A vague AI agent skill is worse than no skill at all — because the agent pays for it in context budget on every single turn, whether it uses it or not. Yet most of us write SKILL.md files by feel and ship them with zero feedback.
So I built skillscore: a command-line tool that statically analyzes any SKILL.md and gives it a 0–100 quality score, a letter grade, and a list of fix-it findings — each one citing the official authoring guide it comes from.
skillscore is an open-source Dart CLI that lints and scores AI agent skills (
SKILL.mdfiles) against the Claude, Codex, and Antigravity authoring guides. It runs fully offline, is deterministic, and exits with CI-friendly status codes.
TL;DR
- 🎯 Scores any
SKILL.md0–100 with a letter grade and actionable findings across 7 categories. - 📚 Rules are drawn from the official Anthropic (Claude), OpenAI (Codex), Google (Antigravity), and Flutter skill-authoring guides — and every finding cites its source.
- 🔌 Offline, deterministic, zero network calls. Same input → same score, every time.
- 🚦 Built for CI:
--min-score 80, JSON output, and SARIF 2.1.0 that annotates pull requests. - ⚡ Install:
dart pub global activate skillscore→
- 💻 GitHub: /
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