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neurips-review-process

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Use when explaining or diagnosing the NeurIPS main-track review process, including OpenReview, reviewer and AC roles, contribution-type review, ethics flags, reciprocal reviewing, discussion, and LLM-review policy.

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/NeurIPS-Skills/skills/neurips-review-process/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/neurips-review-process/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

NeurIPS Review Process

Use this skill to reason about what reviewers and ACs are likely to do with a submission. Do not use it to infer acceptance odds from folklore; use it to identify decision-relevant weaknesses.

Process model

  • Reviews happen in OpenReview under double blind.
  • Reviewers are asked to evaluate quality, clarity, significance, and the submission's declared contribution type.
  • ACs coordinate reviewers, handle conflicts and quality issues, write or supervise meta-reviews, and make recommendations.
  • Ethics concerns can be flagged and routed to ethics reviewers; severe cases can affect decisions.
  • Authors who are also reviewers or ACs face reciprocal-reviewing obligations; gross negligence can create sanctions affecting their own submissions.

Reviewer mental model

Reviewers are overloaded cross-area specialists. They need to see:

  • what the contribution is;
  • why it is new relative to close NeurIPS/ICML/ICLR/ACL/CVPR/KDD-style work;
  • whether the evidence supports the exact claim;
  • whether limitations, safety, data, and reproducibility are handled responsibly;
  • whether the paper can be trusted as a scientific artifact.

Diagnosis workflow

  1. Map each likely review concern to quality, clarity, significance, ethics, reproducibility, or fit.
  2. Predict which concerns the AC can use in a meta-review.
  3. Decide whether the problem is fixable by clarification, extra analysis, better framing, or re-routing.
  4. For author response, prioritize issues that change decision logic rather than issues that only improve tone.

Output format

[Likely review split] enthusiastic / borderline / skeptical
[AC-level issue] <one issue most likely to drive the meta-review>
[Ethics/reproducibility flags] <none or list>
[Response strategy] clarify / concede / add small result / reroute