Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Mathews-Tom/armory --skill citation-auditgit clone --depth 1 https://github.com/Mathews-Tom/armoryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mathews-tom/armory/citation-audit)<a href="https://agentmods.dev/skills/mathews-tom/armory/citation-audit"><img src="https://agentmods.dev/badge/skills/mathews-tom/armory/citation-audit.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 169 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.02460 |
| Opus 5 | $0.00057 | $0.01230 |
| Sonnet 5 | $0.00023 | $0.00492 |
| Haiku 4.5 | $0.00012 | $0.00246 |
Grade A, and why
citation-audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Ghost papers — The paper does not exist. Title, authors, or venue are fabricated.
- Wrong metadata — The paper exists but the bib entry has the wrong arXiv ID, wrong authors, wrong year, or wrong venue.
- 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
.texfile(s) - The
.bibfile - Web access (to fetch papers from arXiv, conference sites, URLs)
Execution
Phase 1: Extract citation contexts
For each \citep{}, \citet{}, \cite{} in the manuscript:
- Record the bib key
- Record the surrounding sentence or paragraph (the claim context)
- 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
- 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):
- Fetch
https://arxiv.org/abs/{eprint_id} - Compare: title, authors, year
- 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):
- Search for the paper by title + author on the web
- Verify: venue, year, author list
- If the paper cannot be found at the stated venue, flag as UNVERIFIABLE or GHOST PAPER
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 249 lines · 115 tokens per session scan A 8e29d32cfb89
citation-audit is a skill published in the GitHub repository Mathews-Tom/armory (316 stars, last pushed 2d ago), licensed MIT. It adds 115 tokens to every session and 2,460 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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