mohitagw15856
51bf4be52f
Three features riding 2026 trends (agentic CI, codegen, evals), sharing one dependency-free Anthropic client (bin/lib/anthropic.mjs). 1. GitHub Action (action/) — run any skill in a consumer repo's CI: uses: mohitagw15856/pm-claude-skills/action@main. Composite action + run.mjs (loads the bundled SKILL.md, calls the API, exposes result as a step output / file). Docs with auto-PR-description example. 2. generate command — `npx pm-claude-skills generate --from <url|file>` turns a team's docs into a SKILL.md following the authoring standard (bin/generate.mjs, wired into the CLI; needs ANTHROPIC_API_KEY). 3. Skill evals + Leaderboard — evals/run-evals.mjs runs each case across models and scores output with an LLM judge (structure/completeness/usefulness/ grounding); scripts/build-leaderboard.mjs renders web/leaderboard.html (built in the Pages deploy, falls back to clearly-labelled example data). Linked from README, catalog, and playground. Offline-testable parts verified (prompt building, skill loading, graceful errors, leaderboard render). SkillCheck/audit/exports all green. Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px Co-authored-by: Claude <noreply@anthropic.com>
41 lines
1.6 KiB
Markdown
41 lines
1.6 KiB
Markdown
# Skill Evals
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An LLM-as-judge harness that scores skill output quality across models — so claims like
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"production-ready" are backed by numbers, not vibes. Results render as a public
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[Skill Leaderboard](https://mohitagw15856.github.io/pm-claude-skills/leaderboard.html).
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## What it measures
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For each [case](cases.json), a model runs the skill, then a **judge model** scores the
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output 1–5 on four dimensions:
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- **structure** — follows a clear, expected structure
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- **completeness** — covers what the task needs
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- **usefulness** — specific and actually useful, not generic
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- **grounding** — stays grounded in the input, no invented facts
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## Run it
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Needs an Anthropic API key (this calls the API and costs tokens):
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```bash
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ANTHROPIC_API_KEY=sk-ant-... node evals/run-evals.mjs
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# --models claude-opus-4-8,claude-sonnet-4-6,claude-haiku-4-5-20251001
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# --judge claude-opus-4-8
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node scripts/build-leaderboard.mjs # render web/leaderboard.html
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```
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`run-evals.mjs` writes `evals/results.json`; the leaderboard builder prefers it and falls
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back to `results.example.json` (clearly labelled) so the page renders before you run real evals.
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## Add a case
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Append to [`cases.json`](cases.json): `{ "skill": "<name>", "input": "<a realistic prompt>" }`.
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Keep inputs short but representative of how the skill is actually used.
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## Honesty notes
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- Scores are an LLM judge's opinion, not ground truth — treat them as a comparative signal.
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- The judge sees the skill's stated purpose and the output, not the model name (reduces bias).
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- Re-run after model upgrades; numbers drift.
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