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Lint skills

Skill event4u-app/agent-config/src/skills/lint-skills

Universal AI Agent OS — audited skills, governance rules, replayable state. One contract, every host agent.

Install
npx -y skills add event4u-app/agent-config --skill lint-skills

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 7 stars7 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when running the package's skill linter against all skills and rules to validate frontmatter, required sections, and execution metadata.

SKILL.md

3.3 KB, 718 tokens by cl100k_base, as published. Nobody here has run it

lint-skills

When to use

Use this skill when:

  • Validating the shape of every skill and rule in .agent-src.uncondensed/
  • Verifying execution metadata (execution.type, handler, command) is well-formed
  • Checking locally before opening a PR that CI's skill-lint job will pass
  • Investigating a reported linter failure on a specific skill or rule

Do NOT use when:

  • Linting only one file — call ./scripts-run src/scripts/skill_linter <path> directly
  • Checking cross-references between files — use check-refs instead
  • Checking condensation freshness — use bash scripts/condense.sh --check instead

Procedure

1. Inspect the environment

Confirm ./scripts-run is executable and the working directory is the agent-config repository root — the linter expects to find src/skills/ and related directories relative to cwd.

2. Dispatch via the runtime layer

Invoke the skill through the runtime dispatcher so the execution: block in this skill's frontmatter governs the call:

./scripts-run src/scripts/runtime_dispatcher run --skill lint-skills

The dispatcher resolves the request, the shell handler runs ./scripts-run src/scripts/skill_linter --all, captures stdout/stderr, and returns a typed ExecutionResult.

3. Verify the result

Check the returned ExecutionResult:

  • status: success and exit_code: 0 → all skills and rules are clean
  • exit_code: 1 → warnings only — review stdout for the listed warnings
  • exit_code: 2 → errors present — fix the flagged files before continuing
  • status: timeout → the linter exceeded timeout_seconds — investigate
  • status: error → the runner could not launch — check that ./scripts-run is on PATH and the repository root is the current working directory

Output format

  1. One-line summary: success | failure | timeout | error, exit code, duration in milliseconds
  2. Count of skills and rules the linter inspected, if known
  3. List of files with errors (first 10), each with code and message
  4. Next action: fix errors, re-run, or surface the raw stdout for review

Gotchas

  • The command uses --all, which walks the full tree — expect several seconds of runtime on a warm repo; bump timeout_seconds if the repo has grown
  • Running outside the agent-config repo root will make the linter report zero skills, which looks like a pass but is actually a no-op
  • Warnings (exit_code: 1) do not fail CI by default; do not dismiss them as "green" when the task is to get to zero warnings

Do NOT

  • Do NOT invoke src/scripts/skill_linter.ts directly when the intent is to test the runtime path — use the dispatcher so the handler and result object are exercised
  • Do NOT raise timeout_seconds to hide a genuinely slow linter pass — investigate the slowdown first
  • Do NOT add shell redirection or pipes to command — the handler runs subprocess.run with shell=False; only argv form is supported

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 327,069. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.