dataset-datasheet
GitHub用于生成数据集数据表，记录数据来源、组成、收集流程及限制。帮助明确数据集用途与边界，识别敏感属性、偏差及合规风险，确保数据资产可复用且符合伦理法律要求。
触发场景
安装
npx skills add mohitagw15856/pm-claude-skills --skill dataset-datasheet -g -y
SKILL.md
Frontmatter
{
"name": "dataset-datasheet",
"description": "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."
}
Dataset Datasheet Skill
Models inherit the flaws of their data, and most data debt is invisible because nobody wrote down where the data came from. A datasheet is that record: how the dataset was collected, what's in it, what's missing, and what it should not be used for. It's the difference between a reusable asset and a liability.
Required Inputs
Ask for these only if they aren't already provided:
- 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.
Output Format
Datasheet: [dataset] v[version]
Owner: [team] · Created: [date] · License: [license]
1. Motivation — why this dataset exists, the task it serves, and who funded/created it.
2. Composition
- What a single instance represents; total count; the schema (fields, label definitions).
- Class/label balance and key distributions (and notable skews).
- Sensitive attributes present (directly or by proxy), and whether individuals are identifiable.
- Known missing data, duplicates, or noise.
3. Collection process — sources, mechanism (scrape/log/survey/annotation), time window, sampling strategy, and the legal/consent basis (license, ToS, opt-in).
4. Preprocessing / labelling — cleaning, dedup, filtering, and how labels were produced (who annotated, guidelines, inter-annotator agreement).
5. Recommended uses & limits
- Appropriate uses: tasks this data supports well.
- Do not use for: tasks where its biases/gaps would cause harm or invalid results.
6. Distribution & access — who can use it, how it's shared, and tenancy/PII handling.
7. Maintenance — owner, update cadence, versioning, and how errors get reported and fixed.
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
- Label provenance is documented (who labelled, with what guidelines, and agreement level)
- An owner and update/error-reporting process are named
Anti-Patterns
- 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
Based On
Datasheets for Datasets (Gebru et al., 2018) and data-documentation practice in responsible-AI reviews.
版本历史
- a38bc30 当前 2026-07-05 11:10
