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Data Card Writer

Documents
data-card-writer

Data Card Writer is an Agent Skill for documents published in the mkurman/zorai repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Generate structured datasheets for datasets (Gebru et al. "Datasheets for Datasets" format) — purpose, composition, collection process, preprocessing, limitations, and licensing.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/mkurman/zorai/blob/HEAD/skills/data-card-writer/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/data-card-writer, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Data Card Writer

Overview

Generates a structured datasheet following the "Datasheets for Datasets" framework (Gebru et al., 2021). Every dataset should ship with a data card that answers: who made this, what's in it, how was it collected, what are the limitations.

Template

# [Dataset Name] — Data Card v[Version]

## Motivation
- Purpose: [What task is this for?]
- Creator: [Who created it?]
- Funding: [Who funded it?]

## Composition
- Instances: [N] examples, [M] features
- Target: [What is being predicted?]
- Protected attributes: [Gender, race, age, etc. — or "not collected"]
- Missing data: [X% missing overall, per-column breakdown]
- Class balance: [Distribution]

## Collection Process
- Source: [URL, database, API, instrument]
- Collection date: [YYYY-MM-DD to YYYY-MM-DD]
- Sampling strategy: [Random, stratified, convenience]
- Ethical review: [IRB protocol # or "not applicable"]
- Consent: [How was consent obtained?]

## Preprocessing
- Raw → cleaned pipeline: [Steps applied]
- Exclusions: [What was removed and why?]
- Transformations: [Normalization, imputation, encoding]
- Cleaning script hash: [sha256]

## Uses
- Recommended uses: [What this dataset is validated for]
- Discouraged uses: [What this should NOT be used for]
- Out-of-scope: [Inappropriate applications]

## Distribution
- License: [e.g., CC-BY-4.0, CDLA-Permissive]
- Access: [URL or process]
- Version: [Semantic version]

## Limitations
- Known biases: [Demographic, temporal, geographic]
- Coverage gaps: [Missing populations, conditions, domains]
- Label quality: [Inter-rater agreement, noise estimates]

## Maintenance
- Maintainer: [Contact or organization]
- Update frequency: [Monthly, annually, none]
- Errata: [Link to corrections]

## Citation
[BibTeX or DOI]

Validation Checklist

  • All sections populated (none left as placeholder).
  • Limitations section is honest — not "no known limitations".
  • License is verified (not guessed).
  • Provenance traces to source data.
  • Contact information is current.
  • Version matches the dataset release tag.