# skillscore: a CLI that scores your AI agent's SKILL.md 0–100 > Source: > Published: 2026-06-12 23:27:22+00:00 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 (It runs fully offline, is deterministic, and exits with CI-friendly status codes.`SKILL.md` files) against the Claude, Codex, and Antigravity authoring guides. `SKILL.md` `--min-score 80` , JSON output, and `dart pub global activate skillscore` → AI agent skills are quietly becoming a standard. A *skill* is just a folder with a `SKILL.md` — YAML frontmatter (a `name` and a `description` ) plus a Markdown body of instructions — and optional `references/` , `examples/` , `scripts/` , and `assets/` subfolders. Claude Code, Codex, Antigravity, Gemini CLI, and Cursor all read the same format. Here's the catch that most people miss: **an agent keeps every skill's name and description in its context window permanently**, so it can decide when to reach for one. A skill with a fuzzy description doesn't just fail to get used — it taxes every prompt and occasionally fires on the wrong request. The vendors all published authoring guides telling you how to avoid this: front-load triggers, write in the third person, state when *not* to use the skill, keep the body short, document your scripts. Good advice — scattered across four different documents, none of them enforceable. There was no `eslint` for skills. So I wrote one. skillscore is a **skill linter and SKILL.md validator** that turns those authoring guides into 24 concrete, checkable rules. Point it at a file, a skill folder, or a whole monorepo, and it produces a score per skill: ``` # Install (it's on pub.dev) dart pub global activate skillscore # Score a single skill — any name, any location skillscore path/to/SKILL.md # Score every skill in a tree skillscore path/to/skills/ ``` The rules live in 7 weighted categories: | Category | What it checks | |---|---| A — Frontmatter validity | `---` delimiters, `name` format, `description` present | B — Description quality | states what + when, third person, front-loaded triggers, boundary clause | C — Conciseness | body length, no explainer bloat, no endless "or" chains | D — Structure | progressive disclosure, links one level deep, TOCs on long references | E — Instruction quality | anti-patterns, workflow checklist, feedback loop, code examples | F — Content hygiene | no rotting date references, forward-slash paths, consistent terms | G — Safety & scripts | a penalty (up to −15) when bundled scripts lack docs or a Safety section | 100 points are distributed across A–F; category G only bites if your skill ships scripts or terminal commands. Profiles that exclude a vendor-specific rule are normalized back to 0–100, so a score means the same thing on every target. Here's skillscore run against a genuine skill from the **Flutter team's public repo** — `flutter-add-widget-test/SKILL.md` : ``` flutter-add-widget-test (SKILL.md) Score: 90/100 Grade: A A Frontmatter validity 15/15 ██████████ B Description quality 21/25 ████████░░ C Conciseness & token economy 15/15 ██████████ D Structure & progressive disclosure 15/15 ██████████ E Instruction quality 14/20 ███████░░░ F Content hygiene 10/10 ██████████ G Safety & scripts no penalty WARNING E1_anti_patterns line 8 Body contains no explicit anti-patterns (no "do not", "never", or "avoid"). fix: Add explicit prohibitions, e.g. "Never share a WidgetTester across tests." INFO B5_boundary_clause line 3 Description has no boundary clause saying when NOT to use the skill. fix: Append a boundary, e.g. "Do not use for multi-screen integration tests." ``` A genuinely good skill, and skillscore says so — but it also pinpoints the two things keeping it off a perfect score: it never tells the model what *not* to do, and its description doesn't state where the skill stops. Both are real, both are fixable in one line, and both come straight from the published guides. Want the rationale behind any finding? Ask: ``` skillscore explain E1_anti_patterns ``` It prints why the rule exists, the exact fix, and the source guide it's from. A score you have to eyeball isn't a gate. skillscore is designed to live in a pipeline: ``` # .github/workflows/skills.yml - name: Lint agent skills run: | dart pub global activate skillscore skillscore skills/ --min-score 80 --no-color ``` `--min-score 80` → the job `--format json` → structured output for dashboards.