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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.

Install / Use

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-review-process

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

83/100

Supported Platforms

Universal

Our assessment of aaai-review-process

aaai-review-process scores 83/100 on our quality scale, 2893rd of 4,610 Development & Engineering skills we index.

Its SKILL.md is 3.7 KB long, split into 7 sections with 1 code example: a solid amount of guidance for an agent.

With 1,158 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
15/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 18 days ago, so aaai-review-process is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

aaai-review-process compared with similar skills

All 4 of these similar skills score higher than aaai-review-process; compare them before choosing.

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Frequently asked questions

How do I install aaai-review-process?
Run npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-review-process. The install tabs above show the steps for each supported agent.
Which AI agents does aaai-review-process work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is aaai-review-process safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is aaai-review-process still maintained?
The repository was last updated 18 days ago, so aaai-review-process is actively maintained.

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-27 uses a two-phase review process. Phase 1 allocates three reviewers — two human reviews supplemented by one non-decisional AI-generated review. Papers with sufficiently negative reviews are rejected before author feedback (AAAI-27: notified 2026-09-24).
  • Papers continuing to Phase 2 receive additional reviews, up to five in total, and one author 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.
  • 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>

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryDevelopment
Updated18d ago
Forks153

Languages

Stata

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions