Agent skill

Verify Refs

by Aperivue in Aperivue/medsci-skills

A skill your agent uses when checking whether a manuscript's references are real.

MITAuto-check passedResearch & Science

Install Verify Refs

skills CLI
$ npx skills add Aperivue/medsci-skills --skill verify-refs -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Aperivue/medsci-skills verify-refs --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Aperivue/medsci-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/verify-refs .claude/skills/verify-refs && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
verify-refs
GitHub stars
329
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
1,567 words
Files
35 (incl. scripts, references)
Skills in repo
54
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when checking whether a manuscript's references are real.

  • Works in 3 steps: Manuscript or bibliography: .md, .docx,… → Optional project root. Default: current… → Optional flags: --offline (extract and…
  • Checking whether a manuscripts references are real
  • SKILL.md covers Inputs, Deterministic Script, Output Contract (v1.3.0) and Workflow, plus 2 more sections
  • Runs Python and Shell scripts from its folder; calls python3 and python

What it does

Verify Refs is an agent skill from Aperivue/medsci-skills. Use when checking whether a manuscript's references are real. Audits each citation against PubMed and CrossRef, flags fabricated or mismatched entries and writes qc/referenceaudit.json. Audit-only; never edits references or refs.bib. Citation-key checks are /manage-refs.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 39 other files, including scripts and reference files (for example `references/claim_evidence_workflow.md`, `references/manual_checkpoint_guide.md` and `scripts/_claim_evidence.py`).

It sits in Research & Science, covering Citation management and Academic paper search. It works with PubMed. The repository describes itself as: Agent Skills for medical research — literature search, reporting-guideline & citation checks, statistics, publication figures, submission. Works with Claude Code, Codex, Cursor &… The licence is MIT.

When your agent uses it

  • Checking whether a manuscripts references are real
  • Tasks that involve Citation management
  • Tasks that involve Academic paper search

Example prompts

  • “/verify-refs”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. Manuscript or bibliography: .md, .docx, .bib, .txt, or .tsv.
  2. Optional project root. Default: current working directory.
  3. Optional flags: --offline (extract and classify without API calls), --timeout N (HTTP

What it can do on your machine

Read from SKILL.md and the folder at commit 3b14ae2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 13 files in scripts/ (Python and Shell, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Verify Refs loads about 3.1k tokens when it runs, and up to ~5.7k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 1,567 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from Aperivue/medsci-skills at commit 3b14ae2, republished under its MIT licence (© Aperivue). 1,567 words, ~3,135 tokens.

Download SKILL.mdSave it as .claude/skills/verify-refs/SKILL.md (or your agent's skills folder). This skill also uses 34 other files; get the full folder from GitHub.
name
verify-refs
description
Use when checking whether a manuscript's references are real. Audits each citation against PubMed and CrossRef, flags fabricated or mismatched entries and writes qc/reference_audit.json. Audit-only; never edits references or refs.bib. Citation-key checks are /manage-refs.
metadata.triggers
verify refs, verify references, citation audit, reference hallucination, fabricated references, bibliography check, PMID check, DOI check

Verify References (Audit-Only)

Audit an existing manuscript or bibliography for fabricated or mismatched references. This skill never writes to references/ or manuscript/_src/refs.bib: corrections flow through /lit-sync (Zotero → Better BibTeX → refs.bib), and /manage-project's scripts/validate_project_contract.py flags any references/* file written here as drift. It does not discover literature (/search-lit), and it never replaces a missing or bad citation with a plausible alternative — replacements go through /search-lit or the user.

Inputs

  1. Manuscript or bibliography: .md, .docx, .bib, .txt, or .tsv.
  2. Optional project root. Default: current working directory.
  3. Optional flags: --offline (extract and classify without API calls), --timeout N (HTTP timeout seconds), --strict, --no-openalex.

For markdown manuscripts with pandoc [@bibkey] citations, run a citation-key check first (/manage-refs's check_citation_keys.py, or your reference manager's). It catches mis-keyed cites; verify_refs.py catches fabricated metadata.

Deterministic Script

Run the bundled script rather than verifying citations by memory:

bash
python "${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:

bash
"${CLAUDE_SKILL_DIR}/scripts/verify_cli.sh" manuscript/index.qmd --strict

--strict forbids --offline and exits non-zero on any UNVERIFIED row. Read references/manual_checkpoint_guide.md for when to run it and what to do per status.

Lookups run PubMed (PMID) → CrossRef (DOI; doi.org's handle registry when CrossRef answers 404) → OpenAlex, with a PubMed title search last. Every OK row rests on DOI, PMID, CrossRef, or PubMed title evidence; a failed lookup is recorded as UNVERIFIED, never silently passed. A title search, in either index, counts only when a returned title matches the cited one (the same token-similarity guard), so a made-up title cannot earn OK from a search that merely returned something.

OpenAlex tier. It recovers conference and non-DOI citations (NeurIPS / ICLR / ACL, common in medical-AI manuscripts) that PubMed and CrossRef miss, and is called only when no author list was obtained yet. It resolves by DOI, otherwise by a title search behind a token-similarity guard so a fabricated title cannot earn OK. OpenAlex names have no family/given split, so they support only an existence check and a tolerant first-author membership check — never the strict positional or author-count MISMATCH, which stays with PubMed efetch / CrossRef. An OpenAlex miss is UNVERIFIED, never FABRICATED. --no-openalex restricts verification to PubMed + CrossRef.

Output Contract (v1.3.0)

qc/reference_audit.json (schema_version 4) is the only output; this skill is its sole writer and writes no TSV and no library.bib.

  • records[]: per reference, status, note, evidence, cited_authors[] / actual_authors[], cited_author_count / actual_author_count. status is one of:
    • OK: a lookup confirmed the work (DOI, PMID, or a title search passing the similarity guard) and the authors agree.
    • MISMATCH: the work exists but the citation is wrong: its authors differ (Gate 4), or its DOI/PMID does not exist while a lookup still found the work (note = "wrong identifier…").
    • FABRICATED: the cited identifier does not exist and nothing else found the work (details below).
    • UNVERIFIED: nothing confirmed or refuted it (a failed lookup, a DOI held only by another registration agency that no index confirmed, no identifier and no title match, a PubMed title-only match whose authors could not be compared or differ (note = "title_only: …"), or --offline).
  • counts and duplicate_findings[] (Gate 5).
  • submission_safe: no FABRICATED, no MISMATCH, and duplicate_findings empty. fully_verified additionally requires no UNVERIFIED.
  • submission_safe tolerates UNVERIFIED rows because offline runs produce them; before a submission, resolve each one (confirm it by hand and say how, or remove the citation) — do not ship an UNVERIFIED reference as if it were checked.
  • source_sha256 and audited_ref_ids: what was audited. A later reader compares them with the current bib; a changed bib makes the audit stale.

Workflow

  1. Identify the input file and project root.
  2. Run scripts/verify_refs.py.
  3. Read qc/reference_audit.json. Every verdict comes from it; never mark a row OK yourself.
  4. Report all FABRICATED and MISMATCH rows first (from records[]).
  5. Report all duplicate_findings[] entries (verbatim PMID/DOI duplicates — cite renumbering required).
  6. If UNVERIFIED rows remain, list them as manual checks and do not call the manuscript fully submission-safe. Check note on every row, whatever its status: pagination_placeholder (e000–e000 / in press / TBD / forthcoming) needs the citation resolved before submission; /self-review Phase 2.5c decides whether any is a P0 blocker.
  7. If the user needs a human-readable table, summarize from records[] in chat — do not write a TSV.

Quality Gates

  • Gate 1: stop submission if any row is FABRICATED.
  • Gate 2: require user confirmation before accepting UNVERIFIED references.
  • Gate 3: rerun after any reference edits.
  • Gate 4 (full-author cross-check): the authoritative author list comes from PubMed efetch.fcgi (XML) when a PMID is present — preferred because CrossRef is unreliable for given names — with CrossRef and PubMed esummary as fallbacks. For BibTeX inputs every cited family name is compared index-by-index, and the cited and source author counts are compared, after normalizing case, diacritics, hyphen vs space, and name particles ("von", "van", "de"); one name may be a whole word of the other ("Garcia" / "Garcia-Lopez"), never a mere substring ("Li" / "Williams"). A row whose DOI/PMID resolves but whose authors differ at any index or in count becomes MISMATCH: note = "first-author hallucination suspected" for the first author, note = "non-first-author hallucination or count mismatch" for #2..#N or the count. A correct first author does not establish the rest of the list. Plain-text / TSV inputs, and lists that cannot be parsed confidently, degrade to the first-author check (skipped if even that is empty). A PubMed title-only match is OK only when the matched record's esummary authors were compared with the cited ones and agree; otherwise it stays UNVERIFIED. A declared truncation — BibTeX and others, or a _audit_truncated = <N> field — turns a shorter-than-source count into a note; citing more authors than the source is always flagged.
  • Gate 5: verbatim PMID or normalized-DOI duplicates in the reference list are MAJOR findings in duplicate_findings[]; submission_safe == true requires the list to be empty.
  • Gate 6: a reference whose raw entry still carries e000–e000, in press, TBD, or forthcoming is marked UNVERIFIED with note = "pagination_placeholder". verify-refs is manuscript-agnostic and only flags; /self-review Phase 2.5c, with the manuscript in hand, decides whether the citation is method- or headline-load-bearing and hence a P0 blocker.
Show full SKILL.md (580 more words)Show less

