model-card-generator

Generate standardized model cards documenting health AI capabilities, limitations, and evidence.

Updated Aug 27, 2026

Search Tags:#documentation #deployment #transparency #fairness #model card #health AI #clinical AI

Ready to run

npx skills add https://github.com/EvidenceOS/awesome-health-ai-skills --skill model-card-generator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: model-card-generator
Source: https://github.com/EvidenceOS/awesome-health-ai-skills/tree/main/skills/clinical-ai/model-card-generator
Command: npx skills add https://github.com/EvidenceOS/awesome-health-ai-skills --skill model-card-generator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical need for clear, standardized documentation of health AI models, moving beyond boilerplate to create useful "nutrition labels" for AI systems.

Core Features & Use Cases

Standardized Model Card Creation: Generates comprehensive model cards following a health AI-adapted template.
Evidence Chain Integration: Incorporates evidence chain status for regulatory compliance.
Fairness & Limitations Documentation: Explicitly documents performance across subgroups and known limitations.
Use Case: A research team developing a new diagnostic AI needs to create a model card for regulatory submission and clinical adoption. This skill guides them through documenting the model's intended use, training data, performance metrics, subgroup analysis, and ethical considerations.

Quick Start

Use the model-card-generator skill to create a model card for the 'cheXpert-classifier' model.

Frequently Asked Questions about model-card-generator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is a health AI model card and what should it include?▼

A health AI model card is standardized documentation detailing a model's capabilities, limitations, and evidence. It should include intended use, training data, performance metrics, subgroup performance, and ethical considerations to ensure transparency and compliance.

How do I generate a model card for clinical AI regulatory submission?▼

To generate a model card for clinical AI, use a standardized template to document the model's intended use, evaluation data, and performance metrics. This process facilitates regulatory compliance by capturing the evidence chain status and ethical considerations required for submission.

Does model card documentation require subgroup performance and fairness analysis?▼

Yes, model card documentation requires explicit subgroup performance and fairness analysis. Documenting performance across different demographic subgroups is essential for identifying biases, ensuring health AI transparency, and meeting regulatory expectations for high-risk AI systems.

Can I document evidence chain status for AI medical devices in a model card?▼

Yes, you can document evidence chain status for AI medical devices directly within a model card. Integrating the evidence chain status into the documentation ensures regulatory compliance and provides a clear audit trail for clinical AI model evaluation and deployment.

What is the best way to document health AI limitations for clinical adoption?▼

The best way to document health AI limitations is to use a standardized model card template that explicitly outlines known limitations and ethical considerations. Transparently documenting these constraints builds trust and facilitates safer clinical adoption of diagnostic AI systems.

When do I need standardized documentation for high-risk AI systems?▼

You need standardized documentation for high-risk AI systems when deploying clinical AI models that require regulatory approval. Generating comprehensive model cards ensures you meet transparency expectations by detailing training data, performance metrics, and ethical considerations.