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Citation Audit

Community
Mathews-Tom
citation-audit

Verify that every citation in a manuscript is real, correctly attributed, and accurately described. Detects ghost papers, wrong arXiv IDs, inverted claims, and dead links by fetching each cited work. Optional fix mode applies bib metadata corrections and surfaces prose rewrites for claim errors. Triggers on: "check my citations", "verify references", "citation audit", "are my references real", "check bib", "reference check", "bib audit", "citation verification". Companion to manuscript-review (Pass 5 hygiene); this skill audits factual truth.

Overview

PublisherMathews-Tom
Repositoryarmory
Skill namecitation-audit
Stars
318
Forks
47
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Citation Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/citation-audit .claude/skills/citation-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Citation Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Citation Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Citation Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Citation Audit Skill

Purpose

Verify every citation in a manuscript against its actual source. LLMs hallucinate citations, invent arXiv IDs, misattribute findings, and confuse authors. This skill catches all of that by fetching and reading each cited work.

Why This Exists

LLMs are unreliable with citations in three distinct ways:

  1. Ghost papers — The paper does not exist. Title, authors, or venue are fabricated.
  2. Wrong metadata — The paper exists but the bib entry has the wrong arXiv ID, wrong authors, wrong year, or wrong venue.
  3. Inverted claims — The paper exists and the bib is correct, but the manuscript mischaracterizes what the paper says.

All three are invisible to structural audits (cross-reference checks, compilation tests). They require reading the actual cited work.

Inputs

  • The manuscript .tex file(s)
  • The .bib file
  • Web access (to fetch papers from arXiv, conference sites, URLs)

Execution

Phase 1: Extract citation contexts

For each \citep{}, \citet{}, \cite{} in the manuscript:

  1. Record the bib key
  2. Record the surrounding sentence or paragraph (the claim context)
  3. Classify the claim type:
    • FACTUAL: "X et al. found Y" / "X et al. measured Y"
    • METHODOLOGICAL: "We follow X" / "We use the benchmark from X"
    • POSITIONAL: "Unlike X, we..." / "X does not measure..."
    • PARENTHETICAL: "(X, 2024)" — no specific claim, just a reference
  4. For FACTUAL and POSITIONAL claims, extract the specific assertion the manuscript makes about the cited work

Phase 2: Verify bib entry metadata

For each bib entry, verify against the actual source:

For arXiv papers (eprint field present):

  1. Fetch https://arxiv.org/abs/{eprint_id}
  2. Compare: title, authors, year
  3. If the fetched paper has a DIFFERENT title/authors than the bib entry, this is a WRONG ID or GHOST PAPER

For conference/journal papers (booktitle or journal field):

  1. Search for the paper by title + author on the web
  2. Verify: venue, year, author list
  3. If the paper cannot be found at the stated venue, flag as UNVERIFIABLE or GHOST PAPER

For web resources (howpublished with URL):

  1. Fetch the URL
  2. Verify it loads and the content matches the described resource
  3. If the URL is dead or redirects to unrelated content, flag as DEAD LINK

For each entry, check:

  • Paper exists (reachable via arXiv, DOI, URL, or web search)
  • Title matches (exact or near-exact)
  • Authors match (at least first author correct)
  • Year matches
  • Venue matches (if applicable)
  • Entry type appropriate (@inproceedings for conferences, @article for journals, @misc for preprints/blogs)

Phase 3: Verify claim accuracy

For each FACTUAL or POSITIONAL claim:

  1. Read the cited paper (abstract + relevant sections at minimum)
  2. Compare the manuscript's claim against what the paper actually says
  3. Classify:
    • ACCURATE — The claim faithfully represents the cited work
    • INACCURATE — The claim mischaracterizes the cited work
    • INVERTED — The claim says the opposite of what the paper found
    • OVERCLAIMED — The claim is stronger than what the paper supports
    • UNDERCLAIMED — The cited work supports a stronger claim than stated
    • UNVERIFIABLE — Cannot access the paper to verify

For INACCURATE and INVERTED findings, provide:

  • What the manuscript claims
  • What the cited paper actually says
  • The specific section/page of the cited paper that contradicts the claim
  • A suggested correction

Phase 4: Check for missing citations

Scan the manuscript for:

  1. Claims that cite no source but should (empirical claims without attribution)
  2. Tools, benchmarks, or datasets mentioned by name without citation
  3. Methods described as "standard" or "well-known" that have a canonical citation

Output Format

Per-citation report

### [bib_key] — [VERDICT]

**Bib entry:** [title] by [authors] ([year])
**Actual paper:** [actual title] by [actual authors] ([actual year])
**Metadata match:** title [✓/✗] | authors [✓/✗] | year [✓/✗] | venue [✓/✗]

**Claim in manuscript (line N):** "[exact text]"
**What the paper actually says:** "[summary of actual finding]"
**Claim accuracy:** [ACCURATE / INACCURATE / INVERTED / OVERCLAIMED / UNDERCLAIMED]

