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Use when packaging code, models, datasets, or media as ACM MM (ACM Multimedia) artifacts — building the anonymous review package versus the public release, and choosing between the Open Source Software Competition, the Dataset track, the Reproducibility track, and main-track supplementary evidence, each with its own blinding and expectations.
Passed 21 of 22 test cases with the skill, and 14 of 22 without it.
and 10 more in the full report
8 fixed13 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am preparing our video summarization software toolkit for submission to ACM Multimedia. We currently have an open GitHub repository with documentation, installation scripts, and permissive code licensing, but haven't set up an anonymous mirror yet. Could you review our release plan and give me a report covering which track we should target, whether our blinding plan aligns with the track rules, the readiness and gaps in both our submission-time and final public artifacts, any outstanding code or media rights issues, and a prioritized list of mandatory fixes?
| Checked | Without skill | With skill |
|---|---|---|
| Recommends the Open Source Software Competition track | Passed | Passed |
| Identifies that the Open Source Software Competition track uses single-blind review | Missed | Passed |
| Includes the section header [Track] | Missed | Passed |
| Includes the section header [Blinding] | Missed | Passed |
The instructions your model reads when the skill loads.
Use this to turn an ACM Multimedia project's code, models, media, and data into the right artifact for the right track. ACM MM has a track economy around artifacts, and the choice determines blinding, format, and what reviewers judge.
| Artifact is primarily... | Route to | Blinding | Judged on |
|---|---|---|---|
| A reusable software system/framework | Open Source Software Competition | Single-blind | Adoption, quality, license, docs |
| A new dataset/benchmark | Dataset track | Single-blind | Scale, quality, ethics, usefulness |
| A reproduction of published results | Reproducibility track | Single-blind | Whether results rebuild; ACM badges |
| Supporting evidence for a method paper | Main-track supplement | Double-blind | Whether it backs the paper's claims |
The named single-blind tracks exist because the artifact's identity cannot be hidden; a main-track method paper's artifact, by contrast, must be anonymous through review.
Plan both from the start:
review/ -> anonymous repo, anon data mirror, no names in code/media, run instructions
release/ -> public repo + DOI, LICENSE, model weights, dataset card, citation
test cases passed, vs 14 / 22 without
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