/model-card

name: model-card
description: Use when a trained medical-imaging model needs its documentation. Fills a Model Card and a Datasheet for its dataset from facts you supply, adds a METRIC-informed data-quality pass and gates that no required section is empty. Never invents numbers, provenance or licence.
metadata:
  triggers: "model card, model cards, datasheet, datasheet for datasets, dataset documentation, model documentation, hugging face card, model metadata, intended use, out-of-scope, data quality, METRIC framework, model reporting, document a model"

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-assessment` audits the design and 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-assessment`.
• 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 model's developers: task + architecture + provenance + licence; intended use and out-of-scope use; training and evaluation cohorts; the reference standard and inter-reader agreement; overall and **per-subgroup** performance; data collection, consent, and de-identification. Anything not supplied stays `[NEEDS INPUT]` — never guess.

Phase 2 — Fill the Model Card

Copy `${CLAUDE_SKILL_DIR}/references/model_card_template.md` to `MODEL_CARD.md` and fill each section from the facts. Keep the headings. Numbers come only from `/model-assessment` / executed results.

Phase 3 — Fill the Datasheet

Copy `${CLAUDE_SKILL_DIR}/references/datasheet_template.md` to `DATASHEET.md` and fill the seven question groups (Motivation, Composition, Collection, Preprocessing/Labeling, Uses, Distribution, Maintenance).

Phase 4 — METRIC data-quality pass

Walk `${CLAUDE_SKILL_DIR}/references/metric_dimensions.md` (completeness, correctness, consistency, representativeness, timeliness, provenance, label provenance, fairness/coverage, leakage safety) and record each finding in the Datasheet. Anything that affects the headline metric's validity is also a `/model-assessment` finding — cross-check there.

Phase 5 — Verify completeness (deterministic gate)

python3 ${CLAUDE_SKILL_DIR}/scripts/check_model_card_complete.py \
  --card MODEL_CARD.md --datasheet DATASHEET.md --strict

`MISSING_SECTION` / `EMPTY_REQUIRED_SECTION` / `UNFILLED_FIELD` must be zero before the card ships. `UNFILLED_FIELD` names each field of a required section still left as `[NEEDS INPUT]` / `[VERIFY]` (e.g. License or subgroup performance), even when a sibling field is filled. An explicit `N/A` / `None` counts as an answer only as a field's whole value, and never in Intended Use, Training Data, Evaluation Data, Metrics or Quantitative Analyses. Only the template tokens as written (`[NEEDS INPUT ...]`, `[VERIFY]`, `[VERIFY: ...]`, upper case) count; markdown link text such as `[verify the protocol](https://...)` or `[Verify][ref]`, and anything inside an HTML comment or a fenced code block, is not a placeholder. Known limits: a field left as a hand-written `TODO` / `TBD` / `<...>` / `XXXX` is caught only when the whole section is unfilled; keep the template's bracketed `[NEEDS INPUT]` markers for open fields.

Phase 6 — Hand off

Carry the card into `/write-paper` (the Methods / supplement reference it), `/check-reporting` (CLAIM 2024 / TRIPOD+AI item audit of the manuscript), and `/self-review`.

Anti-Hallucination

• **Never invent evaluation numbers, subgroup results, or dataset provenance.** Every figure comes from

`/model-assessment` or the user's executed results; every provenance / consent / licence statement is user-confirmed. Unknown → `[NEEDS INPUT]`, which the gate flags.

• **Never mark a section complete without user-supplied content**, and never auto-fill a placeholder to

pass the gate.

• **Never assert a licence or consent status the user did not confirm.**
• The gate checks **presence**, not truth — a complete card can still contain a wrong number; validity

is `/model-assessment` and the human's responsibility.

Deterministic gate

`scripts/check_model_card_complete.py` — verifies every required Model Card / Datasheet section is present and non-empty (stdlib, network-free). Reproducible challenge: `bash ${CLAUDE_SKILL_DIR}/scripts/check_model_card_complete_challenge/verify.sh`.

Note on classification

Model Cards (Mitchell et al. 2019) and Datasheets (Gebru et al. 2021) are **documentation standards**, not clinical reporting guidelines, so they live here as `references/` **templates** (uncounted), not in `/check-reporting`'s counted checklist set — the same way `appraisal_tools/METRICS.md` is kept separate. `/check-reporting` still owns the manuscript-level CLAIM 2024 / TRIPOD+AI item audit.

Boundaries

model-assessment (audit design + metrics)
  └─ model-card (this skill: Model Card + Datasheet +