perf-profilinglisted

> **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