Perf investigation
Diagnose and fix a performance problem with measurement, not guesswork. Use for slow endpoints, high latency, or resource issues. Follows measure → profile → find the bottleneck → fix → re-measure.From its SKILL.md
npx -y skills add vaibhavsaxena022/skills --skill perf-investigationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 27 days oldThe repository was created 27 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.3 KB, 194 tokens by cl100k_base, as published. Nobody here has run it
Performance Investigation
Optimize what the data says is slow — never guess.
Steps
- Measure — establish a baseline (latency percentiles, throughput, resource use) and a target.
- Profile — find where time/allocations actually go: query timings,
EXPLAIN ANALYZE, a flame graph, traces. - Locate the bottleneck — the dominant cost. Usually I/O: N+1 queries, a missing index, chatty external calls, no caching.
- Fix one thing — the biggest bottleneck first.
- Re-measure — confirm the gain against the baseline; check nothing regressed.
Rules
- No optimization without a measurement proving it matters.
- Fix the dominant cost before micro-optimizing.
- Prefer algorithmic / I/O fixes over micro-tuning.
- Keep the code readable — don't trade clarity for a negligible gain.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.