agentleFS
MegapromptingMegaprompting/torque-loop/skills/repo-audit/SKILL.md

repo-audit

Skill10 starsChanged 4 months agoMITView on GitHub

Discover user-facing features, workflows, routes, APIs, configs, and tests in a codebase by evidence, not guesses. Use to turn an unfamiliar or under-documented repo into an inventory of what actually ships. Returns features with code evidence, undocumented screens/routes/workflows, APIs and state transitions needing tests, the highest-risk feature, and the first test suite to write.

23 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use code-review.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • GoogleSkill

    code-review

    Adversarial systems, concurrency, memory-model, and architecture code review protocol. Use this skill whenever asked to review code, PRs, diffs, or branches in Filament.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 19 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Do not infer a feature you cannot point to in code.

    line 20

“Common” is measured: kept by at least 6 in 10 of the 19 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/ratchet:repo-audit — evidence-based feature discovery

A codebase is a claim about what it does. This command verifies the claim by evidence. It discovers user-facing features from the code itself — not from the README, not from memory — and marks the gaps between what exists and what is tested.

Step 0 — Load state and ground truth

ratchet snapshot repo

Then read for real: routes, entrypoints, handlers, UI components, API definitions, config, and existing tests. Use Grep/Glob to find route tables, exported handlers, and test files. Do not infer a feature you cannot point to in code.

Procedure

For each user-facing feature discovered by evidence, capture:

  • Feature ID and name
  • User story — who does what, and why
  • Evidence in code — file:line where it lives
  • Expected behavior — what it should do
  • Edge cases — the inputs that stress it
  • Test cases — what would verify it
  • Dependencies — what it relies on
  • Known assumptions — what it takes for granted
  • Current risk level — critical / high / medium / low

Then identify the gaps:

  1. Screens / routes / workflows not yet documented.
  2. APIs or state transitions that need tests.
  3. The highest-risk feature — most user-facing × least tested.
  4. The first test suite to write.
  5. The smallest fix likely to improve user trust.

Rules

  • Do not declare coverage complete unless every route, screen, workflow, and API interaction has evidence. Missing evidence is a finding, not a rounding error.
  • A feature with no test is a defect waiting to happen — record it.

Output contract

Return the feature inventory as a table, then the five gap findings. Then serialize:

ratchet ledger create
ratchet ledger update features '{"name":"...","area":"...","routes":"...","status":"discovered"}'
ratchet defect add '{"severity":"high","summary":"<highest-risk feature> has no test coverage"}'

Next: /ratchet:qa-ledger to formalize the canonical ledger, then /ratchet:verify.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.