aistats-reproducibility
Use when strengthening AISTATS reproducibility evidence, including the official reproducibility checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, code/data release statements, and checklist-to-claim consistency audits.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-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.
# AISTATS Reproducibility Use this before submission and again before camera-ready. Reopen the current CFP and OpenReview forms to confirm whether a reproducibility checklist is required. ## Evidence map - Map each theorem, algorithmic claim, simulation claim, and empirical claim to a verifiable location in the paper, appendix, supplement, or artifact package. - For theory, state assumptions, proof dependencies, convergence conditions, constants, and failure modes clearly enough for statistical readers. - For experiments, report datasets, splits, preprocessing, evaluation metrics, baselines, hyperparameter ranges, final selected settings, seeds, repeated runs, compute, and runtime. - For small performance differences, add uncertainty estimates: standard errors, confidence intervals, paired tests, bootstrap intervals, or repeated trials as appropriate. - Explain missing code/data honestly and describe how a reader could reproduce the analysis in principle. - Keep the checklist consistent with the manuscript; contradictions between checklist and paper are review-risk multipliers. ## Checklist-to-claim audit table | Checklist item | Pure-theory answer | Theory-plus-experiments answer
- Evidence map
- Checklist-to-claim audit table
- Vignette: a rates-plus-simulation paper
- Degrees of reproducibility
- Output format
What does the aistats-reproducibility skill do?
Use when strengthening AISTATS reproducibility evidence, including the official reproducibility checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, code/data release statements, and checklist-to-claim consistency audits.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-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.