--- name: model-card-generation-94183c849e9a description: Model card generation expertise — auto-generates Model Cards covering intended use, limitations, and training data provenance per the HuggingFace and Mitchell et al. standard --- 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. More data-science AI tools and resources at https://theaicareerlab.com/professions/data-scientist --- Source attribution: alexclowe Original project: https://github.com/alexclowe/awesome-claude-cowork-plugins/blob/99f3455d59e5f3ce66cde3b193fac514e28399b8/data-scientist/skills/model-card-generation/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 The AI Career Lab Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: alexclowe Original project: https://github.com/alexclowe/awesome-claude-cowork-plugins/blob/99f3455d59e5f3ce66cde3b193fac514e28399b8/data-scientist/skills/model-card-generation/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Product Management Roles: Data Scientist, ML / AI Engineer, Product Manager Specialties: API Tools: Hugging Face Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: 317dfe92eef790dabb9a754a96e21d8dbef39d4591ebb8c2bc4e06166b3f27a6