agentleFS
zerniezernie/vigiles/examples/SKILL.md

pr-to-lint-rule

Skill15 starsChanged 5 months agoMITView on GitHub

Convert a recurring PR review comment into an automated lint rule with tests and CLAUDE.md annotation

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Look at the repository to determine:

    line 27
  2. Very specific:

    Add the annotation block. See docs/spec-format.md for the correct format.

    line 44
  3. Very specific:

    Run npm test to ensure the new rule and tests pass.

    line 48

≈ “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
<!-- vigiles:sha256:49b12ebc0cf20852 compiled from examples/SKILL.md.spec.ts -->

Convert a recurring PR review comment into an automated lint rule.

Arguments

$ARGUMENTS — A natural language description of the pattern to enforce. Examples:

  • "we keep telling people not to import directly from antd, use our design system barrel file instead"
  • "people forget to use our custom logger instead of console.log"
  • "don't use unwrap() in production code, use expect() or proper error handling"
  • "API route handlers must use the withAuth wrapper"

Instructions

You are generating an automated lint rule from a recurring code review pattern. Follow these steps:

Step 1: Detect the Project Language and Toolchain

Look at the repository to determine:

  • Primary language (JS/TS, Python, Rust, Go, Ruby, etc.)
  • Linter in use (ESLint, Ruff, Clippy, golangci-lint, RuboCop, etc.)
  • Testing framework (Vitest, Jest, pytest, cargo test, etc.)
  • Existing custom rules (to match conventions)

If the language or linter cannot be confidently detected, ask the user which language and linter to target before generating anything.

Step 2: Generate the Lint Rule

Based on the detected (or user-specified) language, generate the appropriate rule type.

For JavaScript/TypeScript, generate an ESLint rule using the AST visitor pattern with meta and create(context).

Step 3: Update CLAUDE.md

Add the annotation block. See docs/spec-format.md for the correct format.

Step 4: Verify

Run npm test to ensure the new rule and tests pass.

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.