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Acmmm Writing Style

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brycewang-stanford
acmmm-writing-style

Use when revising an ACM MM (ACM Multimedia) paper for house style — putting the cross-modal contribution on the first page, framing media (figures, video, audio) as evidence rather than decoration, making the fusion the visible claim, and compressing the argument into a 6-8 page ACM sigconf body with references-only overflow.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacmmm-writing-style
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 Writing Style 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-writing-style
  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-writing-style .claude/skills/acmmm-writing-style
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acmmm Writing Style 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 Writing Style 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 Writing Style 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 Writing Style

Use this to revise an ACM Multimedia draft for the venue's expectations. The reader is a busy reviewer scanning across sixteen thematic areas; the paper must announce what is multimedia about it before the model diagram.

The ACM MM first-page arc

Lead the abstract and first paragraph with: problem → why one modality is insufficient → the cross-modal method or system → media-grounded evidence → why it matters for multimedia. The multimedia contribution belongs on page one; a paper that opens with a single-modality benchmark reads as a CVPR or ACL paper that wandered in.

  • State the fusion or systems mechanism as the contribution, not a backbone swap.
  • Make every media element do work: a teaser figure that shows the cross-modal signal, a video that is evidence for a claim, an audio clip that a reader can check.
  • Scope claims to what the evidence supports; "improves engagement" needs a measured engagement result behind it.

Media-as-evidence table

Media elementWeak (decoration)Strong (evidence)
Teaser figureA pretty system diagramThe moment where modalities disagree and the method wins
Qualitative gridCherry-picked successesPaired success/failure across modalities with captions that state the point
Supplementary video"See our results"A clip tied to a specific claim, with the baseline shown alongside
Audio sampleAn unlabeled waveformThe case the vision-only baseline misses, annotated

Compression into the sigconf body

The 6–8 page body is short by ACM standards, and figures compete with text for space.

  • Push proofs, full protocols, extra qualitative media, and hyperparameter tables to the supplement; keep the body's argument self-contained without them.
  • Write self-contained captions — a reviewer reading only figures and captions should grasp the cross-modal claim.
  • Budget page space before writing: decide which two or three media elements earn body space and which move to the supplement.

Revision passes

text
Pass 1 (contribution): Does page one name the cross-modal/systems contribution?
Pass 2 (fusion): Is the mechanism the claim, and is it ablated later?
Pass 3 (media): Does each figure/clip support a specific claim, with a self-contained caption?
Pass 4 (scope): Is every "better/more engaging/higher quality" tied to a measured result?
Pass 5 (budget): Does the body fit 6-8 sigconf pages with overflow holding references only?

Title and abstract for cross-area reviewers

Your reviewers may come from different thematic areas, so the title and abstract have to be legible to a vision person, an audio person, and a systems person at once.

  • Put the modalities and the mechanism in the title where natural ("audio-visual," "text-and- image," "cross-modal") so area chairs assign the right reviewers.
  • Make the abstract's first two sentences carry the whole contribution; a reviewer triaging many papers may read little more.
  • Avoid single-community jargon in the abstract; define the one term your cross-area readers will not share.

A body-budget worked pass

Treat the 6–8 page limit as a budget you allocate before writing prose:

text
p1  intro: contribution + why one modality fails + teaser figure
p2  related work (tight) + problem setup
p3-4 method: the fusion/alignment mechanism, one architecture figure
p5-6 experiments: main table, the decisive ablation, failure cases, user-study summary
p7-8 discussion + limitations; references spill onto the overflow pages (references only)

If a section will not fit, move detail to the supplement rather than shrinking the font or margins — template tampering is a desk-reject risk, and a cramped body reads worse than a clean one with a fuller supplement.

Common ACM MM style failures

  • Modality as garnish — audio/text mentioned but never shown to matter; fix by leading with the seam and ablating it.
  • Vision-paper voice — the whole framing is a benchmark race; fix by foregrounding the multimedia question.
  • Unbacked perceptual claims — "more natural," "more engaging" with no user study; fix by measuring or softening.
  • Caption starvation — figures that only make sense from the body text; fix by making captions stand alone.
  • Overflow abuse — method text pushed onto the references pages; those pages are for references only, and misuse risks desk reject.

Output format

text
[First-page verdict] multimedia contribution up front / buried
[Fusion visibility] mechanism is the claim / hidden behind a backbone
[Media evidence] each element earns its place / decorative elements: <list>
[Scope] claims matched to evidence / overclaims: <list>
[Page budget] fits 6-8 sigconf pages / over by <n>
[Top three fixes] <ordered>

Frequently asked questions

What does the Acmmm Writing Style AI skill do?

Use when revising an ACM MM (ACM Multimedia) paper for house style — putting the cross-modal contribution on the first page, framing media (figures, video, audio) as evidence rather than decoration, making the fusion the visible claim, and compressing the argument into a 6-8 page ACM sigconf body with references-only overflow.

Why use Acmmm Writing Style on TypingMind?

Because you install it once and use it with any model. Acmmm Writing Style 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 Writing Style 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-writing-style. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acmmm Writing Style?

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 Writing Style?

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

Is the Acmmm Writing Style 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.

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