fact-check

Fact-check

Every number, quote, date and attribution in the piece traces to a source the reader could open. What can't be traced gets attributed, relabelled or cut. The ledger is the record; the edited piece is the result.

"Verified" means a source you read in this run says it, in the same sense the piece uses it. It never means "sounds right" or "I remember this".

Keep the boundary clear

• This skill owns truth: whether each claim is supported, by what, as of when.
• Use the humanize skill for voice and synthetic-sounding prose. Use the landing-page skill when the page's argument or structure is the problem. Run this skill after either of them, because rewrites create new claims.
• Researching a topic from scratch is not this job. This skill starts from a draft, a deck, a script or a list of claims.

Choose the mode

Infer the mode and state it in one line, so the user can correct it.

• Audit (default): a draft with no single source. Check every claim against the open web, primary first.
• Source check: the user supplies the source (a transcript, a report, a document, a dataset, an interview) and the piece is built from it. Every claim must trace to a location in that source: timestamp, page, line or cell. A claim that is true elsewhere but absent from the source is flagged "not in source", because the piece presents it as the source's.
• Pre-publish gate: the piece is final or nearly final. Return pass or fail with a blocking list. Reuse an earlier ledger if one exists: re-check only changed text, volatile facts and anything checked more than about 90 days ago. Fix nothing beyond the blocking list without asking.

Before extracting claims

1. Read the whole piece once, including headlines, captions, alt text, speaker notes, chart labels, footnotes and image text. Note the audience, the publish date (planned or actual) and the one claim the piece stands on. That central claim is checked first.
2. Fix today's date. Write it at the top of the ledger. Every "as of", every "recently" and every day count is measured from it or from the publish date.
3. Define terms before judging anything. For every product, model, law, metric or event the piece names, search it before evaluating claims about it. If a name is unfamiliar, assume it is newer than your training data, not that it is wrong or a typo. Never substitute a guessed meaning. If the piece uses a term in a sense its primary source doesn't, that is a finding.
4. Check access. Without web access, say so in one line, run source check against whatever the user supplied, and mark every other claim unverifiable (not checked). Never mark a claim verified from memory. Training data is a lead, not a source.