acmmm-review-process
Use when reasoning about the ACM MM (ACM Multimedia) review pipeline — thematic-area routing to reviewers and area chairs, the OpenReview double-blind process and its single-blind track exceptions, the optional anonymous rebuttal, the meta-review and decision, and the oral/poster and award tiers, an…
Install / Use
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-review-processInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Our assessment of acmmm-review-process
acmmm-review-process scores 87/100 on our quality scale, 1481st of 2,881 Automation skills we index.
Its SKILL.md is 4.4 KB long, well organised into 10 sections with 2 code examples: 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 acmmm-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.
acmmm-review-process compared with similar skills
All 4 of these similar skills score higher than acmmm-review-process; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| acmmm-review-process (this skill)by brycewang-stanford | 87 | 1.2k | 18d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 89.0k | 17d ago | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 85.3k | 2d ago | MCP Server |
| rufloby ruvnet | 100 | 73.8k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 10d ago | SKILL.md |
Frequently asked questions
- How do I install acmmm-review-process?
- Run
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-review-process. The install tabs above show the steps for each supported agent. - Which AI agents does acmmm-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 acmmm-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 acmmm-review-process still maintained?
- The repository was last updated 18 days ago, so acmmm-review-process is actively maintained.
Skill content
View source on GitHubname: acmmm-review-process description: Use when reasoning about the ACM MM (ACM Multimedia) review pipeline — thematic-area routing to reviewers and area chairs, the OpenReview double-blind process and its single-blind track exceptions, the optional anonymous rebuttal, the meta-review and decision, and the oral/poster and award tiers, and where an author actually has leverage.
ACM MM Review Process
Use this to build an accurate mental model of how an ACM Multimedia paper is judged, so strategy targets the points where an author can move the outcome and not the points where they cannot.
The pipeline
- Submission + thematic area. The paper enters OpenReview under a chosen thematic area, which routes it to a matching reviewer pool and area chair.
- Assignment. Reviewers and an AC are assigned; a mismatched thematic area is where papers get reviewers who cannot judge the contribution.
- Reviews. Reviewers assess novelty, cross-modal soundness, evidence (including user studies where relevant), and reproducibility.
- Rebuttal. An optional, anonymous author response addresses reviews; new external links are not allowed.
- Discussion + meta-review. Reviewers and the AC discuss; the AC writes a meta-review and recommendation.
- Decision + tiers. Accept/reject, with accepted papers sorted into presentation tiers (oral vs. poster) and award consideration.
Where leverage exists
| Stage | Author leverage | Reality | |---|---|---| | Thematic-area choice | High | You pick who reviews you — choose the area that can judge the contribution | | Submission quality | High | The paper is the main lever; the rebuttal only patches | | Rebuttal | Medium | Fix factual errors and add small confirmatory results; rarely flips a strong reject | | Discussion | Low/indirect | You cannot see it; a clean rebuttal gives the AC ammunition | | Decision/tiers | None directly | Set by AC/PC after discussion |
Reading a review set
- Separate factual errors (a reviewer misread a result) from judgment (they find the delta small); the first is fixable in rebuttal, the second usually is not.
- Weight the cross-modal critiques: "the fusion is not shown to matter" is often the decisive line, and an ablation is the answer.
- Note the AC's implicit questions in the meta-review; that is who the rebuttal is really written for.
Double-blind and its exceptions
The main track and Brave New Ideas are double-blind; the Reproducibility, Open Source Software, and Dataset tracks are single-blind because the artifact carries its identity. Confidentiality runs both ways: reviewers must not deanonymize authors, and authors must not try to identify or contact reviewers.
What the rebuttal cannot do
CAN: correct misreadings, add a promised small experiment/ablation, clarify scope, concede narrowly
CANNOT: add new external links, change the contribution, add pages, argue the reviewer is unqualified
Reading scores and the meta-review
- A spread of scores (one champion, one detractor) is normal; the rebuttal targets the detractor's concrete objection and gives the champion and AC something to cite.
- A uniform lukewarm set is harder than a split — there is no champion to convert, so the rebuttal must move a shared concern, usually the cross-modal-significance one.
- Weight the AC's meta-review most: it is the synthesis the decision rests on, and its implicit questions are what your response should answer.
The dates that constrain strategy
The pipeline has a long middle: reviews and rebuttal cluster in late spring, and the decision lands in early July (2026: rebuttal around June 4, notification early July). The strategic consequence is that the confirmatory experiments a reviewer will ask for should be ready before reviews arrive — the rebuttal window is too short to start a new user study or a large ablation from scratch.
Confidentiality and conduct
- Treat all reviews and discussion as confidential.
- Do not attempt to deanonymize reviewers or lobby ACs outside the system.
- Report suspected violations through the official channel, not by public posting.
Output format
[Area routing] well-matched / mismatched (off-topic review risk)
[Review split] factual-fixable: <list> | judgment: <list>
[Decisive critique] <usually the cross-modal/evidence line>
[Rebuttal leverage] high / medium / low
[Next action] <what to fix vs. what to accept>
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Languages
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.
