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