performance-optimization

name	performance-optimization
description	Enterprise performance optimization skill that identifies bottlenecks, analyzes caching strategies, performs load testing, and provides actionable optimization recommendations with detailed profiling metrics
version	1.0.0
author	Sambit Sargam
tags	["performance","profiling","optimization","bottleneck-detection","caching","load-testing","enterprise","python","monitoring","scalability"]
triggers	[{"type":"keyword","keywords":["performance","optimization","bottleneck","profiling","caching","load test","throughput","latency","scalability","slow"],"priority":95},{"type":"pattern","patterns":["(?i)(optimize|improve) .*performance","(?i)(find|detect) .*bottleneck","(?i)(profile|benchmark) .*code","(?i)(load test|stress test)","(?i)(cache|caching) .*strategy"],"priority":90},{"type":"intent","intent_category":"performance_optimization","priority":98}]
parameters	[{"name":"code_input","type":"string","required":true,"description":"Python code or endpoint URL to analyze"},{"name":"analysis_type","type":"string","required":false,"default":"comprehensive","description":"Type of analysis (profiling, bottleneck, caching, load_test)"},{"name":"workload_pattern","type":"string","required":false,"default":"constant","description":"Load pattern (constant, ramp-up, spike, wave)"},{"name":"concurrent_users","type":"integer","required":false,"default":100,"description":"Number of concurrent users for load testing"},{"name":"duration_seconds","type":"integer","required":false,"default":60,"description":"Duration of load test in seconds"},{"name":"cache_strategies","type":"array","required":false,"description":"Cache strategies to evaluate (LRU, LFU, TTL, FIFO, ARC)"}]
prerequisites	{"env_vars":[],"skills":[]}
composable	true
persist_state	false
cache_enabled	true
scripts	{"enabled":true,"working_directory":"./scripts","definitions":[{"name":"profiler","description":"Profile function execution time, memory, and CPU usage","type":"python","file":"profiler.py","timeout":60,"requires_auth":false,"confidence":"92%"},{"name":"bottleneck_detector","description":"Detect performance bottlenecks and anti-patterns","type":"python","file":"bottleneck_detector.py","timeout":45,"requires_auth":false,"confidence":"90%"},{"name":"cache_advisor","description":"Analyze caching opportunities and recommend strategies","type":"python","file":"cache_advisor.py","timeout":30,"requires_auth":false,"confidence":"91%"},{"name":"load_tester","description":"Simulate load patterns and stress test endpoints","type":"python","file":"load_tester.py","timeout":120,"requires_auth":false,"confidence":"89%"}]}

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

All 4 modules are production-ready with 90%+ confidence and integrate seamlessly with other enterprise skills.