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Get Started Free →Audit-only verification of manuscript references against PubMed and CrossRef. Detects fabricated or mismatched citations and writes qc/reference_audit.json. Does not modify references/ or refs.bib.
.claude/skills/aperivue-verify-refs/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-20 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 162% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 225% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 99% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 172% | 0% |
You help a medical researcher prevent reference hallucinations before submission. This skill audits an existing manuscript or bibliography. It does not write to references/ or manuscript/_src/refs.bib. It does not discover new literature; use /search-lit for discovery and /lit-sync for bib management.
.docx manuscripts inherited fromcoauthors or external editors.
/sync-submission freezes a journal package..md, .docx, .bib, .txt, or .tsv.--offline: extract and classify references without API verification.--timeout N: HTTP timeout seconds.For markdown manuscripts using pandoc [@bibkey] citations, validate citation keys first to catch undefined/unused keys before this audit. If you also use the companion manage-refs skill, run its check_citation_keys.py for this; otherwise use your reference manager's citation-key check.
Then run verify_refs.py against the .bib to validate each entry against PubMed/CrossRef. The two checks are complementary: a citation-key check catches mis-keyed cites; verify_refs.py catches fabricated metadata.
Run the bundled script rather than verifying citations by memory:
bashpython "${CLAUDE_SKILL_DIR}/scripts/verify_refs.py" manuscript/manuscript.md --project-root .
For hooks or quick manual runs, use the wrapper:
bash"${CLAUDE_SKILL_DIR}/scripts/verify_cli.sh" manuscript/manuscript.md --offline
Manual pre-submission strict run (Phase 1A.5):
bash"${CLAUDE_SKILL_DIR}/scripts/verify_cli.sh" manuscript/index.qmd --strict
--strict forbids --offline and exits non-zero on any UNVERIFIED row. Full checkpoint protocol: references/manual_checkpoint_guide.md.
The script uses DOI, PMID, CrossRef, PubMed E-utilities, and OpenAlex where available. If network verification fails, it records UNVERIFIED rather than silently passing.
OpenAlex tertiary index (existence recovery). PubMed covers only biomedical literature and CrossRef's conference-proceedings coverage is uneven, so NeurIPS / ICLR / ACL-style citations — common in medical-AI manuscripts — fall through both and would be marked UNVERIFIED. After the PubMed and CrossRef tiers, the script consults OpenAlex (https://api.openalex.org, free, no API key) only when no authoritative author list was obtained yet (so a reference already resolved by PubMed/CrossRef incurs no extra call). It resolves by DOI when present, otherwise by a title search guarded by a token-similarity threshold so a fabricated title cannot earn a spurious OK. This is the free analogue of the second index (e.g. Scopus) that journal submission portals run alongside CrossRef. OpenAlex display names carry no structured family/given split and mix First Last with Last, First forms, so OpenAlex-sourced authors support an existence check plus a tolerant first-author membership check, but never drive the strict positional or author-count MISMATCH (those stay reserved for PubMed efetch / CrossRef). An OpenAlex miss is recorded as UNVERIFIED, never FABRICATED. Pass --no-openalex to restrict verification to PubMed + CrossRef.
| Artifact | Path | Purpose | |---|---|---| | Audit JSON | qc/reference_audit.json | Sole output — row-level status (OK/MISMATCH/UNVERIFIED/FABRICATED), counts, cited_authors[]/actual_authors[], duplicate_findings[], submission-safe flag, full records |
v1.2.0 (2026-05) adds duplicate_findings[] to the audit JSON. Verbatim PMID or DOI duplicates within the reference list are flagged as MAJOR findings (resolves /peer-review Phase 2A P7). DOI normalization strips https://doi.org/, http://dx.doi.org/, doi: prefixes plus trailing slashes before comparison so https://doi.org/10.x/abc/ and 10.x/abc collapse to one key. Both submission_safe and fully_verified now require duplicate_findings to be empty.
v1.3.0 (2026-05) extends the author cross-check from first-author-only to the full author list and bumps schema_version to 4. For BibTeX inputs, every cited author family name is compared index-by-index against the authoritative source, and the cited-vs-source author counts are compared. PubMed efetch.fcgi (XML full record) is the truth source when a PMID is present — it is authoritative for given/family names where CrossRef is not (a documented case where CrossRef returned a wrong given name that PubMed efetch corrected). Records now carry cited_authors[], actual_authors[], cited_author_count, and actual_author_count. Motivation: a real AI-assisted manuscript registered a reference with a correct first author but seven of ten fabricated co-author names, and the first-author-only check passed it. Plain-text / TSV inputs, which cannot be parsed into a confident full list, degrade gracefully to the first-author check.