Citation-metadata confusion is not fabrication. DOI-suffix digits that look like, but differ from, the article number (a DOI tail "77196" against article 26068) are cosmetic. When the identifier resolves and the authors match, do not report the row as fabricated: the script returns FABRICATED only for an identifier that does not exist — a PMID PubMed has no record of, or a DOI that CrossRef answers 404 for and that doi.org's handle registry (which spans every registration agency: CrossRef, DataCite, mEDRA, JaLC) reports as not found — and only when no lookup found the work some other way. After a CrossRef 404 the DOI verdict is:

doi.org handle registryanother lookup finds the workstatus
not foundnoFABRICATED
not foundyes (a title search passing the similarity guard)MISMATCH — a real paper cited with a wrong DOI
found (registered with another agency)no / yesUNVERIFIED / OK
lookup failed (network, 5xx, unexpected reply)no / yesUNVERIFIED / OK

A DOI field that is not a well-formed DOI (a placeholder such as n/a) is not sent to doi.org and stays UNVERIFIED, and so does a legacy SICI DOI (10.1002/(SICI)…) that doi.org does not find, since its punctuation is easily cut in extraction. A real identifier with wrong authors is MISMATCH (Gate 4).

Claim Fidelity — does the source say what you say it says?

verify_refs.py answers whether a reference is real and whose it is, not whether the sentence citing it is true of it. scripts/check_claim_fidelity.py checks the claims that have a checkable answer against full texts already downloaded and converted (/fulltext-retrieval produces exactly that layout; this script never fetches anything):

bash
python3 "${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
VerdictSeverityFires when
CITED_QUOTE_ABSENTmajorQuoted text attributed to a source is not in it in any reading order.
CITED_QUOTE_UNRESOLVEDpromptThe quote matched with a word or two missing — the signature of a dirty extraction, not of a fabrication. Look; do not assume.
ATTRIBUTION_UNSUPPORTEDpromptNot 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_UNSUPPORTEDprompt"reports three strategies [12]" where the source discusses that noun but never that count near it.

Only the quote verdict can fail --strict; the others are prompts to go read the source, because paraphrase is legitimate. Read the "not checked" lines: a citation with no full text on disk is reported unresolved, and a source whose extracted text is an abstract is reported as too short to judge. Silence means "nothing checkable was wrong", not "everything is right".

Known limits: a quote whose words all appear in order with source tokens between them (INTERLEAVED) is treated as verified, so a quotation that drops a source word such as "not" produces no finding; reread every quotation that changes a negation or qualifier.

Sentence-level source evidence table

qc/claim_fidelity.json also carries evidence_rows: one per recognized prose sentence/citation pair, with manuscript coordinates, source-text and PDF hashes, advisory retrieval identity, and an assessor-entered comparison that starts not_assessed even when the bibliographic status is OK and no probe fires.

bash
python3 "${CLAUDE_SKILL_DIR}/scripts/check_claim_fidelity.py" \
  --manuscript manuscript/manuscript.md --bib manuscript/_src/refs.bib \
  --fulltext-dir fulltext/ --retrieval-report pdfs/retrieval_report.json \
  --reference-audit qc/reference_audit.json \
  --out qc/claim_fidelity.json --evidence-table qc/claim_fidelity.md

Enter pages, excerpts, metric/unit/denominator, population, direction, and a named assessment only after inspecting the actual source; equal numbers or matching words do not establish support. Record whether the assessor used AI assistance, and never describe an AI-generated assessment as human approval. Rerun with --reviewed-report qc/claim_fidelity.json to retain annotations; changed inputs leave old assessments unresolved. The Markdown table is a derived view, not a second editable evidence store. Read references/claim_evidence_workflow.md before entering or carrying forward an assessment.

