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vladcheckvladcheck/agentic/skills/mattpocock-skills-research/SKILL.md

mattpocock-skills-research

Skill1 starsChanged 12 days agoMITView on GitHub

Use when evaluating upstream agent skills for adoption with unclear maturity, maintenance, or issues. Not for authoring, fixes, or repo reviews.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

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

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    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:

    Read the relevant README files, contribution or support docs, release notes, package or plugin manifests, distribution configuration, and …

    line 16
  2. Distinctive:

    Search community reports for each candidate and its relevant workflow terms:

    line 20
  3. Distinctive:

    Follow the comments URL for discussion when the body does not resolve the report.

    line 28

≈ “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

Research upstream skills

  1. Identify the upstream as <owner>/<repo>, the candidate skill names, and the adoption question. Inspect local copies first, starting with the default ~/.claude/skills/<name>/SKILL.md and any configured runtime-specific skill roots; record differences from upstream rather than assuming local files are current.
  2. Establish repository context and read its own documentation before interpreting directory names:
    gh repo view <owner>/<repo> --json nameWithOwner,description,defaultBranchRef,url
    gh api repos/<owner>/<repo>/contents --jq '.[].path'
    gh api repos/<owner>/<repo>/contents/<path> -H "Accept: application/vnd.github.raw+json"
    Read the relevant README files, contribution or support docs, release notes, package or plugin manifests, distribution configuration, and the candidate skill's metadata.
  3. Derive each skill's status from repository-specific evidence. Distinguish documented stable, beta, experimental, in-progress, deprecated, and undocumented states using docs, layout, distribution metadata, and releases together. Treat directory names and inclusion in a distributed package as signals only when that repository explains their meaning; label conflicting or missing evidence as unknown.
  4. Search community reports for each candidate and its relevant workflow terms:
    gh search issues --repo <owner>/<repo> "<skill-name>" --limit 100 --json number,title,state,url
    Repeat with aliases and symptom keywords when needed. Review matching titles first, then inspect relevant issues or pull requests individually:
    gh api repos/<owner>/<repo>/issues/<number> --jq '{title,state,body,html_url,comments_url}'
    Follow the comments URL for discussion when the body does not resolve the report. Separate confirmed defects, limitations, questions, and already-fixed reports; do not treat search counts or absent issues as proof of adoption, quality, or stability.
  5. Compare the local and upstream evidence: declared status, distribution, maintenance activity, unresolved impact, relevance to the intended use, and local modifications. Mark stale local findings and inaccessible or ambiguous sources explicitly.
  6. Synthesize one concise recommendation per skill: adopt, adopt with constraints, or defer. Cite the inspected documentation and issue URLs for each material claim, state residual uncertainty, and base adoption on fitness plus corroborating evidence rather than popularity or issue absence alone.

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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.