AGENTS.md

AGENTS.md

Guidance for agents and contributors adding a source and cutting a release. Read this before opening a PR — several recent contributions added data but skipped the doc/version bump, forcing a maintainer cleanup release.

How the docs are actually maintained

src/make_docs.py is a scaffolding/assembly helper, not a round-trip source of truth. It regenerates the README source table + release totals, LICENSE, a CHANGELOG stub, and template datasheets — but it does not reproduce the committed docs on its own:

• It iterates SOURCES in src/sources.py; any source in data/ but missing from SOURCES is silently dropped from the table and totals.
• It stamps every regenerated datasheet's "Added" with the single global ADDED constant, overwriting real per-source add dates.
• It cannot reproduce hand-written README narrative (audit notes, policy notes, the phrase-frequency section).

So datasheets and the README narrative are hand/contributor-maintained. Never run make_docs.py and commit the result blind — git diff first and restore anything it dropped or restamped.

Before you start: check the findings log

artifacts/source_findings.md records sources already investigated, including the rejected ones and why. Check it before spending a day on a source someone already found to be blocked — and append your own finding there whether the answer was yes or no.

Add a source

1. Produce the artifact under data/<key>/:
   • <key>.parquet — built by src/build_dynaword.py (LFS-tracked automatically).
   • <key>.stats.json — doc/token/char counts (drives all totals).
   • <key>.md — datasheet (see below).
2. Add the ingestion script: src/fetch_<key>.py or src/clean_<key>.py, so the source is reproducible. build_source (build_dynaword.py) expects a <file_key|speakleash_key>.jsonl.zst intermediate.
3. Register the source in src/sources.py (SOURCES) — required, or make_docs never sees it. Copy an existing entry's shape: pretty, license, license_spdx, traceable, upstream, domain, created, is_ocr, and the speakleash_key/file_key. If the datasheet is hand-authored (rich provenance or a fixed add date), add "custom_datasheet": True so make_docs leaves it alone.
4. Add a contract test: src/test_<key>_contract.py (canonical schema, non-empty text, positive token counts, uniform source/license, stats-file consistency). Run python3 -m pytest src/ before committing.

What the PR must show

These two are not optional — they are the difference between a source a reviewer can accept and one that sits in the queue. Treat every source PR as a worked example other contributors will copy.

1. A sample of the data, inline in the PR description. A few real documents (or truncated ones), verbatim, so a reviewer can see at a glance what kind of text this actually is — prose, transcripts, boilerplate, OCR noise, HTML leftovers. Include the metadata columns too, not just text. Don't make the reviewer download a parquet to find out.

2. Provenance and licensing, written out in the datasheet (data/<key>/<key>.md) and summarized in the PR description:

   • Where the data comes from — the concrete origin (institution, portal, API, dump), not just a domain name. Link it.
   • Under what license, and where that license statement lives — link the exact terms-of-use page, API docs section, or statute. "Public domain because it's government data" is a claim, not a source; cite the provision.
   • What the texts are — genre, register, time span, language variety, whether they are OCR'd, machine-translated, or user-generated.
   • How it was collected and filtered — dedup, minimum length, language ID, anything dropped and why.
   • The argument for inclusion — what this adds that the corpus does not already have, and any known bias or quality caveat a downstream user should weigh.

Yes, this is meta work on top of the fetching. That is the point: the datasheet is the artifact other people read to learn how to contribute well.

Normalize the text before you build

build_dynaword.py applies only minimal gates — strip, len < 200, Polish diacritic ratio, OCR alpha ratio, exact sha1 dedup. It does not clean text. src/normalize_schema.py is a schema/stats tool despite the name; it never touches text. So normalization is the fetcher's job, and today each fetcher reimplements it (fetch_govpl.py:43 and fetch_saos.py:42 carry byte-identical html_to_text; clean_samorzad_gov_pl.py:65 a third variant). If you are writing a new fetcher, factor the shared parts out rather than pasting a fourth copy.

What a fetcher should do to text before writing the .jsonl.zst:

• Unicode: NFKC normalize. Map non-breaking/thin/zero-width spaces to plain space (or drop), strip control characters and soft hyphens, normalize the quote/dash/ellipsis zoo. Repair mojibake if the upstream encoding is unreliable.
• Whitespace: collapse runs of spaces/tabs, trim per line, cap blank runs at one empty line. Do not flatten paragraph breaks — they carry structure.
• Structural junk: navigation, cookie banners, "share this", pagination, footnote back-references, and — for OCR/PDF sources — page headers/footers, running titles, and hyphenation split across line breaks.
• Numbering: strip standalone chapter/section/page numbers and repeated heading numerals (1., Art. 5., Rozdział III) only where they are layout artifacts. In dziennik_ustaw or saos the article numbering is content — removing it destroys the document. Judge per source, and say what you did in the datasheet.
• Boilerplate: near-identical blocks repeated across most documents of a source (license footers, institutional disclaimers, "Pokaż odpowiedź"-style UI chrome) should be detected by frequency across the shard and removed, not hand-listed.
• Personal data: scrub before the parquet is written, not after. At minimum email addresses, phone numbers, and national identifiers (PESEL, NIP, REGON, account numbers). Replace with a stable placeholder ([PII], [Telefon]) rather than deleting, so sentence structure survives. Names of public officials acting in an official capacity are not PII and should stay — removing them would gut parliamentary and judicial sources.

Two rules about all of the above:

1. The normalization must live in the committed fetch/clean script, so the shard is reproducible. A shard whose datasheet describes a cleaning step that no script in src/ performs is not reproducible, however good the intent.
2. Report it in the datasheet: which steps ran, and what the gates dropped. Include a handful of before/after excerpts in the PR — this is the fastest way for a reviewer to see whether normalization ate real content.

Cut the release (the part that gets skipped)

1. In src/make_docs.py: bump VERSION and RELEASE_DATE. Add a row for the new version to the version table and a row for yourself to the Contributors table (both are literal rows in the template).
2. Regenerate the phrase-frequency report and charts (registry-independent — it globs data/*/*.parquet):

   python3 src/pattern_frequency_report.py
   

   Writes artifacts/pattern_frequency_hf_snippet.md + 10 PNGs. Splice the tables and chart embeds into the README "Results" section by hand.
3. Update README.md by hand: header totals, version table, source table row, Contributors row. The document/token totals must equal the sum of the per-source stats.json files.
4. Prepend the release notes to CHANGELOG.md under a new ## vX.Y.Z (date) heading. History is append-only — never rewrite earlier releases.

Do not commit

*.log (fetch/build logs), .DS_Store, src/__pycache__/. Only logs/ is in .gitignore — root-level logs and OS cruft are not, so check git status before staging. *.parquet is LFS-tracked; commit the pointer, not the blob.

Known drift / cleanup opportunities

• biblioteka_nauki, europeana, parlamint_pl are in SOURCES but have no data/ shard (make_docs skips them with ! no stats). Intentional placeholders or stale — confirm before relying on build_dynaword.py --all.
• The global ADDED datasheet stamp is a footgun: give datasheets a per-source add date (or custom_datasheet) before making make_docs regenerate them.