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Acmmm Author Response

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brycewang-stanford
acmmm-author-response

Use when drafting the ACM MM (ACM Multimedia) rebuttal — turning multiple anonymous reviews into one focused, anonymous response that fixes factual errors first, adds small confirmatory cross-modal results, concedes calibratedly, and stays inside policy (anonymous, no new external links), written for the area chair who decides.

Overview

Publisherbrycewang-stanford
RepositoryAwesome-Journal-Skills
Skill nameacmmm-author-response
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 Author Response 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-author-response
  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-author-response .claude/skills/acmmm-author-response
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Acmmm Author Response 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 Author Response 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 Author Response 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 Author Response

Use this to write the ACM Multimedia rebuttal. It is optional and anonymous, it must not add new external links, and its real audience is the area chair who will weigh it against the reviews in discussion.

Triage before writing

Sort every review point into one of three buckets:

BucketExampleResponse
Factual error"No ablation of the fusion" (there is one, in §4.3)Point to it precisely; this is your strongest move
Fixable gap"Missing a late-fusion baseline"Add the small result if you can run it in time
Judgment call"The delta is incremental"Reframe the significance briefly, or concede narrowly

Lead with the factual corrections — they are the points an AC can verify and act on fastest.

Structure for the AC

Write for a reader who is skimming several rebuttals. Make it navigable:

text
[R1, R2 shared concern: fusion not shown to matter]
  -> Table A (new): leave-one-modality-out; audio-text term = +X of the +Y total gain.
[R1 factual: "no user study"]
  -> §5 reports a user study, N raters, agreement kappa; see supplement S3.
[R3 judgment: "incremental over late fusion"]
  -> Our gain over the late-fusion baseline is in Table 1; the mechanism (Table A) is the novelty.
[R2 request: clarify synchronization assumption]
  -> Conceded; we will state it explicitly in §3 (one sentence).

Group shared concerns once; do not repeat the same answer per reviewer.

Small confirmatory results

  • Add only results you can actually produce before the deadline — a leave-one-modality-out ablation, one requested baseline, or a small user-study extension.
  • Describe new numbers in text/tables inside the rebuttal; you cannot add a link to an external file, and you cannot add pages to the paper.
  • Do not promise experiments you cannot show; an AC discounts unbacked promises.

Calibrated concessions

Conceding a fair minor point raises credibility on the points you contest. State clearly what you will change (a sentence, a clarified assumption, a moved figure) and why the core claim survives it.

Policy guardrails

text
Anonymous: no author names, institutions, or identifying links.
No new external links: results go in the rebuttal text/tables, not a URL.
No scope change: you cannot rewrite the contribution or add pages.
No reviewer-targeting: never argue a reviewer is unqualified or try to identify them.

Length and format discipline

  • Keep the rebuttal tight and scannable; an AC reading many responses rewards structure over volume, and a wall of text buries your best point.
  • Put new evidence in a small in-line table, not prose — a reviewer can check a table in seconds.
  • Respect any length limit the cycle sets, and do not try to smuggle in a link to sidestep it.

Rebuttal skeleton by review type

text
Strong-reject-on-a-misreading:  correct the fact FIRST, with an exact section/figure pointer.
Borderline-wants-a-baseline:    add the baseline in a mini-table; state it is now in the paper plan.
"Fusion is incremental":        show the mechanism ablation (the term that carries the gain).
"Subjective claim unproven":    report the user study's N, agreement, and result inline.
Fair-minor-point:               concede in one sentence and say exactly what changes.

Tone

Precise and non-defensive. Address the reviewer's underlying concern, not their wording; a rebuttal that reads as annoyed helps the negative reviewer more than you.

Output format

text
[Factual corrections] <ordered, with exact pointers>
[New results] <ablation/baseline/study added in-rebuttal>
[Concessions] <narrow, with the fix>
[Contested] <judgment points, reframed>
[Policy check] anonymous / no new links / no scope change — pass or fix

Frequently asked questions

What does the Acmmm Author Response AI skill do?

Use when drafting the ACM MM (ACM Multimedia) rebuttal — turning multiple anonymous reviews into one focused, anonymous response that fixes factual errors first, adds small confirmatory cross-modal results, concedes calibratedly, and stays inside policy (anonymous, no new external links), written for the area chair who decides.

Why use Acmmm Author Response on TypingMind?

Because you install it once and use it with any model. Acmmm Author Response 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 Author Response 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-author-response. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Acmmm Author Response?

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 Author Response?

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

Is the Acmmm Author Response 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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