citation-audit
Zero-context verification that every bibliographic entry in the paper is real, correctly attributed, and used in a context the cited paper actually supports. Uses a fresh cross-model reviewer with web/DBLP/arXiv lookup to catch hallucinated authors, wrong years, fabricated venues, version mismatches, and wrong-context citations (cite present but the cited paper does not establish the claim). Use when user says \"审查引用\", \"check citations\", \"citation audit\", \"verify references\", \"引用核对\", or before submission to ensure bibliography integrity.
npx skills add majiayu000/claude-skill-registry --skill citation-audit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Verify every \cite{...} in a paper against three independent layers:
- Existence — the cited paper actually exists at the claimed arXiv ID / DOI / venue.
- Metadata correctness — author names, year, venue, and title match canonical sources (DBLP, arXiv, ACL Anthology, Nature, OpenReview, etc.).
- Context appropriateness — the cited paper actually supports the claim it is being used to support in the manuscript.
This skill is designed to run before submission to ensure bibliography integrity and is gated behind user prompts for specific citation-related languages.
How it works
- Step 1: Discover bib file(s) and all files containing
\cite{...}calls. Audit each bib entry that is cited. - Step 2: Extract (cite-key, file, line, surrounding_sentence) tuples and map citations to bib entries.
- Step 3: For each cited bib entry, invoke a fresh cross-model reviewer (
mcp__codex__codex) with modelgpt-5.5to verify:- Existence: YES / NO / UNCERTAIN with verifying URL.
- Metadata: whether author names, year, venue, and title are correct; report as correct / wrong / typo with expected values.
- Context: for each use, verdict as SUPPORTS / WEAK / WRONG with one-sentence reasoning.
- Overall VERDICT: KEEP / FIX / REPLACE / REMOVE.
- Step 4: Save reviewer responses to
.aris/traces/citation-audit/<date>_runNN/<key>.md. - Step 5: Aggregate into
CITATION_AUDIT.jsonusing the provided schema, with top-level verdict and per-entry details. - Step 6: Generate human-readable
CITATION_AUDIT.mddocumenting counts and per-entry actions; include sections for uncited entries if--uncitedis used. - Step 7: If FIX/REPLACE/REMOVE are chosen, prompt for user confirmation to apply changes to the bibliography or citations, with an option to AUTO_APPLY.
- Step 7 also includes a final recompile/check to ensure no new citation warnings appear.
When to use it
Run before submission after paper-write has produced the LaTeX draft and bib file, after paper-claim-audit has verified numerical claims, and before final paper-compile for submission. Do not run on a half-written draft.
What this skill touches
- Uses
mcp__codex__codexwith modelgpt-5.5and web lookups (DBLP/arXiv/venue databases). - Reads
references.bibor equivalent bib files and scans\cite{...}usages in TeX files. - Writes outputs to
CITATION_AUDIT.jsonandCITATION_AUDIT.mdand per-entry trace files under.aris/traces/citation-audit/.
Caveats
- Requires web access for lookups; results depend on live sources.
- Fresh reviewer thread per audit; cannot reuse prior context.
- If a bib file cannot be parsed, behavior is defined to fall back to blocked or partial reports without altering verdict structure.
# Citation Audit Verify every `\cite{...}` in a paper against three independent layers: 1. **Existence** — the cited paper actually exists at the claimed arXiv ID / DOI / venue. 2. **Metadata correctness** — author names, year, venue, and title match canonical sources (DBLP, arXiv, ACL Anthology, Nature, OpenReview, etc.). 3. **Context appropriateness** — the cited paper actually supports the claim it is being used to support in the manuscript. This skill is the fourth layer of \aris{}'s evidence-and-claim assurance, complementing `experiment-audit` (code), `result-to-claim` (science verdict), and `paper-claim-audit` (numerical claims). Together they form a bottom-up integrity stack from raw evaluation code to manuscript bibliography. ## When to Use This Skill **Run before submission.** The right gating point is: - After `paper-write` has produced the LaTeX draft and bib file - After `paper-claim-audit` has verified numerical claims - Before final `paper-compile` for submission **Do not** run this on a half-written draft — most of the work is in cross-checking each `\cite` against context, which is wasted on placeholder text. ## What This Skill Catches The dangerous citation proble
- When to Use This Skill
- What This Skill Catches
- Constants
- Workflow
- Step 1: Discover bib file and section files
- Step 2: Extract all (cite-key, context) pairs
- Step 3: Send each entry to fresh cross-model reviewer
- Step 4: Aggregate verdicts
- Step 5: Generate human-readable report
- Step 6: Apply fixes (interactive)
- Step 7: Recompile and verify
- Uncited Entry Detection (opt-in)
- Why opt-in
- Effect when enabled
latexmk -C && latexmk -pdf -interaction=nonstopmode main.tex
What does the citation-audit skill do?
Zero-context verification that every bibliographic entry in the paper is real, correctly attributed, and used in a context the cited paper actually supports. Uses a fresh cross-model reviewer with web/DBLP/arXiv lookup to catch hallucinated authors, wrong years, fabricated venues, version mismatches, and wrong-context citations (cite present but the cited paper does not establish the claim). Use when user says \"审查引用\", \"check citations\", \"citation audit\", \"verify references\", \"引用核对\", or before submission to ensure bibliography integrity.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill citation-audit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
