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 raja21068/AutoResearch --skill citation-auditgit clone --depth 1 https://github.com/raja21068/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/raja21068/autoresearch/citation-audit)<a href="https://agentmods.dev/skills/raja21068/autoresearch/citation-audit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/citation-audit/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/raja21068/autoresearch/citation-audit"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/citation-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00114 | $0.07281 |
| Opus 5 | $0.00057 | $0.03640 |
| Sonnet 5 | $0.00023 | $0.01456 |
| Haiku 4.5 | $0.00011 | $0.00728 |
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 8d 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.
This is a copy
97% identical to citation-audit — 31 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 494 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Citation Audit
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 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-writehas produced the LaTeX draft and bib file - After
paper-claim-audithas verified numerical claims - Before final
paper-compilefor 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 problems are not wildly fake citations — those are easy to spot. The dangerous ones are:
- Wrong-context citations: real paper, but the cited claim is not what that paper actually establishes (e.g., citing Self-Refine to support "self-feedback produces correlated errors" — Self-Refine actually argues the opposite).
- Author hallucinations: anonymous-author placeholders that slipped through, missing co-authors, wrong order.
- Title drift: arXiv v1 vs v3 with different titles silently merged.
- Venue confusion: arXiv preprint cited but the official venue is now CVPR/ICML/NeurIPS — using the wrong record.
- Year mismatch: arXiv 2023 preprint with 2024 conference acceptance, year reported inconsistently.
- Phantom DOIs: DOI looks real but does not resolve.
- Self-citation drift: your own prior work cited with year off by one.
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.
- 8d ago First seen · 494 lines · 114 tokens per session scan A 4c17550656af
citation-audit is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 114 tokens to every session and 7,281 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to citation-audit, differing in 31 lines, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…