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data-cleaning

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Use when a raw table is too dirty to trust — nulls, sentinels, duplicate rows, category sprawl, mixed types, bad dates — and you need a re-runnable clean() plus a schema gate that fails loud. NOT emitting .xlsx (that is spreadsheet-ops), NOT acquiring rows (that is data-scraper), NOT parsing PDF/HTML into rows (that is structured-extraction).

skills/data-cleaning/SKILL.md

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# Data cleaning — make dirty data trustworthy, and make the cleaning auditable

A clean table is **typed + deduped + normalized + validated + reproducible**. The deliverable here is
never "I opened a notebook and fixed some rows by hand." It is a re-runnable function `clean(raw) -> df`
plus a **schema gate** that fails loud when next month's file violates the contract. Reproducible means
the same input always yields the same output: versions pinned, sorts deterministic, nothing random without
a seed. If you can't re-run it tomorrow and get the identical result, you haven't cleaned the data — you've
edited a snapshot.

Cleaning **starts** once you hold tabular rows and **ends** at a validated table/DataFrame/Parquet. Before
that boundary the job is acquisition ([`data-scraper`](../data-scraper/SKILL.md),
[`structured-extraction`](../structured-extraction/SKILL.md)); after it, consumption
([`spreadsheet-ops`](../spreadsheet-ops/SKILL.md), [`analytics`](../analytics/SKILL.md),
[`business-intelligence`](../business-intelligence/SKILL.md),
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