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.
SkillScore74Maintained
Automatic
◆Research pack
Last verified 9/3/2026
What it changes
Passed 17 of 23 test cases with the skill, and 12 of 23 without it.
Without skill52%
With skill74%
Checks it passed that the model alone missed
Rates compute and data disclosure as incomplete.
Includes the exact tag [Evidence gaps] in the output.
Includes the exact tag [Priority fixes] in the output.
and 12 more in the full report
7 fixed10 passed either way4 still failing2 broke
Pass-rate lift
+22 pp
Tokens
+24%
Agent turns
0%
Response time
-9%
Model gemini-3.6-flashMeasured 9/3/2026v217 of 23 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 7
Prompt
We need an audit summary of our submission draft where dataset licenses are undisclosed and main claims use unbacked seeds. Please evaluate this draft and generate the structured audit report.
What was checked, without and with the skill
Checked
Without skill
With skill
Formats the response using the required bracketed audit tags.
Missed
Passed
Rates compute and data disclosure as incomplete.
Missed
Passed
case-20 · gemini-3.6-flash · 9/3/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md101 linessingle file
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.
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.
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.