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 acl-camera-readygit 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/acl-camera-ready)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acl-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-camera-ready/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/acl-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-camera-ready.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 | $0.00068 | $0.01362 |
| Opus 5.5 | $0.00027 | $0.00545 |
| Sonnet 5.5 | $0.00014 | $0.00272 |
| Haiku 4.5 | $0.00007 | $0.00136 |
Grade A, and why
acl-camera-ready 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Camera Ready
Use this after ACL (or Findings of ACL) acceptance. Camera-ready quality controls how the paper looks forever in the ACL Anthology, which is the permanent open-access record. For ACL 2026 the camera-ready deadline was April 19, 2026 — confirm the current edition's date in the acceptance email before planning anything.
The extra page and what it is for
- Accepted *ACL papers traditionally receive one additional content page: long papers up to 9 pages, short papers up to 5. The page exists to address reviewer and meta-review comments, not to smuggle in a new contribution.
- Spend it in priority order: fixes the meta-review asked for, clarifications reviewers requested, then de-anonymization overhead (author block, acknowledgements) which consumes real space.
- References remain unlimited; the Limitations section remains mandatory in the camera-ready and still sits outside the page count.
De-anonymization pass
- Restore authors, affiliations, and acknowledgements; convert third-person self-citations ("Smith showed") back to natural first person where clearer.
- Replace anonymous supplement links with the permanent public repository, dataset page, or model card; test every URL logged out.
- Add the funding and AI-assistance acknowledgements: ACL policy requires generative-AI use beyond polishing to be disclosed, with details in the Acknowledgements matching your Responsible NLP checklist answers.
Anthology-facing metadata
| Item | Why it matters at ACL | Common error |
|---|---|---|
| Title/abstract in the form | Becomes the Anthology landing page | Unicode or LaTeX macros pasted raw |
| Author names + order | Permanent citation record, BibTeX for everyone | Name spelled differently than prior papers |
| PDF fonts embedded | Anthology archival requirement | Missing Type-1/TrueType embedding from figures |
| License acceptance | Anthology publishes under CC BY 4.0 (post-2016 policy) | Assuming you can restrict reuse later |
| Video/poster uploads | Linked from the Anthology page when provided | Skipped, losing visibility |
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 · 125 lines · 68 tokens per session scan A 9509e4ec52f8
acl-camera-ready is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 1,362 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 2026-08-30.
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