ft-verify

name	ft-verify
description	Stage-2 verification of first-translation flips using INDEPENDENT Claude subagents (subscription, not API key) with full evidence capture. Use to verify proposed badge changes (demotes/promotes) before writing any public claim — the binding step that catches AI-fabricated priors. Each subagent reports its actual search queries + sources, persisted to the first_translation_attempts provenance log.

FT Verification (Stage 2) — independent Claude subagents

The first-translation pipeline is two-stage: a cheap Gemini adjudicator generates candidate verdicts (Stage 1), then this skill independently verifies the consequential ones (Stage 2) before anything touches a public surface. We proved a single pass can't be trusted — ~63% of the Gemini adjudicator's "a prior exists" claims were fabricated. So every flip gets re-checked by a different model family (Claude, not Gemini) to break correlated error.

Why Claude subagents (not the Gemini gate): (1) genuine independence — different training/search, so the blind spots don't correlate; (2) far richer, auditable evidence (the subagent reports every query + every source + what each showed); (3) runs on the subscription, no API key / no Gemini rate limit. Context: issue #2564; .claude/docs/ft-verification-runbook.md.

When to use

• After a Stage-1 adjudication run (ft-gemini-adjudicate.mjs) proposes badge changes.
• Before applying ANY is_first_translation flip or catalog write. Never write a public claim on a single-pass verdict.

Inputs

A set of flips, each with: book_id, work (title), author, lang, and a direction:

• demote — Stage 1 said a prior exists → verify the prior is REAL and COMPLETE.
• promote — Stage 1 said first / no prior → try to REFUTE by FINDING a prior.

Process

1. Batch the flips (~6–8 per round) and spawn one general-purpose subagent per book (model: sonnet), in parallel (one message, multiple Agent calls).
2. Each subagent uses the appropriate prompt below. It MUST do real WebSearch/WebFetch and return ONLY the JSON contract (no prose).
3. Capture the evidence — ALL sinks, one command. Write the round's results to scripts/output/ft-evidence-<date>/roundN-results.json (include a registry_rows[] array on every result whose prior was CONFIRMED real — structured translator/year/title/completeness/ source_language/source_url), then run: node scripts/maintenance/ingest-ft-verify-results.mjs <roundN-results.json> --apply That single command writes Sink A (the first_translation_attempts ledger row with the verbatim queries + per-source findings) and Sink C (every confirmed real translation into the translation_catalogs registry, dedup-on-write, with completeness set). Sink B (the verdict) stays derived-from-evidence; the public flag stays behind the sign-off reconcile. A round is not done until this script has run. Do not hand-roll the writes — three sinks scattered across sessions is exactly how the six-store evidence sprawl happened (#2780).
4. Output survivors vs rejects as a reviewable diff. Do NOT apply badge flips — bring the diff to Derek for sign-off (public bibliographic claims need explicit approval; back up before any write).

The three sinks (write contract — spec: .claude/docs/ft-enumeration-three-sink-spec.md)

Sink	Store	Written by
A — evidence	first_translation_attempts (append-only)	ingest-ft-verify-results.mjs (step 3)
B — verdict	book.first_translation	derive-ft-verdict-from-attempts.ts (never this skill)
C — registry	translation_catalogs (the flywheel of verified positives)	ingest-ft-verify-results.mjs (step 3)

Survivor rules

• demote survives (the demote is valid) only if result == confirmed_complete with a real URL. confirmed_partial / not_found → the badge was right; KEEP it.
• promote survives (the first stands) if result == none_found or only_partial_exists. complete_prior_found → a prior exists; do NOT promote as a plain "first". BUT see first_modern.

first_modern — a complete prior can still be a badgeable first (don't collapse it)

A complete prior does NOT always defeat the claim. The graded model (src/lib/first-translation/types.ts) has a first_modern verdict: if the ONLY complete prior(s) are pre-1900, the text grades first_modern (a first-family badge, "First Modern Translation"), not not_first. A 480-year-old Tudor crib is not a readable modern English translation. derive-from-evidence.ts makes this call automatically — but ONLY if the prior's year is a structured pub_year field in the registry row. So the one thing that CANNOT be dropped is the year: every found prior MUST carry pub_year. Do not bury "translator, 1547, title" in a single string — the grader can't parse it and a genuine first-modern collapses to not_first (this is exactly how Whittington 1547 mis-graded De remediis fortuitorum, 2026-07-13). Always emit registry_rows[] with a real pub_year for every confirmed prior, whatever the result value.

Subagent prompt — DEMOTE (verify a claimed prior is real)

You are verifying a "first English translation" claim for a library audit. Stage 1 says a PRIOR English translation of this work exists — be skeptical, AI invents plausible translators/years. WORK: "" by (). CLAIMED prior: , . Do REAL web research (WebSearch/WebFetch): WorldCat, archive.org, Google Books, HathiTrust, the publisher, tradition-appropriate catalogues, scholarship. Confirm whether THIS specific translation actually exists, and whether it is COMPLETE or only partial/excerpt. Disambiguate same-named works and authors. Record the YEAR of every prior you find — a pre-1900-only prior means the text is a "first MODERN translation", so the year decides the grade. Return ONLY JSON: {"result":"confirmed_complete|confirmed_partial|not_found|uncertain","priors":[{"translator":"","year":0,"english_title":"","completeness":"complete|partial|excerpt","source_url":""}],"prior":"<translator, year, title — human-readable summary of the strongest prior, or empty>","evidence_url":"<real url or empty>","queries_run":["every search you ran, verbatim"],"sources_consulted":[{"url":"...","found":"<one line: what it showed>"}],"reasoning":"<2-3 sentences>"}

Subagent prompt — PROMOTE (try to refute "it's a first")

You are verifying a "first English translation" claim. We are about to claim "" by () is a FIRST English translation — NO complete prior exists. BE SKEPTICAL: try to REFUTE it by FINDING a prior complete English translation. AI tends to wrongly assume "no prior" — fight that. Do REAL web research (WorldCat, archive.org, Google Books, HathiTrust, publisher, tradition-appropriate sources for , scholarship). Separate THIS work from related works / other editions; flag if it's a multi-work container/anthology/manuscript-miscellany (the claim is then ill-posed). If you DO find a complete prior, record its YEAR — a prior that is only pre-1900 does not defeat the claim outright, it grades the text a "first MODERN translation", so the year is load-bearing. Return ONLY JSON: {"result":"complete_prior_found|only_partial_exists|none_found|uncertain","priors":[{"translator":"","year":0,"english_title":"","completeness":"complete|partial|excerpt","source_url":""}],"prior":"<translator, year, title — human-readable summary of the strongest prior, or empty>","evidence_url":"<real url or empty>","queries_run":["every search, verbatim"],"sources_consulted":[{"url":"...","found":"<one line>"}],"reasoning":"<2-3 sentences>"}

Notes

• Quality observed (2026-06-21): Claude subagents disambiguated authors (Johann vs Georgius Agricola), corrected authorship (Detharding vs Agricola), and caught container/miscellany cases the Gemini gate missed — at 45–72 tool calls each.
• For famous-adjacent works where BOTH a Gemini and a Claude pass may share a blind spot (e.g. an old scholarly edition like Allberry's 1938 Manichaean Psalm-Book), route to a human specialist — that's the only layer that removes correlated error.
• The Gemini equivalent (scripts/eval/ft-verify-gate.mjs) is the cheap/scalable fallback when the flip volume is large; prefer Claude subagents for the high-stakes, smaller sets.