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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-artifact-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 wh
- Artifact decision
- Anonymous review package
- Public release package
- Output format
What does the neurips-artifact-evaluation skill do?
Use when packaging NeurIPS code, data, models, demos, benchmarks, or other research artifacts for anonymous review, reproducibility, public release, or MLRC-style artifact scrutiny.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-artifact-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.