skillfrisk 0.2.0

Static security scanner for AI-agent skills and MCP servers.

skillfrisk

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

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: "."

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 an HTML report:

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

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 and PyPI publication are planned but not included in this first version.

Roadmap

• SARIF reporter for GitHub code scanning.
• Dedicated JavaScript/TypeScript AST rules.
• Rule configuration file with allowlisted paths.
• Signed rule bundles and reproducible release workflow.

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☕ Support the author

This project is free and MIT-licensed. If it saved you time, you can send a coffee — it directly funds the next feature.

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