neurips-reproducibility
Use when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions, setting random-seed and compute disclosure, or deciding whether the MLRC/TMLR reproducibility route fits better than the main track or Datasets & Benchmarks track.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-reproducibility --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 Reproducibility Use this skill when a NeurIPS paper's claim depends on experiments, data, code, or a reproducibility argument. The immediate target is a trustworthy main-track paper; the alternative route is MLRC/TMLR when the central contribution is reproduction, replication, or generalizability of prior claims. ## Main-track reproducibility bar - State exact data splits, preprocessing, hyperparameters, selection criteria, compute resources, software versions, and random-seed protocol. - Report uncertainty where it matters: confidence intervals, standard errors, multiple seeds, sensitivity checks, or negative findings. - Distinguish exploratory experiments from evidence that supports the main claim. - Make code/data availability match the checklist answer; "no" is allowed with justification, but a central open-source benchmark or dataset usually needs accessible artifacts. - For human, private, medical, proprietary, or safety-sensitive data, document access constraints and ethical controls rather than pretending full release is possible. ## MLRC route check Consider the NeurIPS Reproducibility / MLRC track when the paper is primarily about confirming, partially reproduci
- Main-track reproducibility bar
- MLRC route check
- Checklist-to-evidence cross-check
- Reviewer-pushback patterns
- Worked vignette: a scaling-law claim
- Output format
What does the neurips-reproducibility skill do?
Use when strengthening NeurIPS reproducibility evidence, aligning Paper Checklist answers with the paper, writing code/data instructions, setting random-seed and compute disclosure, or deciding whether the MLRC/TMLR reproducibility route fits better than the main track or Datasets & Benchmarks track.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill neurips-reproducibility --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.