model-card

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Generate the documentation an engineer-built medical-imaging model must carry — a Model Card (Mitchell et al. 2019), a Datasheet for its dataset (Gebru et al. 2021), and a METRIC-informed data-quality pass — filled from user-supplied facts, then verify every required section is present and non-empty before the card ships to a repo, Hugging Face card, or manuscript supplement. Never fabricates numbers, provenance, consent, or licence; unfilled fields stay flagged. Ships a deterministic completeness gate. Model Card and Datasheet are documentation standards vendored here as templates, not counted reporting checklists.

AI & Automation 220 stars 55 forks Updated today MIT

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Quality Score: 95/100

Stars 20%
78
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Model-Card Skill ## Purpose This skill produces the **documentation an engineer-built medical-imaging model must carry**: a **Model Card** (intended use, out-of-scope use, training data, per-subgroup performance, caveats), a **Datasheet** for its dataset (provenance, composition, collection, labelling, consent), and a **METRIC-informed data-quality pass**. It fills the templates **from facts the user supplies** — it never invents a number, a provenance detail, a consent status, or a licence — and ships a deterministic gate that no required section is missing or left as an unfilled `[NEEDS INPUT]` placeholder. It is the **reporting** seam of the model-engineering lane: after `/model-validation` audits the design and `/model-evaluation` produces the numbers, this skill records them in a portable, auditable card that `/write-paper` and `/check-reporting` consume. It mirrors `/version-dataset` structurally (generate + deterministic verify). ## When to use - A trained model needs a Model Card / Datasheet for a repo, Hugging Face card, or manuscript supplement. ## When NOT to use - Auditing the validation design / metrics → `/model-validation`, `/model-evaluation`. - Versioning the dataset bytes → `/version-dataset`; tabular variable docs → `/generate-codebook`. - Item-by-item reporting-guideline compliance of the manuscript → `/check-reporting`. - Building / training the model → `/model-scaffold`. ## Workflow ### Phase 1 — Collect the facts Gather, from the user / the mod...

Details

Author
Aperivue
Repository
Aperivue/medsci-skills
Created
3 months ago
Last Updated
today
Language
Python
License
MIT

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