agentleFS
reliable-coding-agentsreliable-coding-agents/reliable-python/skills/review-code-quality/SKILL.md

review-code-quality

Skill1 starsChanged 2 months agoMITView on GitHub

Use when auditing Python source, diffs, pull requests, or refactors for reliability, maintainability, SOLID, explicit program structure, or type-contract risks.

1 other project uses a version of this.

Adopted by

Most recently taken up by

  • Other versions1 project uses a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 1

    1. codingpains/skills★ 1same 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 repository7 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 6 are distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read repository guidance and identify the requested change, affected public behavior, and changed lines.

    line 17
  2. Distinctive:

    Run the bundled checker from the repository root:

    line 20
  3. Very specific:

    Add --docstring-style google|rest|any when the project does not use the NumPy default.

    line 27
  4. Very specific:

    Check the documentation convention on changed public units (DOC01-DOC03).

    line 40
  5. Very specific:

    Read ../writing-docstrings/SKILL.md before reporting a docstring finding.

    line 41
  6. Distinctive:

    Run the repository's own formatter, linter, type checker, and focused tests.

    line 44
  7. Distinctive:

    Do not substitute the bundled checker for project validation.

    line 45
  8. Distinctive:

    If asked to fix findings, make the smallest behavior-preserving change, rerun checks, and re-review the resulting diff.

    line 46
  9. Very specific:

    Use error for a clear rule violation or correctness risk, warning for a confirmed smell with material cost, and note for an explicit, …

    line 62
  10. Distinctive:

    If no findings remain, say so and list the checks actually run.

    line 67

≈ “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

Review Code Quality

Produce an evidence-based review of the changed scope. Separate mechanical rule violations from heuristic design smells and avoid speculative findings.

Review workflow

  1. Read ../using-power-of-ten/references/power-of-ten.md, ../using-power-of-ten/references/code-smells.md, ../applying-solid-principles/SKILL.md, and ../writing-maintainable-python/SKILL.md.
  2. Read repository guidance and identify the requested change, affected public behavior, and changed lines. Review the diff first; open surrounding code only as needed to prove a finding.
  3. Run the bundled checker from the repository root:

    python3 <skill-directory>/scripts/audit_python.py --git-diff

    For explicit files or directories, replace --git-diff with their paths. Add --docstring-style google|rest|any when the project does not use the NumPy default. The checker covers only high-signal, statically detectable cases; it does not replace the semantic pass.

  4. Trace the ten rules in order. For Rules 3, 8, and 9, label conclusions as Python-profile findings rather than literal C-rule compliance.
  5. Review PY001-PY005: import safety, explicit entry-point assembly, function responsibility, public annotations, and comprehension readability.
  6. Review SOLID01-SOLID05 against demonstrated clients, extension points, substitution contracts, interface use, and dependency direction. Treat SOLID03 checker output as evidence, not proof of the other principles.
  7. Review the touched design against all five smell families. Report a smell only when the code shows the catalog's signal and a concrete maintenance or correctness cost.
  8. Check the documentation convention on changed public units (DOC01-DOC03). Read ../writing-docstrings/SKILL.md before reporting a docstring finding. A summary-only docstring satisfies every supported style and is not a finding on its own.
  9. Run the repository's own formatter, linter, type checker, and focused tests. Do not substitute the bundled checker for project validation.
  10. If asked to fix findings, make the smallest behavior-preserving change, rerun checks, and re-review the resulting diff.

Finding standard

Report only findings that are actionable and caused or exposed by the changed scope. Use:

[severity] ID — concise title
location: path:line
evidence: what the code demonstrably does
impact: concrete failure or maintenance cost
remedy: smallest safe correction

Use error for a clear rule violation or correctness risk, warning for a confirmed smell with material cost, and note for an explicit, justified tradeoff. Do not report style preferences, existing unrelated debt, or a smell name without evidence.

If no findings remain, say so and list the checks actually run. Never imply that this review proves safety or exhaustively detects every smell.

Suppressions

Honor # quality: ignore[ID] - rationale only when it is on the finding line or immediately above the relevant statement and contains a specific rationale. Challenge broad, stale, or circular rationales. Prefer correcting code over adding a suppression.

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.