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Acmmm Reproducibility

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brycewang-stanford
acmmm-reproducibility

Use when strengthening the reproducibility of an ACM MM (ACM Multimedia) paper or preparing for the ACM MM Reproducibility track and ACM artifact badging — capturing environments, media/data access, seeds, and multimodal pipelines so an independent reviewer can rebuild results and reach Artifacts Evaluated or Results Reproduced badges.

Overview

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

Use it in TypingMind

Enable Acmmm Reproducibility 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 Reproducibility 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 Reproducibility 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 Reproducibility

Use this to make an ACM Multimedia result rebuildable — both for main-track credibility and for the dedicated Reproducibility track, which routes artifacts through ACM's badging pipeline. Multimedia adds a wrinkle: the data is often video, audio, or interactive media, and "run the code" is not enough if a reviewer cannot obtain or render the media.

What reproducibility means here

ACM's artifact model distinguishes availability, evaluation, and reproduction. Map your goal to the badge you are actually pursuing:

Badge (ACM terminology)What it assertsWhat you must ship
Artifacts AvailableThe artifact is publicly, permanently retrievableA DOI/archived repository with the code and media pointers
Artifacts Evaluated (Functional/Reusable)Reviewers ran it and it works / is reusableBuild + run instructions, environment, documentation
Results ReproducedAn independent team reproduced the paper's resultsA pipeline that regenerates the reported numbers/media

Confirm the exact badge set offered for the current cycle on the Reproducibility-track call; ACM's badge names and criteria evolve.

The multimodal reproducibility ledger

Keep a single record that ties each reported result to the code, data, and config that produced it:

text
result: Table 2, row "full model"
  code commit: <hash>
  config: configs/full.yaml
  data: <dataset name + version + anonymous mirror for review>
  media preprocessing: <fps, sample rate, caption source>
  seed(s): <list>
  hardware: <GPU/CPU, hours>
  expected output: results/table2_full.json

Media and data access

  • Provide an anonymous, working path to the data during double-blind review — a mirror that a reviewer can actually download, not a placeholder.
  • State the license and any consent/usage terms; user-generated media often cannot be redistributed, so document how a reviewer obtains it.
  • Pin preprocessing: frame rate, resampling, transcription source, and alignment — small differences here silently break multimodal results.

Determinism where it is achievable

  • Fix and log seeds; note where nondeterminism is irreducible (e.g., some GPU kernels) and report variance instead of pretending to bit-exactness.
  • Version the environment (container or lockfile) and record hardware, since media models are often memory- and throughput-sensitive.

Reproducibility-track readiness pass

  • The Reproducibility and Open Source tracks are single-blind, so the artifact carries its real identity — but the main-track review artifact must still be anonymous.
  • Package for a stranger: a reviewer with your README and nothing else should build, run, and hit an expected-output check within a bounded time.
  • Include a smoke check (see ../../resources/code/README.md) that verifies structure and media rendering before you submit.

Where multimodal pipelines silently break

Multimedia reproduction fails in places pure-code reproduction does not:

  • Codec and container drift — a video re-encoded with a different codec changes pixel values and breaks frame-exact results; pin the decode path.
  • Sample-rate and resampling — audio resampled by a different library shifts features; record the exact resampler and rate.
  • Caption/transcript source — if captions come from an ASR system or a platform, name the version; a different transcript is a different input.
  • Frame sampling — "every k-th frame" depends on the container's frame rate; state fps and the sampling rule.

A reproduction package that omits these looks complete but regenerates different numbers, which is worse than an honest gap.

Anonymous review vs. public artifact

The review artifact and the release artifact have different rules, and conflating them causes anonymity leaks or dead links:

  • During review (double-blind tracks): anonymous repository, anonymous data mirror, no author names in code comments, media metadata, or commit history.
  • At release (camera-ready): the public, de-anonymized repository with a permanent archive (DOI), the license, and the final media — replacing, not merely supplementing, the anonymous mirror.

Output format

text
[Badge target] Available / Evaluated / Results Reproduced
[Ledger] complete / gaps: <which results lack a trace>
[Data access] anonymous + licensed / broken or unlicensed
[Media preprocessing] pinned / underspecified
[Determinism] seeds+env logged / gaps
[Track blinding] correct for chosen track / mismatch
[Top fixes] <ordered>

Frequently asked questions

What does the Acmmm Reproducibility AI skill do?

Use when strengthening the reproducibility of an ACM MM (ACM Multimedia) paper or preparing for the ACM MM Reproducibility track and ACM artifact badging — capturing environments, media/data access, seeds, and multimodal pipelines so an independent reviewer can rebuild results and reach Artifacts Evaluated or Results Reproduced badges.

Why use Acmmm Reproducibility on TypingMind?

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

Which AI models can use Acmmm Reproducibility?

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 Reproducibility?

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

Is the Acmmm Reproducibility 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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