self-perf
Skill4 starsChanged 4 months agoMITView on GitHub
Profile and optimize performance — use when asked to self-perf, optimize performance, profile hot paths, speed this up, or "make it faster". Triggers the deterministic self-perf workflow (baseline → profile → optimize → verify improvement).
In this repository40 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- adrSkill
- auditSkill
- autoloopSkill
- browserSkill
- bugfixSkill
- changelogSkill
- clean-codeSkill
- creating-workflowsSkill
- deep-researchSkill
- doc-genSkill
- dont-reinventSkill
- drySkill
- executingSkill
- featureSkill
- gcliSkill
- harness-auditSkill
- healthSkill
- mega-prSkill
- onboardSkill
- pr-readySkill
- refactorSkill
- researchSkill
- scrapeSkill
- scratchpadSkill
- screenshotSkill
- self-auditSkill
- self-improveSkill
- self-lintSkill
- self-migrateSkill
- self-testSkill
- setup-rulesSkill
- stuckSkill
- test-genSkill
- tri-debugSkill
- tri-dispatchSkill
- tri-reviewSkill
- tri-securitySkill
- yagniSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 12
Use the devkit_start tool with workflow: "self-perf" and input: "{input}".
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Self-Perf
Deterministic performance optimization loop. Baseline → optimize hot paths → verify improvement → summary.
Invoke
Use the devkit_start tool with workflow: "self-perf" and input: "{input}".
Then follow each step the engine returns. Call devkit_advance after completing each step. The engine controls step order, gates, and loops. Do NOT skip steps.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.