agentleFS
TheDecipheristTheDecipherist/claude-code-mastery-project-starter-kit/.claude/skills/dependency-vetting/SKILL.md

dependency-vetting

Skill337 starsChanged 5 months agoMITView on GitHub

Vet an npm (or other) package for supply-chain risk before it's added to a project. Use when asked whether a package is safe, trustworthy, or worth adding, when about to install an unfamiliar dependency, or when something about a package feels off. Runs isolated and read-only, reports an evidence-backed risk verdict, does not install anything.

  • Pipes a download into a shell

2 other projects use 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, 2 are distinctive, and 1 is 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.

  • 1

    Very specific

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

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

    Install scripts: Read package.json for preinstall, install, postinstall.

    line 22
  2. Distinctive:

    What it actually does vs. claims: Read the real entry points.

    line 24
  3. Distinctive:

    Never soften a real finding to be agreeable, and never inflate a thin one into an accusation.

    line 46

≈ “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

Vet a Dependency

npm audit catches known CVEs in packages you already trust. This is the other problem: deciding whether to trust a package in the first place. The goal is an evidence-backed verdict, not a vibe.

What to gather

Pull the facts before judging. Use npm view <pkg>, the npm and GitHub pages, and the published tarball.

  • Downloads vs. stars vs. dependents. A package with thousands of weekly downloads, single-digit stars, and no dependents is a mismatch worth explaining. Real adoption shows up in all three. Check the download shape too: organic growth is a curve; isolated single-day spikes with dead baseline around them are manufactured.
  • Install scripts. Read package.json for preinstall, install, postinstall. Anything that runs on install is the highest-risk surface, read exactly what it does.
  • curl | sh and friends. Any install step that pipes a remote script into a shell, or npx-runs a second package, or registers a persistent MCP server, is a red flag. The package should do what its README says and nothing the README doesn't.
  • What it actually does vs. claims. Read the real entry points. Network calls, child_process, filesystem writes outside its own dir, env/credential reads, and telemetry that the README doesn't disclose are all findings.
  • Obfuscation. Minified-only source with no readable repo, eval, base64 blobs, or dynamic require of constructed strings. Legitimate packages ship readable code.
  • Maintainer and provenance. Account age, other packages, repo activity, whether the npm package's repo link actually matches a real, public repo. A brand-new author with one oddly-popular package warrants more scrutiny.
  • Typosquatting. Is the name one character off a popular package? Is it impersonating a known scope?
  • License sanity. Missing, contradictory, or relicensed-from-someone-else (a file marked MIT that's actually CC-BY upstream) is both a legal and a trust signal.

How to judge, and how to phrase it

Separate what the evidence supports from what it doesn't. This is the discipline that keeps a verdict defensible:

  • You can say a package behaves in ways that warrant caution and point at the exact behavior (an undisclosed postinstall, a downloads/stars mismatch, an undisclosed network call). That's an evidence claim.
  • You generally cannot say it "is malware" or attribute it to a specific actor or coordinated scheme. Intent and authorship usually aren't provable from the artifact. Don't claim what you can't show.
  • Stars and dependents are a truer adoption signal than downloads, which are cheap to manufacture. Weight accordingly.

Output

A short report:

  • Verdict: Safe to add / Add with caution / Avoid.
  • Evidence: the specific findings, each tied to what you observed, in order of severity.
  • If "caution" or "avoid": what would have to change to trust it, or a safer, more-adopted alternative.

Never soften a real finding to be agreeable, and never inflate a thin one into an accusation. Report what the artifact shows.

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.