Individual skill
icml-reproducibility
Use when strengthening ICML reproducibility evidence, including code/data availability, random seeds, compute disclosure, appendix evidence, impact-statement support, and reviewer-facing reproducibility claims.
ICML-Skills/skills/icml-reproducibility/SKILL.md
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# ICML Reproducibility
Use this when the paper's acceptance risk is tied to whether experiments, code, or theory can be
trusted. ICML reviewers are asked to evaluate soundness, and ICML author instructions state that
reproducibility and code availability are considered in decisions.
## Evidence checklist
- Data: source, license, preprocessing, splits, leakage checks, and access restrictions.
- Code: anonymous review package, environment, dependencies, exact commands, and expected runtime.
- Randomness: seeds, variance, confidence intervals, or explanation for deterministic runs.
- Compute: hardware, training budget, evaluation budget, and fairness relative to baselines.
- Baselines: tuning protocol, implementation source, and why missing baselines are not applicable.
- Theory: assumptions, theorem statements, proof dependencies, and edge cases.