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-review-processgit 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-review-process)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-review-process"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-review-process/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-review-process"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-review-process.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 | $0.00073 | $0.01023 |
| Opus 5.5 | $0.00029 | $0.00409 |
| Sonnet 5.5 | $0.00015 | $0.00205 |
| Haiku 4.5 | $0.00007 | $0.00102 |
Grade A, and why
acmmm-review-process 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACM MM Review Process
Use this to build an accurate mental model of how an ACM Multimedia paper is judged, so strategy targets the points where an author can move the outcome and not the points where they cannot.
The pipeline
- Submission + thematic area. The paper enters OpenReview under a chosen thematic area, which routes it to a matching reviewer pool and area chair.
- Assignment. Reviewers and an AC are assigned; a mismatched thematic area is where papers get reviewers who cannot judge the contribution.
- Reviews. Reviewers assess novelty, cross-modal soundness, evidence (including user studies where relevant), and reproducibility.
- Rebuttal. An optional, anonymous author response addresses reviews; new external links are not allowed.
- Discussion + meta-review. Reviewers and the AC discuss; the AC writes a meta-review and recommendation.
- Decision + tiers. Accept/reject, with accepted papers sorted into presentation tiers (oral vs. poster) and award consideration.
Where leverage exists
| Stage | Author leverage | Reality |
|---|---|---|
| Thematic-area choice | High | You pick who reviews you — choose the area that can judge the contribution |
| Submission quality | High | The paper is the main lever; the rebuttal only patches |
| Rebuttal | Medium | Fix factual errors and add small confirmatory results; rarely flips a strong reject |
| Discussion | Low/indirect | You cannot see it; a clean rebuttal gives the AC ammunition |
| Decision/tiers | None directly | Set by AC/PC after discussion |
Reading a review set
- Separate factual errors (a reviewer misread a result) from judgment (they find the delta small); the first is fixable in rebuttal, the second usually is not.
- Weight the cross-modal critiques: "the fusion is not shown to matter" is often the decisive line, and an ablation is the answer.
- Note the AC's implicit questions in the meta-review; that is who the rebuttal is really written for.
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 · 92 lines · 73 tokens per session scan A b2e841fe8ced
acmmm-review-process is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 1,023 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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