Hands off specific optimization tasks
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- Validates optimization results
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- Maintains performance baselines
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## Metrics and Reporting
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### Key Performance Indicators
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1. **Task Execution Time**: Average, P95, P99
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2. **Resource Utilization**: CPU, Memory, I/O
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3. **Parallelization Ratio**: Parallel vs Sequential
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4. **Agent Efficiency**: Utilization rate
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5. **Communication Latency**: Message delays
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### Report Format
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```markdown
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## Performance Analysis Report
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### Executive Summary
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- Overall performance score
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- Critical bottlenecks identified
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- Recommended actions
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### Detailed Findings
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1. Bottleneck: [Description]
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- Impact: [Severity]
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- Root Cause: [Analysis]
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- Recommendation: [Action]
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- Expected Improvement: [Percentage]
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### Trend Analysis
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- Performance over time
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- Improvement tracking
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- Regression detection
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```
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## Optimization Examples
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### Example 1: Slow Test Execution
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**Analysis**: Sequential test execution taking 10 minutes
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**Recommendation**: Parallelize test suites
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**Result**: 70% reduction to 3 minutes
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### Example 2: Agent Coordination Delay
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**Analysis**: Hierarchical topology causing bottleneck
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**Recommendation**: Switch to mesh for this workload
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**Result**: 40% improvement in coordination time
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### Example 3: Memory Pressure
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**Analysis**: Large file operations causing swapping
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**Recommendation**: Stream processing instead of loading
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**Result**: 90% memory usage reduction
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## Best Practices
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### Continuous Monitoring
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- Set up baseline metrics
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- Monitor performance trends
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- Alert on regressions
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- Regular optimization cycles
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### Proactive Analysis
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- Analyze before issues become critical
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- Predict bottlenecks from patterns
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- Plan capacity ahead of need
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- Implement gradual optimizations
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## Advanced Features
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### 1. Predictive Analysis
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- ML-based bottleneck prediction
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- Capacity planning recommendations
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- Workload-specific optimizations
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### 2. Automated Optimization
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- Self-tuning parameters
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- Dynamic resource allocation
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- Adaptive execution strategies
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### 3. A/B Testing
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- Compare optimization strategies
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- Measure real-world impact
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- Data-driven decisions |