perf-theory-tester — Claude Code skill: controlled perf experiments
A skill that keeps perf experiments honest: one change per hypothesis, repeated runs, a recorded verdict. Useful when you need proof that an optimization helped.
npx skills add composio-community/awesome-claude-plugins --skill perf-theory-tester -a claude-code
npx skills add composio-community/awesome-claude-plugins --skill theory-tester -a claude-codeWhat perf-theory-tester does
The skill walks the agent through a controlled experiment. It starts by confirming the baseline is clean. Then it applies a single change tied to the hypothesis and runs at least two validation passes. The code goes back to baseline before the next experiment begins.
Output follows a fixed shape: hypothesis id, a short summary of the change, metric deltas, a verdict of accept, reject or inconclusive, plus evidence — the benchmark command and the files touched. The skill treats docs/perf-requirements.md as the canonical contract to follow.
The rules are strict on purpose. One change per experiment. No parallel benchmarks. Every run leaves a record. Without that discipline, measurement noise passes for a speedup.
What it helps with
- prove that a code change actually made things faster
- isolate the effect of one optimization from surrounding edits
- keep a record of the benchmark command and files for each run
- get a clear accept or reject instead of a guess
- reset the tree to baseline before the next attempt
Example prompts for the agent
- Run a controlled experiment on the hypothesis that caching cuts response time, and format the result the way the skill expects
- Take a baseline of the
/api/searchendpoint, apply one change, then give me a verdict with the commands and changed files - Check my experiment against
docs/perf-requirements.mdand tell me what is missing before a verdict
How to install perf-theory-tester
1Skills CLI (Vercel)
npx skills add composio-community/awesome-claude-plugins --skill perf-theory-tester -a claude-codenpx skills add composio-community/awesome-claude-plugins --skill theory-tester -a claude-code2Manually: put the files in place
~/.claude/skills/theory-tester/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/theory-tester/
1Skills CLI (Vercel)
npx skills add composio-community/awesome-claude-plugins --skill theory-tester -a codex2Manually: put the files in place
.agents/skills/theory-tester/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add composio-community/awesome-claude-plugins --skill theory-tester -a cursor2Manually: put the files in place
.cursor/skills/theory-tester/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install composio-community/awesome-claude-plugins theory-tester2Manually: put the files in place
.github/skills/theory-tester/SKILL.md1Skills CLI (Vercel)
npx skills add composio-community/awesome-claude-plugins --skill theory-tester -a gemini-cli2Platform CLI
gemini skills install https://github.com/composio-community/awesome-claude-plugins --path perf/skills/theory-testerManually: put the files in place
.devin/skills/theory-tester/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add composio-community/awesome-claude-plugins --skill theory-tester -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about perf-theory-tester
How do I install perf-theory-tester in Claude Code?
Run npx skills add composio-community/awesome-claude-plugins --skill perf-theory-tester -a claude-code. Alternatively install it as theory-tester: npx skills add composio-community/awesome-claude-plugins --skill theory-tester -a claude-code.
What does perf-theory-tester do?
It runs one perf hypothesis at a time: clean baseline, a single change, two or more validation passes, then a revert. You get accept, reject or inconclusive with supporting evidence.
How is it different from plain profiling?
It does not hunt for hot spots; it tests a hypothesis you already wrote down. One change per run and no parallel benchmarks keep the effects of separate edits from mixing.