Acmmm Submission logo

Acmmm Submission

CommunityPopular
brycewang-stanford
acmmm-submission

Use when auditing an ACM MM (ACM Multimedia) submission for OpenReview readiness — thematic-area choice, the 6-8 page ACM sigconf budget, references-only overflow, double-blind anonymity versus the single-blind Reproducibility/Open-Source/Dataset tracks, supplementary media, dual submission, desk-reject triggers, and last-week sequencing.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacmmm-submission
Stars
1.2K
Forks
159
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by brycewang-stanford on GitHub. Read the source before you install it.

Installation

Install the Acmmm Submission AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-submission
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git /tmp/Awesome-Journal-Skills
mkdir -p .claude/skills
cp -r /tmp/Awesome-Journal-Skills/ACM-MM-Skills/skills/acmmm-submission .claude/skills/acmmm-submission
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acmmm Submission in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Acmmm Submission on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Acmmm Submission is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

ACM MM Submission

Use this to run a pre-deadline audit of an ACM Multimedia paper. Reopen the current Call for Technical Papers, the Topics of Interest page, each relevant track call, and the OpenReview group before giving deadline-ready advice — ACM MM rewrites these each edition.

Submission audit

  • Confirm the target is the ACM MM main conference (or a specific track), not a co-located workshop, ACM MM Asia, MMSys, or ICMR.
  • Choose the thematic area deliberately: it routes the paper to a reviewer pool, and a mismatched area is a quiet source of off-topic reviews.
  • Apply the current ACM sigconf template with no margin, font, or spacing edits. ACM MM 2026 used a 6–8 page body; references may spill onto up to two additional pages that contain only references.
  • Enforce double-blind anonymity across the paper, appendix, supplement, code, media assets, filenames, PDF metadata, and repository links — unless submitting to the named single-blind tracks (Reproducibility, Open Source Software, Dataset), where the artifact's identity is expected.
  • Register the abstract before the abstract deadline; the paper and the supplement have their own later deadlines (in 2026: abstract Mar 25, paper Apr 1, supplement Apr 8, AoE).
  • Check concurrent-submission rules and prior-publication status; do not keep the same work under review at another archival venue when ACM MM forbids it.

Blocking risks

  • Late abstract registration or paper upload.
  • Overlength body or a tampered sigconf template.
  • Identity leak in the PDF, media metadata, demo watermark, or repository link (on a double-blind track).
  • Wrong track blinding — anonymizing a Dataset/Open-Source submission, or de-anonymizing a main-track one.
  • Non-rendering or proprietary-format supplementary media.
  • Dual submission to another archival venue.

Desk-reject and triage table

TriggerSeverity at ACM MMRepair window
Overlength body (past 8 pages)Desk rejectNone after the deadline
Non-references content on the overflow pagesDesk reject or chair flagBefore the deadline only
Author identity in a double-blind PDF/mediaDesk rejectNone
Wrong thematic areaOff-topic reviews, not a desk rejectFixable before the paper deadline
Supplement that will not play for a reviewerLost evidenceFixable before the supplement deadline
Missing OpenReview coauthor profilesSubmission blockedBefore the deadline

Last-week sequence for a cross-modal paper

  1. Freeze the thematic-area choice and confirm the paper's claims match that area's scope.
  2. Re-render every figure and media clip from scripts so body, supplement, and PDF agree.
  3. Anonymize media: strip author names from filenames, EXIF/metadata, and any on-screen watermark, and route dataset/demo links through an anonymous mirror.
  4. Confirm the OpenReview abstract matches the PDF abstract word for word.
  5. Verify the body fits 8 pages with the overflow pages holding references only.

OpenReview mechanics that trip people up

ACM MM runs on OpenReview, but the failure modes are administrative, not intellectual:

  • Every coauthor needs a complete OpenReview profile with a real institutional history, or the submission is blocked or the conflict graph is wrong.
  • The abstract is registered before the paper — a separate, earlier deadline. Missing it forecloses the paper deadline entirely.
  • Declare conflicts honestly; an undeclared conflict discovered later can sink a paper.
  • The thematic area is set at submission and is hard to change afterward; it selects your reviewers, so treat it as a first-class decision, not a dropdown afterthought.

Track and blinding double-check

The most ACM MM-specific desk-reject cause is submitting to the wrong track's blinding regime.

If you are submitting to...BlindingSo the PDF must...
Main track / Brave New IdeasDouble-blindHide all author identity, in text, media, and links
Reproducibility trackSingle-blindName the artifact and its authors as expected
Open Source Software CompetitionSingle-blindPresent the real project and repository
Dataset trackSingle-blindIdentify the dataset and its providers

Anonymizing a single-blind submission (or de-anonymizing a double-blind one) both signal that the authors did not read the track call — fix it before upload, not after.

Format anchors

  • ACM MM's two-column sigconf layout compresses hard: wide multimodal figures, algorithm blocks, and qualitative media grids overflow the 8-page body, so budget page space as a design decision early, not on deadline night.
  • The page counts, deadlines, and track blinding cited here describe the 2026 cycle; treat every number as provisional and recheck the current CFP and track calls before relying on it.

Output format

text
[ACM MM readiness] Ready / Needs fixes / Not ready
[Track + thematic area] <main/BNI/dataset/... + area>
[Blocking checks] <OpenReview/page/anonymity/media/dual-submission>
[Cross-modal evidence risk] <one issue>
[Desk-reject risk] <one issue>
[Fix order] <ordered fixes before submission>

Frequently asked questions

What does the Acmmm Submission AI skill do?

Use when auditing an ACM MM (ACM Multimedia) submission for OpenReview readiness — thematic-area choice, the 6-8 page ACM sigconf budget, references-only overflow, double-blind anonymity versus the single-blind Reproducibility/Open-Source/Dataset tracks, supplementary media, dual submission, desk-reject triggers, and last-week sequencing.

Why use Acmmm Submission on TypingMind?

Because you install it once and use it with any model. Acmmm Submission is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Acmmm Submission in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ACM-MM-Skills/skills/acmmm-submission. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acmmm Submission?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Acmmm Submission?

As many as you like. As long as a model supports skills, you can use Acmmm Submission with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Acmmm Submission AI skill free?

Yes. It is published on GitHub by brycewang-stanford under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