aamas-review-process
Use when explaining or planning around AAMAS peer review, covering OpenReview review release, the double-blind rebuttal on preliminary reviews, area-chair discussion, the mixed game-theory, MARL, and systems reviewer pool, the public posting of reviews and decisions, and how acceptance criteria weig…
Install / Use
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-review-processInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
OtherSupported Platforms
Our assessment of aamas-review-process
aamas-review-process scores 83/100 on our quality scale, 149th of 216 Other skills we index.
Its SKILL.md is 3.5 KB long, split into 6 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.
Maintenance, license and trust
- The repository was last updated 18 days ago, so aamas-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.
aamas-review-process compared with similar skills
All 4 of these similar skills score higher than aamas-review-process; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| aamas-review-process (this skill)by brycewang-stanford | 83 | 1.2k | 18d ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 10d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 10d ago | SKILL.md |
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| ui-ux-pro-maxby nextlevelbuilder | 100 | 130.2k | 12d ago | SKILL.md |
Frequently asked questions
- How do I install aamas-review-process?
- Run
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aamas-review-process. The install tabs above show the steps for each supported agent. - Which AI agents does aamas-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 aamas-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 aamas-review-process still maintained?
- The repository was last updated 18 days ago, so aamas-review-process is actively maintained.
Skill content
View source on GitHubname: aamas-review-process description: Use when explaining or planning around AAMAS peer review, covering OpenReview review release, the double-blind rebuttal on preliminary reviews, area-chair discussion, the mixed game-theory, MARL, and systems reviewer pool, the public posting of reviews and decisions, and how acceptance criteria weigh the interaction contribution.
AAMAS Review Process
Use this to reason about review-stage strategy. Reopen the current CFP, OpenReview group, author and reviewer instructions, and the code of conduct before making process claims; AAMAS review specifics move between editions.
Process model
- AAMAS runs submission and review on OpenReview under the IFAAMAS namespace in recent cycles.
- Reviewers evaluate technical correctness, the significance of the interaction contribution, the reality and rigor of the multiagent evaluation, clarity, reproducibility, and fit with agents-and-multiagent-systems scope.
- A rebuttal lets authors respond to preliminary reviews before the final decision; area chairs then synthesize.
- Accepted papers and their reviews are published, so the review record is durable and public.
- The most useful rebuttal gives the area chair a clean rationale for acceptance, not a point-by-point defense of every comment.
Who reviews here
- The pool mixes game theorists, multiagent-RL researchers, mechanism-design and social-choice specialists, and systems-minded reviewers; expect at least one to read the game definition and solution concept line by line.
- Because AAMAS is specialized, a paper is likely to meet a reviewer who works on exactly its subarea, so a vague equilibrium claim or an under-specified opponent set gets caught rather than skimmed.
- Borderline interaction papers usually fail on one of three edges: the result turns out to be single-agent in disguise, the solution concept is never pinned down, or the multiagent evaluation is thin (self-play only, no seeds, no held-out opponents).
Scoring leverage table
| Review dimension | What raises it | What sinks it | |---|---|---| | Correctness | A stated game, a named solution concept, and a body-level proof sketch | Hidden information structure; an equilibrium asserted but never defined | | Significance | A finding that only exists because agents interact | An incremental single-agent gain wearing a multiagent label | | Empirical support | Experiments that probe strategy: held-out opponents, deviation tests | Self-play-only curves disconnected from the claim | | Clarity | One notation source and a legible game description | Notation and payoff conventions that shift between sections |
Stage-by-stage realism
- Initial reviews: triage by what the area chair would weigh, not by reviewer tone.
- Rebuttal: windows are short; an early, precise reply anchored in submitted evidence beats a late exhaustive one.
- Decision: the area chair synthesizes, and one unanswered correctness or interaction-reality objection outweighs several resolved clarity complaints.
- Public record: assume the reviews and your rebuttal will be visible with the paper, and keep the exchange professional and concrete.
Output format
[Current stage] submitted / reviews / rebuttal / decision / camera-ready
[Decision actors] <reviewers / area chair / program chairs>
[Likely leverage] <correctness / interaction-reality / significance / experiments / clarity>
[Forbidden moves] <identity leak / new results / revised-paper upload if disallowed>
[Next response move] <one action>
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
