Individual skill
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.
NeurIPS-Skills/skills/neurips-reproducibility/SKILL.md
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- Last checked
- Nov 9, 2026
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- Repository last updated
- Sep 27, 2026
- License
- MIT
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# 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