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arxiv-preflight

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Run a submission-readiness preflight on a manuscript before arXiv upload. Use when the user is preparing an arXiv submission, asks to check a paper before uploading, mentions hallucinated or fake references, leftover LLM meta-comments / prompts in text, placeholder data (TODO, TBD, XX%), AI-use disclosure, scholarly integrity, research integrity, or arXiv moderation risk — even if they don't say "preflight". Also trigger on phrases like "check my paper before arXiv", "verify my references", "scan for AI artifacts", "scan for LLM residue", "is my submission ready", or "review .tex/.bib before submit".

awesome-med-research-skills/Academic Writing/arxiv-preflight/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

# arXiv Preflight

Submission-readiness review for a manuscript before arXiv upload. The goal is **not** to judge whether a paper was AI-written from style. The goal is to find concrete, locatable, reviewable evidence of unchecked LLM output, hallucinated references, placeholder data, unsupported claims, and arXiv-policy risks — and to hand the author a fix list.

Anchor on the actual arXiv stance: authors may use generative AI tools but are fully responsible for the content and must disclose significant use per field norms; AI tools cannot be listed as authors. See `references/arxiv_policy_notes.md` for the exact language.

This skill is intentionally platform-neutral. It should work for any AI coding/review agent that can read files and run local Python scripts; it does not require OpenAI-specific UI metadata.

Requires Python 3.9+.

## Workflow

1. **Identify inputs.** Ask for or detect: LaTeX project directory, single PDF, BibTeX file, supplementary figures/tables, or experiment data. Prefer LaTeX source over rendered PDF — PDF text extraction loses structure and produces noisier reference checks.
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