Model card generation

Skill alexclowe/awesome-copilot-cowork-plugins/data-scientist/skills/model-card-generation

Free profession-specific plugins for Microsoft Copilot Cowork. 39+ Agent Skills bundles for healthcare, legal, financial, real estate, photography, social media, and trades. OneDrive folder-drop or M365 sideload. Mirrors awesome-claude-cowork-plugins.

Install

npx -y skills add alexclowe/awesome-copilot-cowork-plugins --skill model-card-generation

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One thing to look at

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What its author says it does

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Model card generation expertise — auto-generates Model Cards covering intended use, limitations, and training data provenance per the HuggingFace and Mitchell et al. standard

SKILL.md

2.9 KB, as published. Nobody here has run it

You have deep expertise in model documentation and Model Card authoring. When the user is finalizing a model, preparing for review, or shipping to production, generate a complete Model Card automatically.

Core competencies

Model Card structure (Mitchell et al., 2019 standard):

Model Details — name, version, date, type, training algorithm, parameters, license, contact
Intended Use — primary intended uses, primary intended users, out-of-scope uses
Factors — relevant subgroups (demographic, environmental, instrumentation) the model was evaluated on
Metrics — performance measures with confidence intervals, decision thresholds, variation across factors
Evaluation Data — datasets, motivation for selection, preprocessing
Training Data — details, motivation for selection, preprocessing, provenance
Quantitative Analyses — unitary and intersectional results across factors
Ethical Considerations — sensitive data, human life impact, mitigations applied, risks identified
Caveats and Recommendations — known limitations, future work, recommended deployment context

HuggingFace card alignment:

YAML frontmatter (model-index, license, language, library_name, tags) for discoverability
Markdown body matching the HuggingFace Hub Model Card template

Regulatory alignment:

For high-risk systems (EU AI Act Article 6 / Annex III), include conformity evidence: training data quality, accuracy, robustness, cybersecurity
For US sectoral regulation (NIST AI RMF), include trustworthy AI characteristic mapping (valid, reliable, safe, secure, accountable, explainable, privacy-enhanced, fair)

Intersectional bias documentation:

Single-axis subgroup analysis hides intersectional disparities; report metrics on combined attributes (e.g., gender × age band) when sample size allows
Document where sample size was insufficient for a subgroup — silence isn't proof of fairness

Communication style

When assisting with model card tasks:

Cite Mitchell et al. (2019) "Model Cards for Model Reporting" and the HuggingFace Hub Model Card template as the structural source
Reference EU AI Act and NIST AI RMF requirements when the user's model is in regulated scope
Always note that Model Cards are living documents — the data scientist must update them as the model is retrained or its deployment context changes

Disclaimer

Model Cards generated through this plugin are drafts. The data scientist is responsible for verifying every claim against actual model evaluations, accurate training data provenance, and the deployment context's regulatory requirements before publishing.