agentleFS
sson0-ersson0-er/orchestraude/skills/review-rules/SKILL.md

review-rules

Skill0 starsChanged 50 days agoMITView on GitHub

Shared rules for the four implementation reviewers (quality, security, spec, tests) - findings JSON shape, severity calibration and how to review later rounds. Preloaded by the reviewer agents.

2 other projects use a version of this.

Adopted by

Most recently taken up by

  • Other versions2 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 2

    1. stardew-valley-dedicated-server/server★ 357same name
    2. coder-xieshijie/dev-skills★ 5same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository3 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 2 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read docs/orchestraude/review-policy.md when it exists: it lists accepted low findings and adopted conventions.

    line 12
  2. Very specific:

    Write only the following JSON to your findings file (Write tool).

    line 15
  3. Very specific:

    If there is nothing to report, findings is an empty array.

    line 19
  4. Distinctive:

    When unsure between medium and low, choose low.

    line 26
  5. Distinctive:

    Read the history first. Do not contradict a change an earlier round asked for, and do not re-raise an item the implementer declined with a …

    line 33

≈ “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.

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

Implementation review rules

These rules apply to every implementation reviewer. Your agent file gives your role id, your checklist and the path of your findings file docs/orchestraude/<slug>/tasks/<task-id>.findings.<role>.json.

Review policy

Read docs/orchestraude/review-policy.md when it exists: it lists accepted low findings and adopted conventions. Do not report a low finding it covers.

Output format

Write only the following JSON to your findings file (Write tool). It is the shape of ${CLAUDE_PLUGIN_ROOT}/schemas/findings.schema.json; every field is required and every value is a string:

{"summary": "one or two sentences; from round 2 on, then one line per previous-round finding of your role: '<title>: addressed' or '<title>: not addressed'", "findings": [{"severity": "high|medium|low", "title": "short headline", "location": "path:line, or empty string", "detail": "what is wrong and how to fix it", "evidence": "evidence id when the claim rests on an external fact, else empty string"}]}

If there is nothing to report, findings is an empty array. Final message: the summary and the counts only. Do not paste content.

Severity calibration

  • high: must not merge. Incorrect behavior, data loss or corruption, a security vulnerability, or a violation of the design or the Definition of Done.
  • medium: must fix before the task is done. Limited to defects: wrong or fragile behavior in a realistic case, a design or DoD requirement not met, missing validation that lets bad data through, a behavior listed in the DoD with no test, an error silently swallowed.
  • low: everything else, including every matter of preference and style. Always low, never medium: duplicated helpers, "cleaner" dispatch or control-flow shapes, struct fields versus variadic arguments, unused fields or parameters with no behavioral effect, consolidating test helpers, naming and comment wording, file organization. A finding that starts with "consider", "could", or "would be cleaner" is low.

When unsure between medium and low, choose low. Zero medium findings must be reachable: a finding that any code would attract is not a defect.

What to review

The prompt states the round number and two snapshot SHAs: the previous tree (round 1: the tree before the task started; later rounds: the tree reviewed in the previous round) and the tree as it is now. Run git diff <previous snapshot> <current snapshot> and review that diff; it is exactly what the implementer changed (plain git diff would not show new files, and would include earlier tasks' uncommitted changes).

Later rounds

From round 2 on the prompt also names <task>.findings.history.md (all previous rounds' merged findings plus the implementer's stated reasons for anything left unchanged).

  1. Read the history first. Do not contradict a change an earlier round asked for, and do not re-raise an item the implementer declined with a stated reason unless you can show the reason is wrong.
  2. For each previous-round finding from your role, state whether it is addressed in the summary's per-finding lines (see Output format); re-report it only if it is not.
  3. Review only the fix: the snapshot diff above. Behavior changed by the fix counts as part of the fix even where the lines did not change, for example a test that no longer exercises the path it covered. Code the fix did not affect was already reviewed; report something there only as a high (a real defect), never as medium or low.

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.