research-paper-integrity
Skill2.1k starsChanged 7 months agoMITView on GitHub
Standards and audits for writing academic papers, especially ML/NLP submissions to NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL. Covers the author's house prose rules (zero em dashes, zero fluff, every sentence earns its place), verification of hallucinated claims and hallucinated or malformed bibliography entries, the ACL policy on AI writing assistance and what must be disclosed, and best practices for paper structure, experiments, reproducibility checklists, and rebuttals. Use this skill whenever the user is drafting, editing, polishing, restructuring, or reviewing a paper, abstract, intro, related work, rebuttal, or camera-ready; whenever they ask to "clean up" or "tighten" academic writing; whenever citations or a .bib file need checking; and whenever they ask what has to be disclosed about AI assistance. Reach for it even when the user only says "make this section better", "check my references", or "is this ready to submit".
3 other projects use a version of this.
Trending
Quiet since April.thin dataabout the whole repository
Most recently taken up by
- Leanmcp/superview.shword for word, first seen Oct 13, 2026
- chatgptprojects/claude-codeword for word, first seen Oct 8, 2026
- Leanmcp/gateway-skillsword for word, first seen Oct 8, 2026
Who's talking about chatgptprojects/clear-code
- karthink/gptel-agent“The Claude Code source code has leaked, probably bringing a massive boost to open source projects such as …”GitHub · Mar 31, 2026 · 1 reactions
- ProSkillsMD/proskills“[SUBMISSION] clear-code”GitHub · Apr 20, 2026
The biggest 2 of 2 mentions linking to the repository that our crawl found. Each line is their own headline.
Other versions3 projects use a version of this.
Word for word · 3
- Leanmcp/superview.sh★ 2.1k100% same
- chatgptprojects/claude-code★ 2.1k100% same
- Leanmcp/gateway-skills★ 2.1k100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository16 other files it gives its agents.
- claude-code-devSkill
- analyze-exportsSkill
- code-explanationSkill
- fireworks-inferenceSkill
- godot-city-game-3dSkill
- ios-bootstrap-testflightSkill
- leanmcp-builderSkill
- mcp-apps-builderSkill
- mcp-builderSkill
- posthog-adblock-bypassSkill
- publicity-distributionSkill
- research-observabilitySkill
- security-reproSkill
- tinker-training-inferenceSkill
- translate-explanationSkill
- vuln-reproSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 32
1. The text contains no em dashes and no filler: Not because em dashes are wrong in general, but because in this user's papers they signal …
- Distinctive: line 55
Read the draft fully before changing anything.
- Distinctive: line 57
Run the hallucination audit first.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Research paper integrity
This skill is the index. Each area below lives in its own reference file so you only load what the current task needs. Read the file before acting on that area, not after, because these rules change what you write rather than how you check it afterwards.
What lives where
| Area | File | Read it when |
| Prose rules: no em dashes, no fluff, necessity test | references/writing-style.md | Writing or editing any prose the user will submit |
| Hallucination audit: unsupported claims, fake or wrong citations, bib hygiene | references/hallucination-audit.md | Any draft has claims or references that were not hand-verified |
| AI assistance disclosure (ACL 2023 policy and its logic) | references/ai-writing-policy.md | The user asks what to disclose, fills a responsible-NLP checklist, or used a model for text, ideas, or code |
| Conference craft: structure, experiments, figures, rebuttals, checklists | references/conference-writing.md | Planning a paper, restructuring sections, prepping a submission or rebuttal |
scripts/check_prose.py is a fast mechanical pass for em dashes, banned filler,
hedge stacking, and suspicious bib entries. It catches the boring violations so your
attention goes to the ones that need judgment. Per the user's global rule, do not run
it yourself: write it into the relevant workspace if needed and hand over the exact
command.
The three commitments
Everything in this skill reduces to three things the user cares about. Keep them in mind even when you have only loaded this index.
1. The text contains no em dashes and no filler. Not because em dashes are wrong
in general, but because in this user's papers they signal a sentence that was never
restructured, and filler signals a claim that was never sharpened. Replace an em dash
with a period, a colon, a comma, or parentheses, whichever the actual logic calls for.
Details and the filler list are in references/writing-style.md.
2. Every sentence has been asked twice whether it needs to exist. First pass: does this sentence carry information the reader does not already have? Second pass: if it does, is this the shortest honest form of it? A paragraph that survives both passes is usually a third shorter than the draft. Cutting is the main editing operation here, not rewording.
3. Nothing is asserted that has not been checked, and no reference is cited that has
not been confirmed to exist. Fabricated citations and confidently wrong numbers are
the two failure modes that end a paper's credibility, and both look completely normal
in a draft. Treat every number, every "prior work shows", and every bib entry as
unverified until you have traced it. Procedure in references/hallucination-audit.md.
Default workflow when handed a draft
Adapt this, do not perform it mechanically. If the user asked only for a citation check, do only that.
- Read the draft fully before changing anything. Local edits made without the whole argument in mind are how papers lose their thread.
- Run the hallucination audit first. There is no point polishing a sentence built on a claim that turns out to be unsupported, and finding a broken citation early changes what the surrounding paragraph should say.
- Then the prose pass: cut, de-fluff, remove em dashes, tighten.
- Then structure: does each section do the job
references/conference-writing.mddescribes, in the order a reviewer reads. - Report what you changed and, separately, what you could not verify. The second list matters more than the first. Never quietly leave an unverifiable claim in place without flagging it.
How to report
Give the user the edited text plus a short audit section. Something like:
## Changes
- Cut 4 sentences from the intro that restated the abstract
- Removed 11 em dashes, restructured the 3 sentences that needed it
- Section 4.2: "significantly improves" -> stated the actual delta and the test
## Could not verify (needs your call)
- [Smith 2021] claimed for the pretraining result: no paper by that name found in
ACL Anthology or arXiv. Either the citation is wrong or the claim needs a source.
- Table 2 baseline number differs from the number quoted in Section 5.Flagging honestly is the whole value here. A silent pass that leaves a fabricated citation in place is worse than no pass at all, because it launders the error.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

