saferskills

saferskills.ai · Methodology · Discussions · Slack · Security

See it in 15 seconds

Quick start

No install needed — npx runs the prebuilt native binary:

npx saferskills info mcp-server-github      # score, four-axis breakdown, findings & report URL
npx saferskills install mcp-server-github   # install to your detected agents — score re-checked, severity-gated
npx saferskills capability ./my-skill       # scan a local file/dir — or a public GitHub URL
npx saferskills agent                       # behaviorally scan a running agent against ~20 adversarial tests

Prefer it installed? npm install -g saferskills or cargo install saferskills. No terminal at all? Everything the CLI does is in your browser at saferskills.ai — browse the catalog, scan a capability, or run a behavioral agent scan, with no account and nothing to install. Full command + flag reference: cli/README.md.

SaferSkills is v0.x, built in the open — the catalog is filling in as ingestion scales toward the public launch.

Why this exists

You install a Claude skill, an MCP server, a Cursor rules file, or a Codex hook. It runs with your file-system access. It can read your .env. It can curl | bash. It can quietly ship your repo to a paste site. And across the tens of thousands of such items now circulating, there is no public, transparent record of what each one actually does.

SaferSkills is that record. Anyone — a developer, a vendor, a researcher — submits a GitHub URL (or uploads a file), and a ~30-second deterministic scan returns a Yuka-style report: an aggregate trust score (0–100), a four-axis breakdown, every detector that fired, the rule that fired it, the exact line of evidence, the remediation, and a permalink the vendor can dispute.

Methodology, not opinion. Every rule is documented. Every score is reproducible. Every appeal is public.

How it works

The verdict path is fully deterministic — there is no LLM deciding your score. Every finding carries a static rule_id and a quotable line of evidence, so a verdict is reproducible from the trace alone. The result is a public report permalink:

Trust-score rubric

Sub-scores are weighted: Identity 25% · Integrity 25% · Behavior 30% · Provenance 20%. A single critical finding floors the aggregate into the red tier regardless of the other axes. Full rubric and every detection rule → saferskills.ai/methodology.

Use it as

Screenshots

Works across 8 agents

One trust layer for every coding agent. Detect, install, and re-verify across:

claude-code · cursor · windsurf · copilot · codex · gemini · cline · openclaw

Teach your agent to use SaferSkills

One command teaches any of the 8 supported agents to use SaferSkills — and to scan a capability before it installs, adds, recommends, or trusts it:

npx saferskills install saferskills

The canonical skill lives at skills/saferskills/SKILL.md and renders to each agent's native format (Claude Code & OpenClaw get the SKILL.md verbatim; Cursor .mdc, Cline/Windsurf rules, Codex/Copilot AGENTS.md, Gemini GEMINI.md get a native render). See the install guide.

⭐ Support the project

If SaferSkills helps you install AI capabilities more safely, star the repo — it's the cheapest way to help a free, public safety service reach the people installing risky skills. Stars are how this gets in front of the next developer about to curl | bash something they didn't read.

Community

• 💬 Discussions — questions, ideas, show-and-tell.
• 🐛 Issues — bugs and feature requests.
• 📐 Rule proposals — propose a new detection rule (RFC required before any rule lands).
• ⚖️ Vendor appeals — dispute a verdict about an item we scanned. Every verified appeal gets a substantive public response within 1 hour.

Develop

git clone https://github.com/OpenLatch/saferskills.git
cd saferskills
pnpm install
pnpm run generate     # 9 generators: ingestion registry + Pydantic + SQLAlchemy + openapi.json + TS DTO + Zod + methodology + agent-pack
docker compose up     # postgres + api + webapp
curl http://localhost:8000/api/v1/health
open http://localhost:5173

Requirements: Node 24 LTS, Python 3.14, pnpm 10, uv 0.7+, Docker.

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