weave-audit
Skill5 starsChanged 2 months agoMITView on GitHub
Audit the whole repository for removable code: dead modules, needless dependencies, duplicate helpers, dead flexibility, and custom code a native feature or the stdlib replaces. Ranked list, biggest cut first. Use when the user says "weave audit", "/weave-audit", "audit this repo", "find bloat", or "what can I delete". One-shot report, edits nothing.
In this repository9 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .cursor/rules/weave.mdcCursor rule
- weave-debtSkill
- weave-gainSkill
- weave-helpSkill
- weave-planSkill
- weave-promptSkill
- weave-reviewSkill
- weaveSkill
The file
Files
Weave Audit
weave-review, repo-wide. Scan the tracked tree (skip node_modules, .git,
build output, vendored code). Confirm usage with a search before calling
anything dead.
Tags
Same as weave-review: delete, reuse, stdlib, native, yagni,
shrink, shared.
Hunt
- Dependencies the stdlib or platform already ships, or used for one call.
- Two helpers doing the same job in different folders.
- Interfaces with one implementation, factories with one product, wrappers that only delegate.
- Flags, options, and config keys nothing sets.
- Exported functions with no importer.
Output
One line per finding, ranked:
<tag> <what to cut>. <replacement>. [path] evidence: <search or usage that proves it>
Then two groups:
- Safe now: confirmed by search, no behavior change.
- Needs a decision: requires measurement, a product call, or touches a public API.
End with net: -<N> lines, -<M> deps possible. Nothing to cut:
Lean already. Ship.
Boundaries
Complexity only; correctness, security, and performance belong elsewhere. Lists findings, applies nothing. One-shot.
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.