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Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP.
Passed 21 of 23 test cases with the skill, and 17 of 23 without it.
and 1 more in the full report
4 fixed17 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
To save compute budget during benchmark evaluation, we ran our algorithm once on the test set and reported that single score in our main results table. Will reviewers consider a single evaluation run sufficient?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends reporting mean and standard deviation over multiple runs. | Missed | Passed |
| Recommends running experiments over 3 to 5 runs. | Passed | Passed |
The instructions your model reads when the skill loads.
Publishing at top machine learning venues—NeurIPS, ICML, ICLR, AAAI, and similar conferences—requires not only strong technical contributions but also clear, persuasive writing that follows community conventions. The reviewing process at these venues is highly competitive (acceptance rates of 15-30%), and the difference between a borderline accept and a borderline reject often comes down to how well the paper communicates its contributions.
This skill provides a comprehensive guide to writing ML/AI research papers that meet the expectations of reviewers at top venues. It covers paper structure, the specific writing conventions of the ML community, common reviewer complaints to avoid, and practical templates for each section.
The guidance here is based on published reviewer guidelines from NeurIPS, ICML, and ICLR, as well as widely-cited advice from established researchers in the field.
Your title should be specific and informative. Avoid generic titles like "A Novel Approach to X." Include:
Good examples:
Avoid:
Structure your abstract as four implicit paragraphs, even if written as a single block:
The introduction expands the abstract with more context and should accomplish:
test cases passed, vs 17 / 23 without
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