Most Agent Skills don't load. We wrote three that do.
There are hundreds of thousands of free Agent Skills now, and a lot of them quietly fail — bad frontmatter, oversized bodies, or worse. Here's what separates a skill that works from one that just sits there, and three we open-sourced to prove it.
An Agent Skill is a folder with a SKILL.md file that teaches an AI agent how to do one task well. The format is a genuinely good idea — write a workflow down once, and it works across Claude Code, Codex, Cursor, Copilot, Gemini CLI, and dozens of other agents. So people wrote a lot of them. Vercel's skills.sh directory alone lists hundreds of thousands.
The catch is that a skill is just plaintext, which means it's as easy to publish a broken one as a working one. Snyk scanned around 4,000 public skills and found 36% had at least one security flaw. A big share of the rest fail for a duller reason: they don't load at all.
The ways a skill quietly fails
A skill doesn't error loudly when it's wrong. It just never activates, or loads and does nothing useful. The usual causes are boring and repeatable:
- The
namedoesn't match the folder, or has an uppercase letter or an underscore. The spec allows lowercase letters, numbers, and single hyphens only. Break that and the agent skips it. - The description is vague. At startup an agent sees only each skill's
nameanddescription— nothing else. "Helps with PDFs" never wins against a real task. The description has to say what the skill does and when to reach for it, in the words a user would actually type. - The body is a wall. The whole
SKILL.mdloads into context on activation, so a 2,000-line skill is a tax on every run. Good skills stay under ~500 lines and push detail into referenced files that load only when needed.
None of this is hard. It's just unglamorous, and nobody checks it — so a fifth of the corpus doesn't work.
What we did about it
We build and run a fleet of agents on our own systems, so we already had skills we trusted from daily use. We cleaned up three, made them MIT, and — the part that matters — shipped the checker too.
- skill-author teaches an agent to write a spec-valid
SKILL.md, and it bundles a dependency-freevalidate.py. Point it at any skill, ours or yours:python3 validate.py path/to/skill. It catches exactly the frontmatter and sizing mistakes above. - commit-hygiene turns a messy working tree into clean, one-concern-per-commit history with messages that explain why, not just what.
- repro-first-debugging makes the agent reproduce a failure before touching code, then prove the fix against that same repro — the discipline that stops "try this and see" loops.
All three pass their own validator. That's the whole point: a skill you can check is a skill you can trust.
# Claude Code
/plugin marketplace add xentropyai/skills
# any agent
npx skills add xentropyai/skills
The repo is github.com/xentropyai/skills. It's free and it stays free.
Where this goes
Free skills are a commodity — the text is trivially copyable, and the standard is open. What isn't a commodity is a skill that stays correct: re-tested as new models ship, with published evals and a changelog, so it doesn't silently rot the way a static file does. That's the line we're building next, for the higher-stakes workflows where "probably fine" isn't good enough.
If a tested skill that stays current is worth more to you than a free one you maintain yourself, join the waitlist. Either way, the three above are yours.