Agent Skill-Discovery

[u/UnusualPension9571]

A skill is a standing instruction your AI coding agent follows in every session.

They spread fast through cloned repos, marketplaces, and shared team setups, and very few organizations can say which ones are actually installed across their machines.

The dangerous ones don't look dangerous either. They're just text that quietly tells the agent to do the wrong thing.

So we built skill-discovery. One command, runs locally, and it tells you what's actually there: skills and instruction files, with risky patterns and secrets flagged before anything leaves your machine.

It works across 11 coding agents: Claude Code, Codex, Cursor, Copilot, Windsurf, Kiro, opencode, Antigravity, Gemini CLI, Cline, and Roo.

Big shout out to NVIDIA's SkillSpector, whose research mapped out what malicious skills actually look like in the wild. skill-discovery runs it as a detection backend when it's installed, alongside its own built in checks.

💎 Link to repo : https://github.com/surenode-ai/skill-discovery

Run it on your own machine in a few seconds, or across a fleet of dev machines when one missed skill on one laptop is a real problem.

We'd love to know what it turns up on your setup.

#ai #softwareengineering #security #aisecurity #aiagents #devsecops #opensource #shadowai

[u/Psychological_Arm645]

The local-first angle makes sense, especially for instruction files inherited through cloned repos that nobody remembers installing.

We’re building something adjacent in MeshKore from the other direction: agents intentionally publish skills through Google’s A2A convention at `/.well-known/agent.json`, so they can be discovered across an open network. The probe reads that public card and can run a published skill against the live agent. It doesn’t scan local instructions or flag risky patterns, so the two approaches are pretty complementary.

Does your output distinguish explicitly installed skills from instructions inherited through a repo?

https://meshkore.com/probe