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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-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.
# 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. - Check whether ethics or reproducibility statements are relevant under the current Author Guide. - Make the appendix useful but not required for basic verification; reviewers may not inspect every appendix page. ## Common ICLR weak points - Single-seed wins on unstable benchmarks. - Missing comparison to strong open-source baselines or recent OpenReview/arXiv work. - Ambiguous data leakage, test-set tuning, or prompt selection. - Scaling claims without enough model sizes, tasks, or compute repo
- Reproducibility audit
- Common ICLR weak points
- The reproducibility statement as a contract
- Worked vignette
- Reviewer-pushback patterns
- Output format
What does the iclr-reproducibility skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-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.