acmmm-topic-selection
Use when deciding whether a project is a genuine ACM MM (ACM Multimedia) contribution rather than single-modality work, choosing a thematic area, and routing between ACM MM, CVPR/ICCV, ACL/EMNLP, ICMR, MMSys, NeurIPS/ICLR, and the ACM TOMM journal by finding the cross-modal or media-systems core of the contribution.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-topic-selection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ACM MM Topic Selection Use this before writing. ACM MM is strongest for work that **treats more than one medium at once** — vision, audio/speech, language, sensor, interaction — or that advances the systems that transport, index, and render media. The core test is whether the contribution *lives at a seam between media*. ## Fit test - Prefer ACM MM when the contribution is **cross-modal integration** (fusion, alignment, cross-modal retrieval/generation), a **media-systems** advance (streaming, QoE, transport), or a **human-centric media** result (emotion, aesthetics, engagement, art). - Route to **CVPR/ICCV/ECCV** if the contribution is a pure computer-vision claim — a better detector, segmenter, or backbone with no essential second modality. - Route to **ACL/EMNLP** if it is a pure language claim, and to **NeurIPS/ICLR** if it is a general ML method whose multimedia setting is incidental. - Route to **ICMR** for retrieval-centric work that is more IR than multimedia systems, to **MMSys** for systems/networking-heavy media delivery, and to the **ACM TOMM** journal when the work needs journal-length treatment. - Confirm the argument can be made convincing in a **6–8 page** sigconf
- Fit test
- Fit signal table
- Picking the thematic area
- Vignette: where an audio-visual model goes
- Routing within ACM MM
- The single-modality trap
- Sharpening moves before committing
- Output format
What does the acmmm-topic-selection skill do?
Use when deciding whether a project is a genuine ACM MM (ACM Multimedia) contribution rather than single-modality work, choosing a thematic area, and routing between ACM MM, CVPR/ICCV, ACL/EMNLP, ICMR, MMSys, NeurIPS/ICLR, and the ACM TOMM journal by finding the cross-modal or media-systems core of the contribution.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-topic-selection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.