performance-profiler
Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.
DeepseekModel
Curated skill
Quality Excellent · 90
v1.0.0
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name performance-profiler description Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production. Performance Profiler Tier: POWERFUL Category: Engineering Domain: Performance Engineering Overview Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after. Core Capabilities CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go Memory profiling — heap snapshots, leak detection, GC pressure Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection Load testing — k6 scripts, Artillery scenarios, ramp-up patterns Before/after measurement — establish baseline, profile, optimize, verify When to Use App is slow and you don't know where the bottleneck is P99 latency exceeds SLA before a release Memory usage grows over time (suspected leak) Bundle size increased after adding dependencies Preparing for a traffic spike (load test before launch) Database queries taking >100ms Quick Start # Analyze a project for performance risk indicators python3 scripts/performance_profiler.py /path/to/project # JSON output for CI integration python3 scripts/performance_profiler.py /path/to/project --json # Custom large-file threshold python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256 Golden Rule: Measure First # Establish baseline BEFORE any optimization # Record: P50, P95, P99 latency | RPS | error rate | memory usage # Wrong: "I think the N+1 query is slow, let me fix it" # Right: Profile → confirm bottleneck → fix → measure again → verify improvement Node.js Profiling → See references/profiling-recipes.md for details References references/profiling-recipes.md — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots references/optimization-playbook.md — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices
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| Field | Description |
|---|---|
| format | Format tag (skill/v1) |
| skill_id | Unique skill ID |
| name | Skill name |
| version | Version |
| description | Description |
| category | Categories (array) |
| trigger_words | Trigger words |
| tags | Tags |
| source | Source |
| source_url | Source URL (this page) |
| exported_at | Exported at (set per download) |
| system_prompt | System prompt body |
| model_config | Model config: provider / model / temperature / max_tokens / top_p |
| examples | Examples |
| install_guide | Import guide for Coze / Dify / Claude / custom frameworks |
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