Performance Optimization

Improve a named user outcome with comparable evidence. Prose shortening and ordinary cleanup are outside this skill. A newly introduced regression whose cause is the main question is a bug. To fix it, route to the bug-fix playbook: at top level return to mvp-mode; inside a delegated task, report that route to the parent. For the diagnosis alone, use systematic-debugging.

1. Define the relevant metric, workload and target. If unclear, propose the measurement or ask about the consequential tradeoff before tuning code.
2. Capture a baseline with representative data, cache state, concurrency and environment. Repeat enough to estimate variability for the decision; use percentiles when the service target concerns tail latency. Record the command, conditions and results rather than assuming a fixed repeat count proves significance.
3. Locate the material cost using existing measurements, focused instrumentation or profiling. Code inspection can suggest candidates; measurements determine whether they matter. Use references/measurement-recipes.md for unfamiliar tools and references/frontend-performance.md for browser performance.
4. Change one attributable factor where practical. For an inseparable set, explain the coupling. Preserve required semantics, freshness, safety and accessibility.
5. Compare under the same conditions and run applicable correctness checks. Keep changes whose benefit justifies their complexity; remove only your ineffective experimental edits, preserving other work. A result within noise is inconclusive, not evidence of a speedup. Record cost/latency/memory tradeoffs explicitly.

Keep a compact attempt record with baseline → result, decision and reason so failed experiments are not repeated after context recovery. Persist it in the task/report when the work needs handoff, not as an extra artifact for every tiny experiment.

For a material recurring risk, add an economical regression guard: query counts, bounded allocations or a controlled benchmark, using existing tooling. Scale its cost to the risk; avoid flaky wall-clock assertions and unnecessary frameworks. Report measured outcomes, applicable checks and limits. Performance improvement does not excuse a correctness regression or authorize unrelated edits.