**Fix required:** [description of what needs to change, or "None"]

Summary table

| Bib Key | Exists | Metadata | Claim | Verdict |
|---------|--------|----------|-------|---------|
| key1    | ✓      | ✓        | ✓     | PASS    |
| key2    | ✓      | ✗        | ✗     | FAIL    |
| key3    | ✗      | —        | —     | GHOST   |

Verdict categories

  • PASS — Paper exists, metadata correct, claims accurate
  • METADATA — Paper exists, bib entry has errors (wrong ID, wrong authors, wrong year)
  • CLAIM — Paper exists, metadata correct, but manuscript mischaracterizes it
  • GHOST — Paper does not exist as described
  • DEAD — URL/link is broken
  • UNVERIFIABLE — Cannot access the paper to verify

Severity

  • CRITICAL: GHOST papers, INVERTED claims
  • HIGH: Wrong arXiv IDs, wrong authors, INACCURATE claims
  • MEDIUM: Wrong year, wrong venue, OVERCLAIMED
  • LOW: Missing citations, incomplete bib entries, UNDERCLAIMED

Important notes

  • NEVER trust your own knowledge of papers. ALWAYS fetch and verify. Your training data contains hallucinated citations. The only way to verify is to read the actual source.
  • For arXiv papers, always fetch the abstract page to confirm the paper exists and matches.
  • For conference papers, search DBLP, ACM DL, or the conference site.
  • WebFetch and WebSearch are your primary tools. Do not skip verification because a citation "looks right."
  • Blog posts and documentation URLs change. Always check that the URL still works and points to the described content.
  • When a bib entry has both an eprint (arXiv ID) and a booktitle (venue), verify both independently.

Phase 5: Fix (when invoked with "fix" or "on")

When the user invokes with an argument containing "fix" or "on", execute Phases 1–4 as above, then apply fixes for every non-PASS citation.

What to auto-fix (no user confirmation needed)

These are mechanical corrections with a single correct answer:

METADATA errors (paper exists, bib entry wrong):

  • Wrong arXiv ID → replace eprint with the correct ID
  • Wrong authors → replace with authors from the actual paper
  • Wrong year → replace with year from the actual paper
  • Wrong title → replace with title from the actual paper
  • Wrong venue → replace with venue from the actual paper
  • Wrong entry type → change @misc/@inproceedings as appropriate

DEAD links:

  • URL redirects → update howpublished URL to the final destination
  • URL 404 but resource found at different URL → update URL
  • URL 404 and resource gone → flag as HUMAN-REQUIRED

Minor author corrections:

  • Misspelled author names → fix spelling
  • Missing authors from author list → add them
  • Collective author name where individual names are available → replace (keep collective name as a note if it is how the group identifies)

What requires HUMAN-REQUIRED decision

Present these and wait for the user:

GHOST papers:

  • Paper does not exist at all → present options: (a) Replace with a real paper that makes the same point (b) Remove the citation and adjust the prose (c) The user knows the paper exists and provides the correct reference

INVERTED or INACCURATE claims:

  • The manuscript says X about a paper that actually says Y → present:
    • What the manuscript claims
    • What the paper actually says
    • A suggested rewrite of the prose that accurately represents the paper
    • Whether the paper still supports the manuscript's argument (and how)
    • Let the user decide the final wording

Dead URLs with no replacement found:

  • Blog post / resource deleted with no archive or alternative

Fix procedure

  1. Apply all auto-fixes to the .bib file
  2. For each HUMAN-REQUIRED item, present the options clearly
  3. After user decisions, apply prose changes to the .tex file
  4. Verify: re-read the .bib and .tex to confirm all fixes applied
  5. Update the audit report: mark each finding as [FIXED], [RESOLVED], or [DEFERRED]

Safety rules

  • NEVER invent a replacement citation. If a ghost paper needs replacing, search for real papers that make the cited point. Present candidates to the user with abstracts. Let the user choose.
  • NEVER change the manuscript's argument. If an inverted claim needs fixing, present the rewrite as a suggestion, not an edit.
  • NEVER remove a citation without user confirmation, even if it is a ghost paper. The user may know something you do not.
  • When fixing URLs, always verify the new URL loads and contains the expected content before writing it.

Save report as

[name]-citation-audit.md in the manuscript directory.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Citation Audit AI skill do?

Verify that every citation in a manuscript is real, correctly attributed, and accurately described. Detects ghost papers, wrong arXiv IDs, inverted claims, and dead links by fetching each cited work. Optional fix mode applies bib metadata corrections and surfaces prose rewrites for claim errors. Triggers on: "check my citations", "verify references", "citation audit", "are my references real", "check bib", "reference check", "bib audit", "citation verification". Companion to manuscript-review (Pass 5 hygiene); this skill audits factual truth.

Why use Citation Audit on TypingMind?

Because you install it once and use it with any model. Citation Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Citation Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/citation-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Citation Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Citation Audit?

As many as you like. As long as a model supports skills, you can use Citation Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Citation Audit AI skill free?

Yes. It is published on GitHub by Mathews-Tom under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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