I built a Open Source local CLI to audit the AI tools, MCP configs, and agent skills on your machine

Disclosure: I’m the founder of Guard0 and one of the maintainers of g0.

Four months ago, I asked this community what was actually blocking AI agents from reaching production. The discussion kept returning to visibility, permissions, auditability, and continuous monitoring.

We built an open-source, local CLI to tackle the developer-machine and repository side of that problem:

The Nx supply-chain attack specifically searched infected developer machines for Claude, Gemini, and Q configuration files and authentication tokens. More recently, researchers found hundreds of malicious skills in the ClawHub marketplace.

That left us with a basic question that was surprisingly difficult to answer:

What AI tooling is configured on this machine, and what in that setup should be investigated first?

We added a one-command background check to g0:

npx -y /g0 check

It requires no account. The check runs locally and does not use an LLM.

It inspects supported AI development tools and MCP configuration locations, installs OpenClaw skills, and local process and network posture. It returns an A–F grade with the main reasons behind it.

The larger CLI also includes:

g0 scan .          # static assessment for agent code
g0 test            # red-team an agent or MCP server
g0 proxy install   # observe or enforce policy on MCP traffic

This is not a claim that static inspection can prove an agent is safe. Indicator matching can miss new attacks and produce false positives, and the runtime proxy can only inspect traffic routed through it.

I’m sharing it because I would like people here to challenge it. The most useful feedback would be incorrect classifications, unsupported clients or configuration paths, and concrete ways to evade the threat model.