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.
Works with
--- 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. license: MIT --- # 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 ```bash # 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 ```bash # 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](references/profiling-recipes.md) — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots - [references/optimization-playbook.md](references/optimization-playbook.md) — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices
More Debugging skills
diagnosing-bugs
mattpocock/skills
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
explore-code
lllllllama/rigorpilot-skills
Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.
safe-debug
lllllllama/rigorpilot-skills
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.
