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-related-workgit 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-related-work)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-related-work"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-related-work/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-related-work"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-related-work.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00090 | $0.01177 |
| Opus 5.5 | $0.00036 | $0.00471 |
| Sonnet 5.5 | $0.00018 | $0.00235 |
| Haiku 4.5 | $0.00009 | $0.00118 |
Grade A, and why
acmmm-related-work 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACM MM Related Work
Use this to position an ACM Multimedia paper against a literature that is unusually spread out: a cross-modal contribution touches several single-modality communities plus the multimedia venues that combine them.
The five multimedia shelves
A strong ACM MM related-work section shows command of all shelves the contribution touches, not just the author's home community:
- Vision — the visual side (CVPR/ICCV/ECCV) your method builds on or competes with.
- Audio/speech and music — the acoustic side (ICASSP, INTERSPEECH, ISMIR) if a stream is audio.
- Language — the text side (ACL/EMNLP) if captions, transcripts, or descriptions matter.
- HCI / QoE / human-centric — perception, engagement, and interaction (CHI, QoMEX) when the claim is subjective.
- Multimedia proper — ACM MM, ICMR, MMSys, and TOMM, where these threads are combined.
Coverage vs. venue-hygiene table
| Task | What to do | Failure it prevents |
|---|---|---|
| Cover each modality you use | Cite the current best single-modality work per stream | "You ignored the vision literature" |
| Cite the fusion lineage | Trace the cross-modal line your method extends | "No delta over existing fusion" |
| Verify the venue string | Confirm each "ACM MM" cite on dblp conf/mm |
Misattributing an ICMR/CVPR paper to ACM MM |
| Handle concurrency | Note contemporaneous arXiv work honestly | "You missed / overclaimed novelty vs. X" |
| Keep it blind | Cite your own prior work in third person | Double-blind violation |
Positioning, not listing
Do not enumerate. For each closest neighbor, state in one clause what it did and in one clause what your paper adds — and make sure the delta is cross-modal, since "we swap a better encoder" is a single-modality delta a reviewer will discount.
[Neighbor] Late-fusion audio-visual highlight scoring (VenueYear).
[Their move] Average per-modality scores.
[Our delta] Score the timing DISAGREEMENT between streams — a signal averaging cannot represent.
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 · 101 lines · 90 tokens per session scan A 62e834f9025a
acmmm-related-work is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 90 tokens to every session and 1,177 once invoked, about $0.0004 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 2026-08-30.
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