Agent skill · Documentation

authoring-model-cards

Generate a model card for an OpenMed clinical NER or de-identification model documenting intended use, quantitative metrics, subgroup performance, limitations, and a medical-device disclaimer for clinical AI governance. Use when the user wants to write or update a model card, a README model section, or governance documentation, or to turn OpenMed eval outputs (release gate report, fairness_report, error_report) into the card's metrics and limitations sections. Trigger on \"model card\", \"intended use\", \"model documentation\", \"governance\", \"limitations section\", \"datasheet\", or \"FDA/

maziyarpanahigithub.com/maziyarpanahiGitHub ↗
claude-codeApache-2.0
Install
npx skills add maziyarpanahi/openmed --skill authoring-model-cards --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Version: 1.0
Path: skills/authoring-model-cards/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 4,851
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Authoring Model Cards A model card is the honest spec sheet for a model: what it's for, how well it works, where it breaks, and who it might fail. For clinical models this is governance-critical — an undocumented de-id model is one nobody can sign off on. This skill fills a model card directly from OpenMed eval outputs so the numbers are reproducible, not aspirational. ## When to use this skill - You're publishing or updating an OpenMed model and need its card. - You have eval artifacts (`GateReport`, `fairness_report`, `error_report`) and need to turn them into intended-use, metrics, and limitations sections. - A clinical AI governance / model-risk review needs a transparency document. Run the evals **first** (see `evaluating-with-leakage-gates`, `benchmarking-clinical-ner`, `auditing-subgroup-fairness`); this skill documents their results — it does not generate the numbers. ## Card sections (Mitchell et al., + clinical extensions) See `references/model-card-sections.md` for the full section-to-source map. The load-bearing sections for an OpenMed model: - **Model details** — repo id, family, tier, format, params, milestone, license (Apache-2.0). Pull from the `GateReport` identi

What's inside
Steps it walks through
  1. When to use this skill
  2. Card sections (Mitchell et al., + clinical extensions)
  3. Quick start — fill the card from eval outputs
  4. Workflow
  5. Disclaimer block (paste & adapt)
  6. Hand-off to / from OpenMed
  7. Edge cases & gotchas
  8. Standards & references
Ships with 1 file
  • references/model-card-sections.md
More from openmed
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About this skill
What does the authoring-model-cards skill do?

Generate a model card for an OpenMed clinical NER or de-identification model documenting intended use, quantitative metrics, subgroup performance, limitations, and a medical-device disclaimer for clinical AI governance. Use when the user wants to write or update a model card, a README model section, or governance documentation, or to turn OpenMed eval outputs (release gate report, fairness_report, error_report) into the card's metrics and limitations sections. Trigger on \"model card\", \"intended use\", \"model documentation\", \"governance\", \"limitations section\", \"datasheet\", or \"FDA/

How do I install it?

Run `npx skills add maziyarpanahi/openmed --skill authoring-model-cards --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going