© Aperivue, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 34 other files (scripts, references) in skills/verify-refs of Aperivue/medsci-skills.

  • SKILL.md
  • references/claim_evidence_workflow.md
  • references/manual_checkpoint_guide.md
  • scripts/_claim_evidence.py
  • scripts/_quote_match.py
  • scripts/check_claim_fidelity.py
  • scripts/claim_fidelity_challenge/fixture/fulltext/10.1000_synthetic.oversight.md
  • scripts/claim_fidelity_challenge/fixture/fulltext/10.1000_synthetic.stub.md
  • scripts/claim_fidelity_challenge/fixture/manuscript_numbered.md
  • scripts/claim_fidelity_challenge/fixture/manuscript_shortsource.md
  • scripts/claim_fidelity_challenge/fixture/manuscript_supported.md
  • scripts/claim_fidelity_challenge/fixture/manuscript_unsupported.md
  • scripts/claim_fidelity_challenge/fixture/refs.bib
  • scripts/claim_fidelity_challenge/verify.sh
  • scripts/verify_cli.sh
  • scripts/verify_refs.py
  • … and 19 more

Open the folder on GitHubat commit 3b14ae2

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in Aperivue/medsci-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Verify Refs next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Verify Refs compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Verify Refs this skillAperivue/medsci-skills3291 repos~3.1kAutomated safety check: PassMIT
Literature Reviewneflibata-feng/MyArxiv-Agent12621 repos~5.9kAutomated safety check: NotesMIT
Citation ManagementK-Dense-AI/claude-scientific-writer2.4k3 repos~3.9kAutomated safety check: NotesMIT
Citation Managementneflibata-feng/MyArxiv-Agent12620 repos~8.1kAutomated safety check: NotesMIT
Academic Search and Citation RouterYuan1z0825/nature-skills46k—~884Automated safety check: PassApache-2.0
Nature Academic Searchjing1312/nature-figure-skill167—~1.3kAutomated safety check: NotesMIT

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Works with

Questions about Verify Refs

What does Verify Refs do?

A skill your agent uses when checking whether a manuscript's references are real. Verify Refs is an agent skill from Aperivue/medsci-skills. Use when checking whether a manuscript's references are real.

When should I use Verify Refs?

Verify Refs fits situations like: checking whether a manuscripts references are real; tasks that involve Citation management; tasks that involve Academic paper search.

How do I install Verify Refs in Claude Code?

Run `npx skills add Aperivue/medsci-skills --skill verify-refs -a claude-code`. Or copy the skill folder (skills/verify-refs in Aperivue/medsci-skills) into .claude/skills/verify-refs in your project. Claude Code loads it when a task matches its description.

How do I install Verify Refs in Codex?

Run `npx skills add Aperivue/medsci-skills --skill verify-refs -a codex`. Or copy the skill folder (skills/verify-refs in Aperivue/medsci-skills) into .agents/skills/verify-refs in your project. Codex loads it when a task matches its description.

Can I use Verify Refs in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Aperivue/medsci-skills --skill verify-refs -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/verify-refs, .gemini/skills/verify-refs, .github/skills/verify-refs and .opencode/skills/verify-refs in your project.

What does Verify Refs need to run?

Going by SKILL.md and its folder, Verify Refs needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (python3 and python). Our summary lists: Python 3; A Bash shell.

Does Verify Refs access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Verify Refs safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Verify Refs use?

Verify Refs is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Verify Refs use?

About 3.1k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.5k tokens, read only when the agent opens those files.

What are the alternatives to Verify Refs?

Skills that share tags, products or a category with Verify Refs: Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars), Citation Management (K-Dense-AI/claude-scientific-writer, 2.4k stars), Citation Management (neflibata-feng/MyArxiv-Agent, 126 stars) and Academic Search and Citation Router (Yuan1z0825/nature-skills, 46k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Verify Refs?

Aperivue (a GitHub organization) maintains it in Aperivue/medsci-skills, which has 329 GitHub stars. The repository holds 54 skills in this directory. The repository was last updated on October 5, 2026.

Source: Aperivue/medsci-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.