/aaai-review-process
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.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/aaai-review-process
Context 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.
SKILL.md
aaai-review-process.SKILL.mdname: 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.
AAAI Review Process
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.
Process model
- AAAI main technical track uses double-blind reviewing.
- AAAI-26 used a two-phase review process. Phase 1 had human reviews and a non-decisional
AI-generated review. Papers with sufficiently negative reviews could be rejected before author feedback.
- Papers continuing to Phase 2 received additional reviews and one author feedback phase.
- Final decisions were made through reviewer discussion and senior program committee oversight,
not by the AI review.
- Author feedback is short and constrained; it is mainly for correcting misunderstandings, not
replacing the paper.
Author strategy
- Reduce Phase 1 reject risk before submission by making contribution, evidence, and checklist
compliance obvious.
- When reviews arrive, distinguish human-review claims, AI-review errors, and AC/SPC decision
questions.
- Use rebuttal to resolve the highest-impact factual issue under the character limit.
- Do not attack the AI review. Correct it when it contains consequential false statements.
- Avoid new experiments in rebuttal; use submitted evidence and camera-ready promises sparingly.
Stage-by-stage decision map
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 |
Why papers die in Phase 1
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.
Worked vignette
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.
Output format
[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>
Read more
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.
AAAI Review Process
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.
Process model
- AAAI main technical track uses double-blind reviewing.
- AAAI-26 used a two-phase review process. Phase 1 had human reviews and a non-decisional
AI-generated review. Papers with sufficiently negative reviews could be rejected before author feedback.
- Papers continuing to Phase 2 received additional reviews and one author feedback phase.
- Final decisions were made through reviewer discussion and senior program committee oversight,
not by the AI review.
- Author feedback is short and constrained; it is mainly for correcting misunderstandings, not
replacing the paper.
Author strategy
- Reduce Phase 1 reject risk before submission by making contribution, evidence, and checklist
compliance obvious.
- When reviews arrive, distinguish human-review claims, AI-review errors, and AC/SPC decision
questions.
- Use rebuttal to resolve the highest-impact factual issue under the character limit.
- Do not attack the AI review. Correct it when it contains consequential false statements.
- Avoid new experiments in rebuttal; use submitted evidence and camera-ready promises sparingly.
Stage-by-stage decision map
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 |
Why papers die in Phase 1
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.
Worked vignette
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.
Output format
[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>
Stanford REAP × CoPaper.AI · 由斯坦福实证方法论团队精选与维护 访问 copaper.ai 微信:CoPaper.AI 按 11 个主流学科板块覆盖 经管与商科 社会科学 人文学科 数学与物理科学 生命科学 医学与健康 工程与技术 计算机科学与 AI 体育科学 点击任一学科名可跳转到对应说明;每类下的代表子领域在正文总览中完整列出。下方封面墙按 venue 导航,完整分类见覆盖一览。 🧭 布局指南 · 📚 Skill Pack 一览 · ⚡ 如何使用 · 🧪 自动实证
Other skills on awesome-journal-skills.
- /aaai-artifact-evaluation
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.
Open skill - /aaai-author-response
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, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round
Open skill - /aaai-camera-ready
Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and
Open skill - /aaai-experiments
Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and
Open skill - /aaai-related-work
Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a
Open skill - /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
Open skill

