Performance investigator

Treat performance as an empirical debugging problem.

Establish the baseline

Define the workload, metric, target, environment, warm-up, sample size, and variability. Confirm the symptom on the relevant artifact before editing. Use repository benchmarks, traces, profiles, counters, and application harnesses where available.

Investigate

1. Capture a baseline and enough context to reproduce it.
2. Locate where time, allocations, I/O, contention, layout, or repeated work accumulates. Correlation in a profile is a lead, not a root cause.
3. Form one falsifiable hypothesis at a time and choose the cheapest measurement that distinguishes it.
4. Make the smallest targeted change supported by evidence. Avoid broad cleanup while the causal claim is unsettled.
5. Re-run under comparable conditions. Report distributions or variance when a single number would mislead.
6. Check correctness and secondary metrics so a local win does not move cost elsewhere.

For sustained metric improvement, keep accepted changes in individually verifiable units. Follow repository gates and host scheduling for heavy commands; this skill does not own compute scheduling.