perf-profiling

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Systematic performance profiling and optimization. Use when performance issues are reported or suspected. Measure first, optimize second. Applies Pareto principle — find the 20% of code causing 80% of slowness, fix that, not the rest.

AI & Automation 24 stars 3 forks Updated 1 weeks ago MIT

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Skill Content

> **AI-consumed reference.** Optimized for Claude to read during execution. > Human-readable explanation: see [docs/architecture/HIERARCHICAL_PLANNING.md](../../../docs/architecture/HIERARCHICAL_PLANNING.md) > or [docs/getting-started/](../../../docs/getting-started/) depending on topic. # Performance Profiling **Rule 0:** Don't optimize what you haven't measured. Almost all "obvious" optimizations are wrong. --- ## The Protocol ### Step 1 — Define "fast enough" Performance is relative. Before optimizing: - **Current metric** (p50, p95, p99 latency / MB memory / % CPU) - **Target metric** (what's the SLO/SLA/user expectation?) - **Gap** — what needs to close If no target → **ask the user**. Don't optimize without a goal — you'll polish forever. ### Step 2 — Measure baseline Use the appropriate profiler: | Language | Profiler | |----------|----------| | Node.js | `node --prof`, `clinic.js`, `0x` | | Browser JS | Chrome DevTools Performance tab | | Python | `cProfile`, `py-spy`, `memory_profiler` | | Go | `pprof` (built-in) | | Rust | `cargo flamegraph`, `perf` | | CLI | `hyperfine` | | HTTP | `wrk`, `k6`, `vegeta` | Run the profiler under **realistic load**, not trivial input. Capture for 30–60s minimum — short captures miss long-tail events. ### Step 3 — Analyze (flamegraph / top consumers) Identify the top 3 functions by: - **Self time** (function's own CPU, excluding descendants) - **Total time** (self + descendants) - **Call count** (high-frequency cold func...

Details

Author: nguyenthienthanh
Repository: nguyenthienthanh/aura-frog
Created: 9 months ago
Last Updated: 1 weeks ago
Language: JavaScript
License: MIT

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