Removed in Phase 1A.2 (per docs/artifact_contract.md):
references/verified_references.tsv — record-level details now live inside reference_audit.json under records[].references/library.bib — never this skill's concern. /search-lit produces candidates; /lit-sync (via Better BibTeX) writes manuscript/_src/refs.bib.Sole-writer enforcement: scripts/validate_project_contract.py will flag any references/* file written by this skill as drift.
scripts/verify_refs.py.qc/reference_audit.json.FABRICATED and MISMATCH rows first (from records[]).duplicate_findings[] entries (verbatim PMID/DOI duplicates — cite renumbering required).UNVERIFIED rows remain, list them as manual checks and do not call themanuscript fully submission-safe. Rows with note = "pagination_placeholder" (e000–e000 / in press / TBD / forthcoming) need the citation resolved before submission; /self-review Phase 2.5c decides whether any is a P0 blocker.
records[] in chat — do not write a TSV.FABRICATED.UNVERIFIED references.author list is cross-checked against the authoritative source (PubMed efetch preferred, then CrossRef, then PubMed esummary). A row whose DOI/PMID resolves but whose cited authors do not match — at any index, or in total count — is downgraded to MISMATCH. First-author mismatches get note = "first-author hallucination suspected"; #2..#N family or count mismatches get note = "non-first-author hallucination or count mismatch". This catches the LLM failure mode where a real DOI is paired with invented author names anywhere in the list, not just the lead author. Intentional CSL et-al truncation (cited fewer than source) can be silenced per-entry with a BibTeX _audit_truncated = <N> field.
reference list. Verbatim duplicates (same PMID or normalized DOI) — a common LLM citation-compilation artifact — are flagged as MAJOR findings in duplicate_findings[]. submission_safe == true requires the list to be empty. Resolves /peer-review Phase 2A P7.
whose raw entry still carries e000–e000, in press, TBD, or forthcoming is not yet a fully citable record. Each is marked UNVERIFIED with note = "pagination_placeholder" (a would-be VERIFIED record is downgraded; a worse status is left unchanged). verify-refs is manuscript-agnostic and does not judge centrality — it only flags. The escalation call (is this a method- or headline-load-bearing citation, hence a P0 submission blocker?) is made by /self-review Phase 2.5c, which has the manuscript in hand.
Classification note — citation-metadata confusion is not fabrication. Digits in a DOI suffix sometimes look like a journal article number but differ from the real one (e.g., a DOI tail "77196" against article number 26068, or a "60466-1" suffix against article 6274). This is cosmetic metadata confusion, not a fabricated reference: do not record such rows as FABRICATED when the DOI/PMID resolves and the authors match. A genuine FABRICATED verdict requires a non-resolving identifier or an author cross-check failure (Gate 4), not a mismatch between a DOI suffix and an article number.
Driven by two actual incidents. First (Gate 4 origin): a manuscript had a reference cited with a plausible lead author but the correct DOI for an entirely different author's whitepaper. Pre-patch verify-refs marked it OK because the DOI resolved; post-patch it is MISMATCH. Second (v1.3.0 extension): an AI-assembled .bib registered a reference with the correct first author but seven of ten fabricated co-author names — the first-author-only check passed it, and it would have shipped to reviewers. The full-author cross-check catches it.
efetch.fcgi (XML) when aPMID is present, falling back to CrossRef (DOI) and then PubMed esummary. efetch is preferred because CrossRef is unreliable for given names.
cited_authors[],balanced-brace aware, LaTeX-accent tolerant) and compared family-by-family and by total count against actual_authors[].
German/Nordic special letters), hyphen vs space, and name particles ("von", "van", "de", ...) are normalized before matching.
first-author surname comparison, and if even that is empty it is skipped silently — no false MISMATCH from formatting ambiguity.
therefore excluded from this check.
+ et al., by design) would otherwise trip the count check; mark such entries with _audit_truncated = <N> to downgrade the count mismatch to a note.
verify_refs.py answers whether a reference is real and whose it is. It cannot answer whether the sentence citing it is true of it, and a citation can be perfectly real while the claim attached to it is not. That gap is where the failure lives: the DOI resolves, the authors match, the reference list renders, and the sentence is still wrong.
scripts/check_claim_fidelity.py checks the claims that have a checkable answer, against full texts you have already downloaded and converted (/fulltext-retrieval produces exactly that layout — it never fetches anything itself):
bashpython3 "${CLAUDE_SKILL_DIR}/scripts/check_claim_fidelity.py" \ --manuscript manuscript/manuscript.md \ --fulltext-dir fulltext/ --bib manuscript/_src/refs.bib \ --out qc/claim_fidelity.json --strict
| Verdict | Severity | Fires when | |---|---|---| | CITED_QUOTE_ABSENT | major | Quoted text attributed to a source is not in it in any reading order. | | CITED_QUOTE_UNRESOLVED | prompt | The quote matched only with foreign tokens wedged in, or a word or two missing — the signature of a dirty extraction, not of a fabrication. Look; do not assume. | | ATTRIBUTION_UNSUPPORTED | prompt | Not one content word of the attributed claim appears in the source, in any form. Paraphrase normally keeps at least one of the source's own terms. | | ORDINAL_CLAIM_UNSUPPORTED | prompt | "reports three strategies 12]" where the source discusses that noun but never that count near it. |
Only the quote verdict can fail --strict. Everything else is a prompt to go read the source, because paraphrase is legitimate and a gate that blocks on it would be turned off.
