Individual skill
iclr-reproducibility
Use when strengthening reproducibility for ICLR papers, including seeds, variance, compute, datasets, implementation details, ethics statements, and reviewer-verifiable evidence. Use when writing the ICLR reproducibility statement, when a reviewer says a result is not verifiable, or when mapping each representation-learning claim to a seed, split, and command so anyone reading the permanent OpenReview record can check it.
ICLR-Skills/skills/iclr-reproducibility/SKILL.md
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# ICLR Reproducibility
Use this when the paper's main claims depend on experiments, simulations, data processing, human
subjects, or benchmark comparisons. ICLR reviewers are asked to evaluate rigor and reproducibility,
not just headline scores.
## Reproducibility audit
- Map each central claim to a table, figure, proof, appendix item, or artifact command.
- Record seeds, variance, confidence intervals, test splits, preprocessing, early stopping,
hyperparameter search, model selection, and compute budget.
- Distinguish training compute from inference compute and report hardware details that affect
comparability.
- Add negative results and failure cases when they explain boundary conditions.