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Neurips artifact evaluation

Skill brycewang-stanford/Awesome-Journal-Skills/NeurIPS-Skills/skills/neurips-artifact-evaluation

Use when packaging NeurIPS code, data, models, demos, benchmarks, or other research artifacts for anonymous review, reproducibility, public release, or MLRC-style artifact scrutiny.From its SKILL.md

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-artifact-evaluation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

2.3 KB, 426 tokens by cl100k_base, as published. Nobody here has run it

NeurIPS Artifact Evaluation

NeurIPS main track does not reduce artifact quality to a generic badge workflow. It expects code, data, and execution details when they are needed to support the scientific claim, and its checklist and code/data guidance make artifact quality visible to reviewers.

Artifact decision

  • If the contribution is a method, include training and evaluation code or justify why it cannot be shared.
  • If the contribution is a dataset or benchmark, provide metadata, license, preservation plan, representative-use discussion, and access restrictions.
  • If the contribution depends on a model, include weights, prompts, decoding settings, compute resources, or a precise explanation of unavailable components.
  • If the contribution is theoretical, artifact focus may shift to proof checks, symbolic scripts, experiment notebooks, or counterexample generation.

Anonymous review package

  • Keep the ZIP within the current official size limit and anonymize filenames, repository URLs, usernames, commit history, model cards, dataset cards, comments, notebooks, and logs.
  • Include a short README with exact commands, environment, expected runtime, hardware assumptions, and which experiments are reproducible from the package.
  • Do not require reviewers to run unsafe code outside a secure environment.
  • Avoid external links unless the current policy allows them and anonymous browsing is guaranteed.

Public release package

  • De-anonymize accepted artifacts.
  • Add licenses for code, data, model weights, and generated outputs.
  • Archive code in a durable service when appropriate; NeurIPS MLRC guidance recommends Software Heritage for reproducibility papers.
  • Keep a mapping from paper claims to commands or notebooks so users can reproduce headline results.

Output format

[Artifact role] method / dataset / benchmark / model / demo / proof / none
[Review package] sufficient / insufficient
[Anonymity risks] <paths, metadata, URLs, usernames>
[Reproducibility gaps] <commands, environment, data, hardware, licenses>
[Public-release plan] <archive, DOI, license, docs>

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.