From aistats-skills
Audits AISTATS submissions against dual-community literature (ML conferences and statistics journals) for novelty, eligibility, and citation coverage.
How this skill is triggered — by the user, by Claude, or both
Slash command
/aistats-skills:aistats-related-workThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this to audit novelty and eligibility. Reopen the current CFP for dual-submission,
Use this to audit novelty and eligibility. Reopen the current CFP for dual-submission, anonymity, and prior-publication rules before advising authors.
| Literature lane | Typical sources | What AISTATS reviewers check |
|---|---|---|
| ML conferences | NeurIPS, ICML, ICLR, UAI, COLT, prior AISTATS volumes in PMLR | Whether the nearest ML method is compared or explicitly distinguished |
| Statistics journals | Annals of Statistics, JMLR, JASA, Biometrika, EJS | Whether classical estimators and known rates are acknowledged |
| Applied statistical fields | Econometrics, biostatistics, epidemiology | Whether identification and inference assumptions follow standard usage |
A bibliography citing only ML venues tells a statistician reviewer that known statistical results may be getting rediscovered — a recognizable AISTATS reject pattern that no amount of benchmark strength repairs.
Imagine the paper proposes a variance-reduced off-policy evaluation estimator with an asymptotic normality result. Its nearest neighbors: a NeurIPS estimator with no inference guarantee, a JASA semiparametric efficiency bound, and a prior AISTATS paper with a slower rate. The novelty sentence should name all three contrasts — inference where the ML line had none, computational tractability where the statistics line stayed abstract, and a sharper rate than the direct predecessor.
[Eligibility] clear / needs declaration / risky
[Closest literatures] <ML/statistics/application>
[Nearest 3 works] <work -> distinction>
[Archival-overlap risk] <none/issues>
[Novelty sentence] <AISTATS-ready contribution contrast>
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin aistats-skillsEvaluates manuscript fit for the International Conference on Artificial Intelligence and Statistics (AISTATS), providing venue-specific framing, evidence bar, and submission-cycle checks.
Helps determine whether a research project fits AISTATS vs. other ML/stats venues, identifies the statistical primitive, and sharpens the AI-statistics framing.
Audits an IJCAI submission's novelty, prior work positioning, and compliance with dual-submission/resubmission rules. Useful for distinguishing archival vs. non-archival versions and citing arXiv/workshop papers.