opensanctions Dataset metadata
Standardise the metadata of the dataset at $ARGUMENTS.
The rules live in zavod/docs/metadata.md — read it
first and follow it. This skill is only the procedure for applying it; when a rule is
unclear, read the doc, do not invent a convention. For a legislature/parliament PEP
dataset, take the title, description and coverage.frequency patterns from the
/legislature-metadata skill; the rest of this checklist applies as written.
1. Read
The .yml, then the crawler (entry_point, usually crawler.py) for scope facts:
what the source covers, what is deliberately skipped, which lookups are not plain type
lookups and what they do.
2. Check each field against the doc
title— the doc's Title rules.summary— length and style per the doc's Summary rules.description— the doc's Description rules, including its keep-out list.coverage.frequency— the doc's house default for the dataset type.tags— present and plausible for list type and target countries.publisher— all subfields per the doc's Publisher section.data—urlfetchable;formatandlangper the doc's Source data section.
3. Maintainer comments
- Top-of-file
#block: known failure modes, source quirks, recurring-warning runbooks. - One short
#comment above each non-type lookup: what it matches, why it exists.
Only write down evidence: things observed in the code, the source data, issues.log,
git history, or stated by the user. Never speculate, and never delete existing comments
that still hold. Keep user-facing prose (description) and maintainer notes (comments)
strictly apart.
Verify
python -c "from pathlib import Path; from zavod.meta import load_dataset_from_path; d = load_dataset_from_path(Path('<yml path>')); print(d.name, '-', d.model.title)"
Re-read the edited fields: every factual claim in title/description traces to the source or crawler, the summary length is in range, and no sourcing or mechanics language remains in the description.