
Aaai Reproducibility
Use when strengthening an AAAI paper's reproducibility checklist (placed after...
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-reproducibility -g -a claude-code -y- Repository
- brycewang-stanford/Awesome-Journal-Skills1.3k
- Category
- Data and ML
- Updated
- 1mo ago
- License
- MIT
- Context cost
- Lightabout 1,425 tokens
- Runs scripts
- No, instructions only
What it does
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
AAAI Reproducibility
Use this when a draft needs to survive AAAI review on rigor, not just novelty. AAAI-27 requires the reproducibility checklist to be uploaded separately from the main PDF, in its own field on the submission form (AAAI-26 carried it inside the PDF after the references), so it is a document a reviewer opens on its own, and it has to agree with the paper and supplement rather than read as an afterthought. AAAI-27 also states that reviewers assess reproducibility from what was actually submitted, and that material promised "after acceptance or publication" is not evidence it exists.
Reproducibility audit
- Map each central claim to submitted evidence: theorem, table, figure, ablation, appendix item, checklist answer, or code/data artifact.
- Record seeds, splits, preprocessing, hyperparameters, model selection, early stopping, prompt selection, and hardware.
- Report variance or uncertainty when stochasticity affects conclusions.
- Document dataset licenses, access constraints, sensitive data, human-subjects issues, and annotation procedures.
- Separate training compute, inference compute, and experiment search cost.
- Check the reproducibility checklist for contradictions with the main text and supplement.
Common AAAI weaknesses
- Checklist says code/data are available but supplement lacks runnable commands.
- Main results rely on one seed, one benchmark, or one prompt family.
- Baselines are weaker than current open-source or widely cited systems.
- Evaluation uses closed data or APIs with no reproducibility substitute.
- Human evaluation omits annotator instructions or quality control.
Checklist-to-evidence consistency grid
AAAI places the reproducibility checklist after the references, and reviewers cross-check each "yes" against the paper and supplement. A "yes" with no backing artifact reads worse than an honest "no", because it signals the checklist was filled in carelessly.