`--format sarif` → valid Exit codes are pipeline-grade: `0` everything passed, `1` a gate failed, `2` a usage error. No flaky LLM in the loop, no network — the same skill always scores the same. | skillscore | Vendor schema check | Markdown linter | "Ask an LLM" | | |---|---|---|---|---| | Validates frontmatter | ✅ | ✅ | ❌ | ⚠️ | Scores quality (discoverability, structure, instructions) | ✅ | ❌ | ❌ | ✅ | | Cites a source guide per finding | ✅ | ❌ | ❌ | ❌ | | Deterministic / reproducible | ✅ | ✅ | ✅ | ❌ | | Safe for a CI gate | ✅ | ✅ | ✅ | ❌ | | Offline | ✅ | ✅ | ✅ | ❌ | An LLM review is great for nuance but non-deterministic — you can't gate a build on it. A schema check tells you the file is *valid*, not whether it's any *good*. skillscore fills the gap in the middle, and it pairs nicely with the other two. The CLI is a thin wrapper over a public Dart API, so you can embed scoring in your own tooling: ``` import 'package:skillscore/skillscore.dart'; void main() { final doc = SkillParser().parseFile('my-skill/SKILL.md'); final result = Scorer(RuleRegistry()).score(doc, Target.universal); print('${result.score}/100 ${result.grade}'); } ``` **What is an AI agent skill?** A folder with a `SKILL.md` manifest (YAML frontmatter + Markdown instructions) that teaches an AI agent a repeatable task. Optional subfolders hold references, examples, scripts, and assets. The format is shared across Claude Code, Codex, Antigravity, Gemini CLI, and Cursor. **Which agents does skillscore support?** All of them — the `SKILL.md` format is shared. Score against one vendor with `--target claude|codex|antigravity` , or use the default `universal` profile, which a portable skill should pass everywhere. **Is it really offline?** Completely. No network calls at runtime, local files only, fully deterministic — the same input always produces the same score and the same finding order. **Does my skill have to be named a particular way?** No. skillscore is name-agnostic: the frontmatter `name` , the folder name, and the file name are independent, and even non-ASCII folder names work. Rule `A2` will still tell you if the `name` *field* breaks the official format. **What happens with malformed frontmatter?** No crash. The relevant frontmatter errors are reported, every other rule that can still run does, and you always get a score. v0.1.0 is live and the rubric is stable, but it's early. The roadmap: more vendor targets as new guides land, an autofix mode for the mechanical findings (forward slashes, missing TOCs), and a GitHub Action wrapper so CI setup is one line. The rule engine is deliberately simple — **a new rule is one class plus one registration** — so contributions are welcome, and every rule must cite the published guide it enforces. ``` dart pub global activate skillscore skillscore your-skill/ ``` **skillscore** statically analyzes any AI agent skill — a `SKILL.md` manifest and its folder — and produces a **0–100 quality score**, a **letter grade** and a list of **actionable findings**, scored against the official skill authoring guides from **Anthropic (Claude)**, **Google (Antigravity)**, and **OpenAI (Codex)**. Offline, deterministic, CI-friendly. skillscore is a **skill linter / SKILL.md validator / agent-skill quality checker / AI skill scorer**. Agent skills are an open standard — a folder with a `SKILL.md` (YAML frontmatter + Markdown body) plus optional `references/` , `examples/` , `scripts/` , and `assets/` — used by Claude Code Codex, Antigravity, Gemini CLI, and Cursor. Because an agent keeps every skill's `name` and `description` in its context budget permanently, **a vague or malformed skill is worse than no skill**. skillscore catches exactly those… If you maintain skills, run it against your `SKILL.md` and tell me what score you get — and what it got *wrong*. I want the rules to reflect how people actually author skills, so findings you disagree with are the most useful feedback I can get. And if it saves you a context-budget headache, a ⭐ helps it reach other people building agents.