Legwork

Point it at a GitHub repo, get a working MCP tool back.

Legwork reads a repo's README, has your LLM write an MCP wrapper for it, installs it in a sandbox, and proves it runs before handing it to Claude Code, Cursor or any other MCP client. If a repo can't be wrapped, it says why instead of producing something broken.

uvx legwork-mcp owner/repo

Proof, not a pitch

On 2026-09-26 we ran Legwork against this week's 10 most-starred new AI repos on GitHub, taken exactly as search ranked them, with nothing skipped for being hard:

Two of the week's top-10 "AI tools", about 700 stars each and three days old, carried the same byte-identical obfuscated dropper under different file names. Legwork's pre-install scan refused both in about a second. Nothing from them was installed or run. Stars are not a trust signal.

Full write-up, including what failed along the way and what we fixed: docs/designs/legwork-trending-trial-2026-09-26.md.

Quick start

You need macOS, uv, and any OpenAI-compatible chat-completions endpoint. Legwork itself is free; the only cost is your own model usage, which is 1–3 calls per build.

export LEGWORK_LLM_ENDPOINT=https://api.openai.com/v1
export LEGWORK_LLM_API_KEY=...        # your own key; never a CLI flag
export LEGWORK_LLM_MODEL=gpt-5

uvx legwork-mcp 2akouwu/reverify

A build takes 1–3 minutes and ends with the line to connect it:

Built an MCP wrapper for 2akouwu/reverify on attempt 2 of 3.
  What it wraps: Verify claims about binaries using Reverify's deterministic tools (wraps `reverify verify --json`)

Connect it to Claude Code:
  claude mcp add reverify -- ~/.local/bin/uvx legwork-mcp serve 2akouwu/reverify

It also prints an mcpServers block for Claude Desktop, Cursor and other clients. For a permanent legwork command: uv tool install legwork-mcp.

Repos already in the public cache need no model call and no API key. uvx legwork-mcp 2akouwu/reverify works as is.

How it works

0. Cache. If the repo is in the public cache, Legwork uses that wrapper instead of steps 3–4. It still clones and scans the repo, scans the cached wrapper too, and installs and self-tests it locally; if the self-test fails, it writes a fresh wrapper. --no-cache skips the cache.
1. Fetch. Checks the repo is public and reachable, then shallow-clones it.
2. Scan. Statically scans every Python and JavaScript/TypeScript file for obfuscated payloads: XOR-decoded byte arrays, computed imports, javascript-obfuscator output, eval of decoded strings, code hidden off-screen behind whitespace. A hit stops everything before install.
3. Read. The README, plus setup docs it links to (install.md, docs/getting-started.md, …), capped at 50KB with install and usage sections kept first.
4. Write. Your model writes the install command and a Python MCP wrapper with a self-test, or refuses with a specific reason: no programmatic entrypoint, needs hardware, needs its own credentials, needs a toolchain the sandbox doesn't have, and so on.
5. Install, sandboxed, network on.
6. Self-test, sandboxed, network off. A failure goes back to the model and it tries again, up to 3 attempts and 10 minutes.
7. Serve. legwork serve owner/repo runs the wrapper as an MCP server over stdio, sandboxed, network off.

What "built" means

The self-test calls the simplest documented command and checks the shape of its result. So "built" means the wrapper starts and its simplest tool works. It does not mean every tool works. In the trial:

• golive-skill's wrapper exposes its read-only commands, not its deploy flow, which needs accounts.
• AnyJev's decision tool needs a running vLLM server, which the self-test didn't have.

Tools that must reach the network at run time won't work under serve, because the network is off.

Risks, stated plainly

Legwork runs code you didn't write: the target repo's install steps and a wrapper an LLM wrote from a README a stranger wrote. What contains it:

• Install time is the biggest risk. Package installs and install.sh scripts run with network on, because they have to. The sandbox limits them to their own working folder: no access to your home directory (SSH keys, cloud credentials, dotfiles), no writes outside the folder, and an environment with none of your variables (including your model key). A malicious package can still misbehave inside that folder and reach the network during install.
• The scan covers the repo's source, not what installers download. magpie, for example, installs with the vendor's curl … | sh. For downloaded code, the sandbox is the protection.
• Prompt injection is not mitigated. A README can steer the model into writing a wrapper that does something other than what you asked for. The wrapper still runs sandboxed with network off, which bounds the damage; it doesn't prevent a wrong or misleading tool.
• The scanner is heuristic. It catches the obfuscation patterns seen in real payloads so far, and a determined author can get past it.

The sandbox is macOS sandbox-exec. If it isn't available, Legwork refuses to run. It never falls back to running unsandboxed.

Commands

Builds live in ~/.legwork (override with LEGWORK_HOME). A failed build saves its full error output to attempts.log in its build folder.

Contributing a wrapper

legwork contribute owner/repo writes cache/<owner>__<repo>/ containing wrapper.py and manifest.json, and prints a PR description. Before writing anything, it:

• blocks the entry if the wrapper copies 50+ words in a row from the source repo
• re-runs the self-test in the sandbox
• blocks the write if any output contains something shaped like an API key, including your own configured key

A GPL, AGPL, missing or unrecognized source license is flagged in the manifest and the PR description, but not blocked. If the repo has commits newer than the build or the existing entry, you get a warning. Open the PR yourself; a bad entry is removed with a plain git revert.

Status

Early. What's next:

• Linux sandbox. Legwork is macOS-only today.
• Better multi-tool verification than a single simplest-command self-test.

Development

See CONTRIBUTING.md. Security reports: SECURITY.md.

uv sync
uv run pytest        # the sandbox and integration tests need macOS

tests/fixtures/ai_data_extractor_payload.py is a real malicious file with its payload destroyed (every encoded byte randomized, code shape kept), so the scanner is tested against a real technique. Tests only parse it with ast; see tests/fixtures/README.md.

License

MIT