# Skill Evals An LLM-as-judge harness that scores skill output quality across models — so claims like "production-ready" are backed by numbers, not vibes. Results render as a public [Skill Leaderboard](https://mohitagw15856.github.io/pm-claude-skills/leaderboard.html). ## What it measures For each [case](cases.json), a model runs the skill, then a **judge model** scores the output 1–5 on four dimensions: - **structure** — follows a clear, expected structure - **completeness** — covers what the task needs - **usefulness** — specific and actually useful, not generic - **grounding** — stays grounded in the input, no invented facts ## Run it Needs an Anthropic API key (this calls the API and costs tokens): ```bash ANTHROPIC_API_KEY=sk-ant-... node evals/run-evals.mjs # --models claude-opus-4-8,claude-sonnet-4-6,claude-haiku-4-5-20251001 # --judge claude-opus-4-8 node scripts/build-leaderboard.mjs # render web/leaderboard.html ``` `run-evals.mjs` writes `evals/results.json`; the leaderboard builder prefers it and falls back to `results.example.json` (clearly labelled) so the page renders before you run real evals. ### No local key? Run it in CI 1. Add an `ANTHROPIC_API_KEY` repo secret. 2. Enable **Settings → Actions → General → Workflow permissions → "Allow GitHub Actions to create and approve pull requests"** (so the workflow can open its results PR — `main` requires PRs). 3. **Actions → "Update Skill Leaderboard" → Run workflow.** It runs the evals and opens a PR with `evals/results.json`. **Merge that PR** and the Pages deploy re-renders the public leaderboard with real numbers — no laptop required. ## Add a case Append to [`cases.json`](cases.json): `{ "skill": "", "input": "" }`. Keep inputs short but representative of how the skill is actually used. ## Honesty notes - Scores are an LLM judge's opinion, not ground truth — treat them as a comparative signal. - The judge sees the skill's stated purpose and the output, not the model name (reduces bias). - Re-run after model upgrades; numbers drift.