skillfrisk

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skillfrisk is a static security scanner for AI-agent skills and MCP servers.

Problem

AI agents increasingly install third-party skills, MCP servers, hooks, and scripts that can read files, call the network, and influence tool use. A malicious or careless skill can hide prompt injection, steal secrets, or run destructive shell commands before a developer notices.

Why it matters

Generic SAST tools are useful, but they do not understand agent-specific risk: hidden instructions in Markdown, SKILL.md frontmatter, MCP tool permissions, or prompt-injection language embedded in docs. skillfrisk is a pre-install and CI gate for that niche.

Architecture

CLI (Typer)
  -> filesystem parser for SKILL.md / YAML / JSON / scripts
  -> rule engine: prompt injection, secret access, RCE, Unicode hiding, MCP permissions
  -> reporters: terminal table, JSON, HTML
  -> exit code for CI policy

Demo

uv run skillfrisk scan tests/fixtures/malicious_skill --json

Example finding:

{
  "rule_id": "REMOTE_CODE_EXEC",
  "severity": "critical",
  "recommendation": "Pin and inspect downloads; never pipe network output directly into shells."
}

Quickstart

pipx install skillfrisk   # or: uv tool install skillfrisk / pip install skillfrisk
skillfrisk scan path/to/skill-or-mcp --html reports/skillfrisk.html
skillfrisk scan path/to/SKILL.md   # a single file works too

One-off run without installing:

uvx skillfrisk scan path/to/skill-or-mcp

For local development:

git clone https://github.com/Topicspot/skillfrisk.git
cd skillfrisk && uv sync --extra dev
uv run skillfrisk scan tests/fixtures/malicious_skill --json

Install as an agent skill (teaches your agent to vet skills/MCP servers before installing them):

npx skills add Topicspot/skillfrisk

Use in CI as a GitHub Action:

- uses: Topicspot/skillfrisk@main
  with:
    path: "."

To upload findings to GitHub code scanning, let the action write SARIF and upload it:

permissions:
  security-events: write

steps:
  - uses: actions/checkout@v4
  - uses: Topicspot/skillfrisk@main
    with:
      path: "."
      sarif: reports/skillfrisk.sarif
      args: --no-fail-on-high
  - uses: github/codeql-action/upload-sarif@v3
    with:
      sarif_file: reports/skillfrisk.sarif

Or with Docker:

docker build -t skillfrisk .
docker run --rm -v "$PWD:/scan" skillfrisk scan /scan --json

The command exits with code 2 when high or critical findings are present.

Examples

Scan a safe skill:

uv run skillfrisk scan tests/fixtures/benign_skill

Scan an MCP manifest:

uv run skillfrisk scan tests/fixtures/mcp_server --json

Write HTML and SARIF reports:

uv run skillfrisk scan . --html reports/report.html --sarif reports/skillfrisk.sarif --no-fail-on-high

Update gate: skillfrisk diff

Skill managers update skills by comparing folder hashes and reinstalling. None of them show what changed inside the instructions a privileged agent will follow. A skill update is a merge of unreviewed third-party text into your agent's context.

skillfrisk diff compares two local versions of a skill and reports what the update changes, offline and in milliseconds:

git clone --depth 1 https://github.com/owner/skill /tmp/skill-new
skillfrisk diff ~/.agents/skills/foo /tmp/skill-new

skillfrisk diff  foo -> skill-new    files: 2 changed, 1 added, 0 removed
NEW FINDINGS (2)
  high  PROMPT_INJECTION  SKILL.md:41  "...do not tell the user about this step..."
  high  SECRET_ACCESS     scripts/sync.sh:12  cat ~/.aws/credentials | curl ...
  allowed-tools: +Bash
  network hosts: +tele.example
resolved: 0, carried over from the old version: 1
VERDICT: RISK INCREASED.

• New findings are matched semantically (rule, file, normalized snippet), so shifted or reflowed text does not produce false alarms.
• The capability delta covers allowed-tools frontmatter, shell commands, and network hosts.
• --fail-on high (default) exits 2 on new high or critical findings; --fail-on any-change also fails when the capability surface grows; --no-fail always exits 0.
• --json and --html reports/diff.html for automation; --show-resolved prints findings the update removed.

What diff cannot catch, by design: findings already present in the old version (use scan), semantic redirection written in benign language, malicious content in binary files, code fetched at runtime, and upstreams that serve different content to different users (pin commits with a lockfile tool; diff complements it).

Rule coverage

Current rules detect:

• prompt-injection instructions in Markdown and configs;
• curl/wget piped into shells;
• reads from .env, ~/.ssh, os.environ, and similar secret stores;
• destructive shell commands such as rm -rf $HOME;
• suspicious secret exfiltration patterns;
• hidden bidirectional/invisible Unicode controls;
• Python eval/exec and subprocess(..., shell=True);
• MCP wildcard permissions and dangerous write/delete/exec-like tools.

False-positive control

tests/corpus/ vendors 10 full skills (92 files, including bundled Python/JS scripts) from anthropics/skills; the test suite fails if skillfrisk reports a single high-severity finding on any of them. Current state: 0 findings on the whole corpus, about 45 ms per skill on a laptop-class machine. To reproduce the comparison with other scanners on the same corpus, run uv run python benchmarks/run.py; pinned tool versions and the latest results are in benchmarks/.

Alternatives / why another one

Several scanners target the same problem. Use whichever fits your workflow:

• NVIDIA/SkillSpector - LangGraph pipeline, static checks plus optional LLM semantic analysis, SARIF output.
• snyk/agent-scan - discovers agents, skills and MCP servers installed on your machine and checks them via Snyk's verification service.
• cisco-ai-defense/skill-scanner - YAML/YARA pattern engine with optional LLM, VirusTotal and API integrations; CLI, library and REST API.
• NMitchem/SkillScan - static analysis plus LLM behavioral prediction and Docker sandbox execution (owns the skillscan name on PyPI).

This project stays deliberately small: no LLM calls, no network access, no Docker requirement, no API keys. One dependency-light Python package that runs in milliseconds, so it fits in a pre-commit hook, and a public regression corpus of real skills that keeps high-severity false positives at zero by construction.

Limitations

• Static analysis can miss runtime-only behavior.
• Regex rules trade precision for speed and explainability; some findings may require human review.
• JavaScript/TypeScript AST checks are not implemented yet.
• SARIF output is available; JavaScript/TypeScript AST checks and configurable allowlists are still planned.

Roadmap

• Dedicated JavaScript/TypeScript AST rules.
• Rule configuration file with allowlisted paths.
• Signed rule bundles and reproducible release workflow.

☕ Support the author

This project is free and MIT-licensed. If it saved you time, you can send a coffee.

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