jiayaoqijia outputs:
- type: metrics format: json description: Performance metrics including timing, memory, CPU
- type: bottleneck_report format: json description: Detected bottlenecks with severity and recommendations
- type: cache_analysis format: json description: Cache strategy rankings and hit rate estimations
- type: load_test_report format: json description: Load test results with latency percentiles and error rates
- type: recommendations format: markdown description: Actionable optimization recommendations
examples:
- input: "Function profiling for data processing" output: "Time: 145ms, CPU: 32.5%, Memory: 12.4MB"
- input: "Detect bottlenecks in database queries" output: "N+1 query pattern found, missing index on user_id"
- input: "Analyze caching for user session data" output: "LRU cache recommended, 85% hit rate expected"
- input: "Load test with 100 concurrent users" output: "Throughput: 425 req/s, P99 latency: 892ms"
success_criteria:
- Identified performance bottlenecks with 90%+ accuracy
- Profiling overhead < 5% of execution time
- Cache strategy recommendations improve hit rate by 20%+
- Load test simulation realistic within 15% variance
integration_points:
- Code Refactoring Advisor (code quality metrics)
- Database Operations Manager (query optimization)
- Security Vulnerability Scanner (performance security)
- API Integration Helper (endpoint monitoring)
notes: | Performance Optimization provides enterprise-grade performance analysis and optimization capabilities:
- Profile Python functions at microsecond precision
- Detect 10+ performance anti-patterns
- Evaluate 5 major caching strategies
- Simulate realistic load patterns
- Generate actionable optimization recommendations