aaai-reproducibility
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-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.
# AAAI Reproducibility Use this when a draft needs to survive AAAI review on rigor, not just novelty. AAAI-26 required a reproducibility checklist after references, so the checklist must agree with the paper and supplement rather than read as an afterthought. ## 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 repr
- Reproducibility audit
- Common AAAI weaknesses
- Checklist-to-evidence consistency grid
- Claim-evidence ledger
- Artifact dry-run
- Reviewer-pushback patterns
- Worked vignette
- Output format
What does the aaai-reproducibility skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-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.