paper-machine
AGENTS.md0 starsChanged 3 months agoMITView on GitHub
Condensed from paper-machine. Drop this file in a repo where an agent will write or edit a paper. The full skills carry the reasoning and the incidents behind each rule. Never write a BibTeX entry from memory. Every reference comes from Semantic Scholar, DBLP, CrossRef or arXiv, or it does not go in the file. Agents produce entries that are almost right: real authors, plausible venue, no such paper. LaTeX cannot detect this. Only a database lookup can. Never write…
- Installs packages
Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 31,700 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository14 other files it gives its agents.
- agent-cost-controlSkill
- citation-disciplineSkill
- delivery-contractSkill
- env-isolationSkill
- experiment-disciplineSkill
- figure-integritySkill
- latex-compileSkill
- literature-surveySkill
- numbers-traceableSkill
- page-fittingSkill
- paper-machineSkill
- paper-structureSkill
- research-integritySkill
- reviewer-mindsetSkill
The file
We split this AGENTS.md into the rules it gives an agent
10 rules. About 0 are common, 7 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈7
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 9
Never write a BibTeX entry from memory: Every reference comes from Semantic Scholar, DBLP, CrossRef or arXiv, or it does not go in the file.
- Distinctive: line 17
Never write a comparison you have not computed.
- Distinctive: line 19
Never simulate a result: If an experiment cannot run, mark it blocked and say so in the paper.
- Very specific: line 23
Never install into a shared environment: Use a project-local venv or conda prefix.
- Distinctive: line 30
If the literature tool fails, retry, then fall back to direct academic search.
- Very specific: line 39
Leave an evidence trail beside numbers: % [evidence] results/exp1.json -> metrics.f1 = 0.8234
- Distinctive: line 48
Never display a bitmap wider than its native resolution supports.
- Very specific: line 49
Every figure must be referenced by \ref in the text.
- Distinctive: line 53
Run the full chain, every time:
- Distinctive: line 73
If any fails, report the failure and what would fix it.
≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md 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
Writing research papers: rules for agents
Condensed from paper-machine. Drop this file in a repo where an agent will write or edit a paper. The full skills carry the reasoning and the incidents behind each rule.
Non-negotiable
Never write a BibTeX entry from memory. Every reference comes from Semantic Scholar, DBLP, CrossRef or arXiv, or it does not go in the file. Agents produce entries that are almost right: real authors, plausible venue, no such paper. LaTeX cannot detect this. Only a database lookup can.
Never write a number you have not just read from a file. Open the file
under results/ and copy the value, even if you produced it yourself
earlier in this session. Your memory of a number is a generated guess.
Never write a comparison you have not computed.
Never simulate a result. If an experiment cannot run, mark it blocked and say so in the paper. "We could not evaluate X because Y" is a normal sentence in a normal paper. Invented numbers are misconduct.
Never install into a shared environment. Use a project-local venv or
conda prefix. One pip install into a shared env once downgraded a core
library and silently broke every user of that machine for five days.
Before writing
- Ground the paper in real prior work you retrieved and read.
- If the literature tool fails, retry, then fall back to direct academic search. If the survey is degraded, say so in the paper. A thin foundation is acceptable; an invisible one is not.
While writing
- Claims, not activities. "We conduct extensive experiments" says nothing.
- Match claim strength to evidence. One dataset is not "across domains".
- Report the experiment that did not work. Its absence is conspicuous.
- Leave an evidence trail beside numbers:
% [evidence] results/exp1.json -> metrics.f1 = 0.8234
Figures
- Plot only values that exist in
results/. - The file's bytes must match its extension. A JPEG saved as
.pngcompiles fine and then fails hard when a vision model reads it. Re-encode rather than rename, because the filename is already referenced. - Never display a bitmap wider than its native resolution supports.
- Every figure must be referenced by
\refin the text.
Compiling
Run the full chain, every time:
pdflatex main.tex && bibtex main && pdflatex main.tex && pdflatex main.texA bare pdflatex re-run does not refresh the bibliography, and page-fitting
passes often trigger one. Verify the delivered PDF, not an earlier one.
Before saying it is done
The paper is finished only when all of these hold:
main.pdfexists, opens, and is over 50 KB- Zero
?markers in the rendered text - Every reference resolves in an academic database (state the count)
- Every number traces to a file (state the count)
- Every figure is referenced in the prose
- The AI-assistance disclosure appears in the rendered PDF
If any fails, report the failure and what would fix it. Do not report success. A truthful failure is worth more than a confident lie.
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.

