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 wanshuiyin/Anti-Autoresearch --skill citation-forensicsgit clone --depth 1 https://github.com/wanshuiyin/Anti-AutoresearchWrote 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/wanshuiyin/anti-autoresearch/citation-forensics)<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/citation-forensics"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/citation-forensics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/citation-forensics"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/citation-forensics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Anti-Refusal · line 360 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00193 | $0.12884 |
| Opus 5.5 | $0.00077 | $0.05154 |
| Sonnet 5.5 | $0.00039 | $0.02577 |
| Haiku 4.5 | $0.00019 | $0.01288 |
Grade A, and why
citation-forensics 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- citation-forensics — 94% identical, 346 lines differ
How it starts
The opening of the file, as written. The whole thing — 801 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citation Forensics — are the references real and honestly used?
Audit citation integrity for: $ARGUMENTS (requires claims.json from
/evidence-ledger; reasons over its type:"citation" claims). Emit span-anchored
citation-forensics.findings.json. This skill computes no verdict.
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is verdict-bearing input — it proposes the citation findings the deterministic adjudicator turns into the report. Re-firing it on a wall-clock timer adds no signal: its output changes only when the paper / ledger / bibliography changes, not with the clock, and each run spends real cross-model + lookup budget per cited key. Schedule the external wait that precedes it — bibliography finalized → ledger rebuilt → audit once. (Mirrors ARIS's external-cadence doctrine.)
Adapted from ARIS
citation-audit, re-wired onto this repo's evidence ledger and the reviewer≠adjudicator contract, and reframed from "audit + rewrite the bib" to "emit ledger-anchored findings, never touch the paper." Three layers, ported verbatim: existence → metadata → context. Following the repo'sbaseline-comparison-auditpattern, the executor gathers the canonical facts (DBLP / arXiv / DOI) as neutral evidence; a fresh cross-model reviewer judges existence + metadata + context over those facts. The reviewer never grades — the deterministic adjudicator does.
Why this exists
An autoresearch pipeline (or a rushed human) generates a bibliography in a separate pass from the prose and never reconciles the two. The failure modes are not wildly fake entries — those are easy to spot. The dangerous ones are:
- Hallucinated reference — no paper exists at the claimed arXiv id / DOI / venue;
authors, title, or year are fabricated. (
HP-CITE-HALLUC, critical) - Metadata drift — a real paper cited with the wrong year, wrong venue (the arXiv
preprint number used after the work appeared at CVPR/ICML/NeurIPS, or vice versa),
or a v1 title silently merged with a v3 retitle. (
HP-CITE-HALLUC, major) - Wrong-context citation — a real paper used to support a claim it does not
make, or argues against (e.g. citing a self-refinement paper to support
"self-feedback yields correlated errors" when the cited paper argues the
opposite). (
HP-CITE-CONTEXT, major)
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.
- 1mo ago First seen · 801 lines · 193 tokens per session scan A 8b14ef660cda
citation-forensics is a skill published in the GitHub repository wanshuiyin/Anti-Autoresearch (172 stars, last pushed 11d ago), licensed MIT. It adds 193 tokens to every session and 12,884 once invoked, about $0.0008 per session on Opus 5.5. 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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