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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-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.
# 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. - Impact: support claims in the impact statement with real evidence or narrow the statement. ## ICML-specific public-record issue Accepted papers may publish original supplementary material. Do not put unreleasable data, identity leaks, or private credentials in the review package. If data cannot be public, document the access path and ethic
- Evidence checklist
- ICML-specific public-record issue
- Reproducibility scoring lens
- Worked vignette: theory-plus-benchmark paper
- Reviewer-pushback patterns and the ICML fix
- Output format
What does the icml-reproducibility skill do?
Use when strengthening ICML reproducibility evidence, including code/data availability, random seeds, compute disclosure, appendix evidence, impact-statement support, and reviewer-facing reproducibility claims.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icml-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.