Awesome Journal Skills is a collection of agent skill packs tailored to hundreds of academic journals across fields including economics, social science, medicine, science, and engineering. Researchers use the packs for tasks such as choosing topics, designing empirical strategies, preparing tables and figures, submitting papers, and responding to reviewers. The catalogue entries are the project's journal-specific skills and related plugins.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill acmmm-topic-selectiongit clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-topic-selection)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-topic-selection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-topic-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00086 | $0.01296 |
| Opus 5.5 | $0.00034 | $0.00518 |
| Sonnet 5.5 | $0.00017 | $0.00259 |
| Haiku 4.5 | $0.00009 | $0.00130 |
Grade A, and why
acmmm-topic-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 body.
Fit signal table
| Signal in the project | ACM MM reading |
|---|---|
| Two or more modalities that must interact for the result to hold | Core fit — the house genre |
| A reusable media system, framework, or dataset the community adopts | Core fit (Open Source / Dataset tracks) |
| Subjective quality / emotion / engagement measured with a user study | Core fit (human-centric areas) |
| A single-modality benchmark win (vision-only, text-only) | Better at CVPR/ICCV or ACL |
| Retrieval accuracy with no systems or cross-modal novelty | ICMR or SIGIR |
| Media delivery / networking with little content modeling | MMSys |
Picking the thematic area
The main track is split into thematic areas (Multimodal Fusion; Generative and Foundation Models; Search and Recommendation; Emotional and Social Signals; Art and Culture; Systems; Transport and Delivery; Responsible Multimedia; and more). The area is not cosmetic — it selects your reviewers. Name the primary area honestly; if the paper genuinely spans two, pick the one whose reviewers can best judge the contribution, not the application.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 103 lines · 86 tokens per session scan A bb083c74799d
acmmm-topic-selection is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 1,296 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-15.
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