optimize
Use for performance work. Measure-first, profile-driven. Refuses to optimise without a baseline measurement. Targets the actual hotspot, not where intuition says it is.
What this skill does
# optimize Measure. Find the hotspot. Optimise the hotspot. Re-measure. Stop. ## Method 1. **Baseline.** Reproduce the slowness with a deterministic command. Record: - Wall time, CPU time, memory, allocations, query count, network calls (whichever apply) - Example input characteristics 2. **Goal.** State a numeric target. "Faster" is not a goal. "p95 < 200ms" is. 3. **Profile.** Use the right tool for the language: - Node: `--prof`, clinic.js, 0x - Python: cProfile, py-spy, scalene - Apex: Salesforce debug log analysis (`/siftcoder:sf-debug`) - SQL: EXPLAIN ANALYZE - Browser: DevTools Performance tab 4. **Identify the actual hotspot.** Often surprising. Optimising elsewhere is wasted. 5. **Hypothesise the fix.** State why it should help, by how much. 6. **Apply minimal change.** 7. **Re-measure.** If improvement is real and meets the goal — stop. If not, revert and try another hypothesis. 8. **Regression test.** Add a perf test (with reasonable tolerance) so you don't lose the win. ## Output shape ``` Goal: <numeric target> Baseline: <metric snapshot> Profile: <hotspot identified — function or query, w/ %> Hypothesis: <fix description, expected gain> Change: <diff> After: <new metric snapshot> Improvement: <delta> Status: ✓ goal met | ✗ goal not met (revert) ``` ## Rules - **No optimisation without baseline.** "Faster" is not measurable. - **No optimisation without profiling.** Intuition is wrong about hot paths >50% of the time. - **Goal is numeric.** P95, throughput, memory, query count. - **Revert if the change doesn't move the metric.** Speculative optimisations rot the code. - **Add a perf test for the win.** Otherwise it regresses silently in 6 months. ## Anti-patterns - "Cleaner code is faster" — sometimes, often not - Caching without measuring cache hit rate - Async-ifying sync code that wasn't blocking - Premature optimisation in code that runs once a day - Micro-optimising at the expense of readability when the macro problem is unsolved ## When NOT to use - Refactoring for clarity (no perf goal) — `/siftcoder:refactor` - Deleting code (smaller is better but not perf-driven) — `/siftcoder:zen` - Bug fix — `/siftcoder:fix` ## Subagent dispatch - `general-purpose` for targeted optimisation work - `investigator` for read-only profiling diagnosis - For Salesforce: `/siftcoder:sf-debug parse <log>` — dedicated debug-log analysis ## Value over native CC CC will optimise on request. CC won't naturally insist on baseline + profile + numeric goal + revert-if-no-improvement. This skill enforces the discipline that distinguishes real optimisation from cargo-cult.
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