From icml-skills
Packages ICML artifacts (code, data, weights, notebooks) for double-blind review and public release. Checks anonymity, licensing, decision relevance, and OpenReview code URL compliance.
How this skill is triggered — by the user, by Claude, or both
Slash command
/icml-skills:icml-artifact-evaluationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
ICML does not let artifacts sit outside the paper's scientific argument. Reproducibility and code
ICML does not let artifacts sit outside the paper's scientific argument. Reproducibility and code availability are explicitly considered in decision-making, and accepted submissions may publish the original supplementary material on OpenReview.
ICML double-blind review means a single deanonymizing artifact can trigger a desk reject, so audit the package the way an adversarial reviewer would.
| Leak vector | Where it hides | Mitigation |
|---|---|---|
| Repo ownership | Anonymous-repo account name, commit author | Use a fresh anonymized host, strip git history |
| File metadata | PDF author field, notebook kernel, model card | Clear metadata, rename author paths |
| Hard-coded paths | Cluster usernames in scripts and logs | Replace with placeholders before zipping |
| External links | Personal site, non-anonymous URL, shortener | Remove or route through an anonymous mirror |
A paper shipping an adaptive optimizer includes training scripts, a pretrained checkpoint, and a proof-checking notebook. The review package gives minimal commands, expected runtime, and the hardware assumption so a reviewer can map a command to a benchmark number, while the checkpoint filename and the notebook kernel are scrubbed of the lab name. Because accepted ICML supplements can become public, the team confirms the checkpoint is releasable and the license is stated before the deadline, then plans the public repository and OpenReview code URL for camera-ready.
[Artifact role] code / data / model / benchmark / proof / none
[Review package] sufficient / incomplete / unsafe
[Anonymity risks] <metadata, repo, filenames, links>
[Decision relevance] <why reviewers need it>
[Public release plan] <repo/archive/license/code URL>
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin icml-skillsPackages ICLR paper artifacts (code, data, checkpoints, demos) for anonymous review and post-acceptance release, with guidance on minimal reproduction paths, anonymization, and reviewer-friendly structure.
Packages NeurIPS research artifacts (code, data, models, demos, benchmarks) for anonymous review, reproducibility, or public release. Guides artifact decisions, anonymization, and output formatting.
Strengthens ICML reproducibility evidence: code/data availability, random seeds, compute disclosure, appendix evidence, and reviewer-facing claims.