Model documentation skill for generating model cards following Google's model card framework.
Scanned 9/2/2026
Install to Claude Code
npx -y skills add a5c-ai/babysitter --skill model-card-generator --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: model-card-generator
description: Model documentation skill for generating model cards following Google's model card framework.
allowed-tools:
- Read
- Write
- Bash
- Glob
- Grep
graph:
domains: [domain:data-science]
specializations: [specialization:data-science-ml]
skillAreas: [skill-area:ml-governance-compliance, skill-area:explainability-interpretation]
roles: [role:data-scientist, role:ml-ops-engineer]
workflows: [workflow:ml-model-lifecycle]
---
# model-card-generator
## Overview
Model documentation skill for generating comprehensive model cards following Google's model card framework for ML model documentation.
## Capabilities
- Model details documentation (architecture, training, etc.)
- Intended use specification
- Performance metrics documentation
- Ethical considerations section
- Caveats and limitations
- Quantitative analysis sections
- Version history tracking
- Multiple output formats (HTML, Markdown, JSON)
## Target Processes
- Model Interpretability and Explainability Analysis
- Model Evaluation and Validation Framework
- ML Model Retraining Pipeline
## Tools and Libraries
- Model Card Toolkit
- TensorFlow Model Analysis (optional)
- Jinja2 (templating)
## Input Schema
```json
{
"type": "object",
"required": ["modelDetails", "intendedUse"],
"properties": {
"modelDetails": {
"type": "object",
"properties": {
"name": { "type": "string" },
"version": { "type": "string" },
"type": { "type": "string" },
"architecture": { "type": "string" },
"trainingDate": { "type": "string" },
"framework": { "type": "string" },
"citations": { "type": "array", "items": { "type": "string" } },
"license": { "type": "string" }
}
},
"intendedUse": {
"type": "object",
"properties": {
"primaryUses": { "type": "array", "items": { "type": "string" } },
"primaryUsers": { "type": "array", "items": { "type": "string" } },
"outOfScopeUses": { "type": "array", "items": { "type": "string" } }
}
},
"factors": {
"type": "object",
"properties": {
"relevantFactors": { "type": "array", "items": { "type": "string" } },
"evaluationFactors": { "type": "array", "items": { "type": "string" } }
}
},
"metrics": {
"type": "object",
"properties": {
"performanceMetrics": { "type": "array" },
"decisionThresholds": { "type": "object" },
"variationApproaches": { "type": "array" }
}
},
"evaluationData": {
"type": "object",
"properties": {
"datasets": { "type": "array" },
"motivation": { "type": "string" },
"preprocessing": { "type": "string" }
}
},
"trainingData": {
"type": "object",
"properties": {
"datasets": { "type": "array" },
"motivation": { "type": "string" },
"preprocessing": { "type": "string" }
}
},
"ethicalConsiderations": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"mitigationStrategy": { "type": "string" }
}
}
},
"caveatsAndRecommendations": {
"type": "array",
"items": { "type": "string" }
},
"outputConfig": {
"type": "object",
"properties": {
"format": { "type": "string", "enum": ["html", "markdown", "json"] },
"outputPath": { "type": "string" }
}
}
}
}
```
## Output Schema
```json
{
"type": "object",
"required": ["status", "modelCardPath"],
"properties": {
"status": {
"type": "string",
"enum": ["success", "error"]
},
"modelCardPath": {
"type": "string"
},
"format": {
"type": "string"
},
"sections": {
"type": "array",
"items": { "type": "string" }
},
"warnings": {
"type": "array",
"items": { "type": "string" },
"description": "Warnings about missing recommended sections"
}
}
}
```
## Usage Example
```javascript
{
kind: 'skill',
title: 'Generate model card',
skill: {
name: 'model-card-generator',
context: {
modelDetails: {
name: 'Fraud Detection Model',
version: '2.0.0',
type: 'Binary Classification',
architecture: 'XGBoost',
trainingDate: '2024-01-15',
framework: 'scikit-learn',
license: 'Proprietary'
},
intendedUse: {
primaryUses: ['Transaction fraud detection'],
primaryUsers: ['Risk management team'],
outOfScopeUses: ['Credit scoring', 'Identity verification']
},
metrics: {
performanceMetrics: [
{ name: 'AUC-ROC', value: 0.95 },
{ name: 'Precision@0.5', value: 0.87 }
]
},
ethicalConsiderations: [
{ name: 'Demographic bias', mitigationStrategy: 'Regular fairness audits' }
],
outputConfig: {
format: 'markdown',
outputPath: 'docs/model_card.md'
}
}
}
}
```
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