aaai-artifact-evaluati…
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without…
Use when explaining or planning around AAAI's two-phase review process, Phase 1 rejection risk, Phase 2 additional reviews, AI-assisted review pilot, author feedback, SPC/AC discussion, and final decisions.
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-review-process --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/aaai-review-processContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when explaining or planning around AAAI's two-phase review process, Phase 1 rejection risk, Phase 2 additional reviews, AI-assisted review pilot, author feedback, SPC/AC discussion, and final decisions.
name: aaai-review-process description: Use when explaining or planning around AAAI's two-phase review process, Phase 1 rejection risk, Phase 2 additional reviews, AI-assisted review pilot, author feedback, SPC/AC discussion, and final decisions.
Use this to plan around AAAI review rather than treating it as a generic OpenReview rebuttal. Reopen the current review-process page and author FAQ before advising on timing or strategy.
supplemented by one non-decisional AI-generated review. Papers with sufficiently negative reviews are rejected before author feedback (AAAI-27: notified 2026-09-24).
feedback phase (AAAI-27: 2026-10-19 to 10-25, final decisions 2026-11-30). Phase 2 reviewers are not shown the Phase 1 reviews until they have submitted their own — so a Phase 2 review is an independent read, not a reaction to the earlier ones.
not by the AI review.
replacing the paper.
compliance obvious.
questions.
AAAI's pipeline differs from a single-round OpenReview venue, so plan actions per stage rather than treating every signal as a rebuttal opportunity.
| Stage | What is happening | Author leverage | | --- | --- | --- | | Pre-submission | Phase-1 bar is set by clarity and checklist | maximal: fix the paper itself | | Phase 1 | human reviews plus advisory AI review | none yet; summary reject possible | | Phase 2 | additional reviews, one feedback round | one short response, no new results | | Discussion | reviewers and SPC/AC weigh feedback | indirect: a clean correction can swing it | | Decision | SPC/AC oversight, not the AI review | archive everything for appeal or journal |
Because the reviewer pool is large and submission volume is high, clearly-below-bar papers are cut early to protect later effort. Common triggers: an unreadable first page, a contribution a non-specialist cannot place, a checklist that contradicts the paper, or evidence too thin to trust. None of these can be repaired after the Phase-1 cut, so they must be eliminated before submission.
An NLP paper with strong results buries its contribution under three pages of setup. A Phase-1 reviewer from a planning background cannot find the AI claim and scores it a reject; the paper never reaches feedback. The fix belongs entirely pre-submission: a first-page contribution statement and a checklist that matches the experiments, so the broad-AI reviewer can place and trust it fast.
[Stage] pre-submission / Phase 1 / Phase 2 / rebuttal / discussion / decision [Decision risk] summary reject / borderline / likely accept / ethics-policy risk [Best action] revise before submission / rebut / clarify evidence / escalate [AI-review handling] ignore / correct / cite submitted evidence [Rationale] <why this fits AAAI process>
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Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without…
Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance,…
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance,…
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human…
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across…
Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting,…