What do I need to use Dataset Datasheet?

Dataset name, version, owner; Motivation; Composition; Collection; Known issues.

Is Dataset Datasheet free to use?

Yes — run it free in the browser playground (no API key needed for the sponsored trial, or bring any provider key), or install it into Claude Code, Cursor, ChatGPT and more via npx pm-claude-skills. MIT-licensed and open source.

Dataset Datasheet

Q: How do I know the Dataset Datasheet output is good?

The skill verifies itself against its own quality checks: The collection method and **legal/consent basis** are stated — not assumed; Class balance and key distribution skews are quantified, not hand-waved; Sensitive attributes (and proxies for them) are identified explicitly; "Do not use for" lists concrete tasks where the data would mislead.

🔵 Stable🕐 updated 2026-10-06 🔷 SkillSpec L3 pm-ai

Document a dataset so others know what it is, how it was made, and when not to use it. Use when asked to write a datasheet for a dataset, document training/eval data, or assess whether a dataset is fit for a use. Produces a datasheet — motivation, composition, collection process, preprocessing, recommended uses & limits, distribution, and maintenance.

📚 Based on Datasheets for Datasets (Gebru et al., 2018)

▶ Run it free — no key needed 📝 Grade your existing draft ⬇ Add to Claude (works in Duet) 📱 Use as an app View SKILL.md ↗ What changed

🗣 Say this to your agent

“Write a datasheet for a dataset.”“Document training/eval data.”“Assess whether a dataset is fit for a use.”

What to give it

▸Dataset name, version, owner — and what it's used for today.

▸Motivation — why it was created and for what task.

▸Composition — what an instance is, how many, fields/labels, and time range.

▸Collection — sources, method (scraped, logged, purchased, annotated), and consent/licensing basis.

▸Known issues — gaps, imbalances, label noise, sensitive attributes, duplicates.

✅ The bar it holds itself to

Every skill in this library self-verifies — these are this skill's own quality checks, straight from its definition.

✓The collection method and **legal/consent basis** are stated — not assumed

✓Class balance and key distribution skews are quantified, not hand-waved

✓Sensitive attributes (and proxies for them) are identified explicitly

✓"Do not use for" lists concrete tasks where the data would mislead

✓Label provenance is documented (who labelled, with what guidelines, and agreement level)

✓An owner and update/error-reporting process are named

⚠️ What it refuses to do

Do not describe only the happy-path contents — the gaps, skews, and noise are what cause model failures

Do not omit the consent/licensing basis — "we scraped it" is a legal and ethical liability if undocumented

Do not ignore proxy variables — removing race/gender columns doesn't remove the bias if zip code or name encodes it

Do not present label quality as perfect — state who labelled it and the agreement rate, or note it's unmeasured

Do not leave the dataset ownerless — an unmaintained dataset silently rots as the world changes

Install

npx pm-claude-skills add --agent claude   # or codex · cursor · gemini · hermes
# or one-line MCP (every skill, any client):
claude mcp add pm-skills -- npx -y -p pm-claude-skills pm-claude-skills-mcp

Related skills

🔌 Embed this skill

Drop this on your blog, docs, or site — it renders a "Run this skill" card:

<div data-pm-skill="dataset-datasheet"></div>
<script src="https://mohitagw15856.github.io/pm-claude-skills/embed.js" async></script>

💬 Discussion

Dataset Datasheet is one of 1326 open-source professional AI agent skills — all SkillSpec L3. Try them all in the browser · ⭐ Star on GitHub · Browse the full catalog