agentleFS
JihaoXinJihaoXin/paper-machine/AGENTS.md

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

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
  1. Distinctive:

    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.

    line 9
  2. Distinctive:

    Never write a comparison you have not computed.

    line 17
  3. Distinctive:

    Never simulate a result: If an experiment cannot run, mark it blocked and say so in the paper.

    line 19
  4. Very specific:

    Never install into a shared environment: Use a project-local venv or conda prefix.

    line 23
  5. Distinctive:

    If the literature tool fails, retry, then fall back to direct academic search.

    line 30
  6. Very specific:

    Leave an evidence trail beside numbers: % [evidence] results/exp1.json -> metrics.f1 = 0.8234

    line 39
  7. Distinctive:

    Never display a bitmap wider than its native resolution supports.

    line 48
  8. Very specific:

    Every figure must be referenced by \ref in the text.

    line 49
  9. Distinctive:

    Run the full chain, every time:

    line 53
  10. Distinctive:

    If any fails, report the failure and what would fix it.

    line 73

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
AGENTS.md

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 .png compiles 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 \ref in the text.

Compiling

Run the full chain, every time:

pdflatex main.tex && bibtex main && pdflatex main.tex && pdflatex main.tex

A 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.pdf exists, 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.

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  • It worked
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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.