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.
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.
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
tools
Read, Write, Edit, Bash, Grep, Glob
model
inherit
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 — 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 placeholder.
from facts the user supplies
[NEEDS INPUT]
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.
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-evaluation / 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-validation finding — cross-check there.
MISSING_SECTION / EMPTY_REQUIRED_SECTION must be zero before the card ships.
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-evaluation 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-validation 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.
