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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/ONC transparency" for an OpenMed model.

77

Quality

96%

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SKILL.md
Quality
Evals
Security

Quality

Content

93%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a lean, actionable overview that gives executable code, a concrete workflow, and one well-structured reference file. Its only weakness is a missing explicit validate→fix→retry feedback loop in the workflow, which keeps workflow clarity at 4 rather than 5.

Suggestions

Add an explicit validate→fix→retry loop to the Workflow (e.g., 'If manifest_coherence or model_card gate checks fail, correct the card front matter and re-run the gate until green') to lift workflow clarity to 5.

The Quick start builds a `card` dict but only comments 'Render `card` into Markdown' — a one-line concrete render step (e.g., a Jinja/HF template call) would close the last actionability gap.

DimensionReasoningScore

Conciseness

Lean and efficient — the opening framing, quick-start code, workflow, and edge-cases list all earn their tokens; no explaining of concepts Claude already knows and no padded sections.

5 / 5

Actionability

Fully executable guidance: a copy-paste-ready Python quick start using real openmed.eval imports and object attributes, a paste-and-adapt disclaimer block, and a concrete card dict mapping — specific examples cover the common case.

5 / 5

Workflow Clarity

A clear 7-step sequenced workflow with named verification via the gate's manifest_coherence and model_card checks, but it lacks an explicit validate-then-fix retry loop, keeping it just below the explicit-checkpoint anchor.

4 / 5

Progressive Disclosure

A clear SKILL.md overview with a single well-signaled, one-level-deep reference (references/model-card-sections.md) that exists and maps sections to real eval fields, giving easy navigation.

5 / 5

Total

19

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20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is a strong, third-person statement that names concrete actions, comprehensive natural triggers, and an explicit use-when clause scoped to a distinct OpenMed niche. It satisfies the what/when requirement with concrete trigger phrases and no padding.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — generating a model card documenting intended use, quantitative metrics, subgroup performance, limitations, and a medical-device disclaimer, plus converting named eval outputs (release gate report, fairness_report, error_report) into specific card sections — for comprehensive coverage.

5 / 5

Completeness

Explicitly answers both "what" (generate a model card documenting...) and "when" ("Use when the user wants to..." and "Trigger on...") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Provides comprehensive natural trigger phrases a user would actually say — "model card", "intended use", "model documentation", "governance", "limitations section", "datasheet", and "FDA/ONC transparency".

5 / 5

Distinctiveness Conflict Risk

Scoped to a clear niche — OpenMed clinical NER / de-identification model cards — with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
maziyarpanahi/openmed
Reviewed

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