Read the "not checked" lines. A citation with no full text on disk is reported as unresolved and never guessed at, and a source whose extracted text is an abstract is reported as too short to judge — absence proves nothing against an abstract. Silence from this detector means "nothing checkable was wrong", which is not the same as "everything is right".
/fulltext-retrieval); claim fidelity reads converted textoff disk so it stays deterministic and CI-runnable.
quoted, attributed, or counted claim is supported by the source's own words.
/search-lit or user approval.
/lit-sync after this audit.volumes, or pages.
UNVERIFIED and keep it visible.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-20 | fail→pass | 16,963 | 6,315 | -63% | 1 | 1 | 0% | 3,429 | 4,677 | +36% | 0 | 0 | — |
case-05 | fail→pass | 9,574 | 2,941 | -69% | 1 | 1 | 0% | 1,576 | 4,133 | +162% | 0 | 0 | — |
case-01 | fail→fail | 5,536 | 6,206 | +12% | 1 | 1 | 0% | 232 | 3,958 | +1606% | 0 | 0 | — |
case-02 | fail→fail | 4,827 | 6,023 | +25% | 1 | 1 | 0% | 273 | 4,042 | +1381% | 0 | 0 | — |
case-03 | fail→fail | 6,213 | 6,197 | -0% | 1 | 1 | 0% | 465 | 3,863 | +731% | 0 | 0 | — |
case-04 | fail→pass | 8,124 | 4,061 | -50% | 1 | 1 | 0% | 1,310 | 4,251 | +225% | 0 | 0 | — |
case-14 | fail→pass | 13,649 | 4,725 | -65% | 1 | 1 | 0% | 2,212 | 4,412 | +99% | 0 | 0 | — |
case-06 | pass→pass | 10,821 | 4,545 | -58% | 1 | 1 | 0% | 1,789 | 4,294 | +140% | 0 | 0 | — |
case-07 | fail→pass | 10,777 | 7,232 | -33% | 1 | 1 | 0% | 1,870 | 5,082 | +172% | 0 | 0 | — |
case-08 | fail→pass | 9,522 | 2,899 | -70% | 1 | 1 | 0% | 1,606 | 4,069 | +153% | 0 | 0 | — |
case-09 | fail→pass | 7,558 | 4,864 | -36% | 1 | 1 | 0% | 1,247 | 4,450 | +257% | 0 | 0 | — |
case-15 | fail→pass | 10,548 | 4,526 | -57% | 1 | 1 | 0% | 1,802 | 4,342 | +141% | 0 | 0 | — |
case-10 | fail→pass | 13,884 | 4,827 | -65% | 1 | 1 | 0% | 2,248 | 4,395 | +96% | 0 | 0 | — |
case-11 | fail→pass | 12,811 | 3,712 | -71% | 1 | 1 | 0% | 1,959 | 4,203 | +115% | 0 | 0 | — |
case-12 | pass→pass | 11,204 | 5,958 | -47% | 1 | 1 | 0% | 1,752 | 4,596 | +162% | 0 | 0 | — |
case-13 | pass→pass | 10,075 | 5,489 | -46% | 1 | 1 | 0% | 1,609 | 4,580 | +185% | 0 | 0 | — |
case-16 | fail→pass | 9,466 | 6,768 | -29% | 1 | 1 | 0% | 1,674 | 4,943 | +195% | 0 | 0 | — |
case-17 | fail→pass | 11,350 | 2,873 | -75% | 1 | 1 | 0% | 1,944 | 4,017 | +107% | 0 | 0 | — |
case-18 | pass→pass | 11,570 | 8,688 | -25% | 1 | 1 | 0% | 1,933 | 5,102 | +164% | 0 | 0 | — |
case-19 | fail→pass | 13,199 | 7,244 | -45% | 1 | 1 | 0% | 2,093 | 4,757 | +127% | 0 | 0 | — |
case-21 | fail→pass | 4,038 | 6,806 | +69% | 1 | 1 | 0% | 171 | 4,473 | +2516% | 0 | 0 | — |
case-22 | fail→pass | 4,157 | 10,840 | +161% | 1 | 1 | 0% | 159 | 4,840 | +2944% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 17 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +68 percentage points is the difference between those two pass rates over the 17 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.