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 atc-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/atc-camera-ready)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/atc-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/atc-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/atc-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/atc-camera-ready.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 | $0.00074 | $0.01070 |
| Opus 5.5 | $0.00030 | $0.00428 |
| Sonnet 5.5 | $0.00015 | $0.00214 |
| Haiku 4.5 | $0.00007 | $0.00107 |
Grade A, and why
atc-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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ATC Camera-Ready
Use this after a conditional accept. Final acceptance is gated by the shepherd, so the camera-ready is not just formatting — it must fold in every required revision and pass the shepherd's sign-off, then satisfy ACM Open Access production. ATC 2026 publishes in the ACM Digital Library (a change from the old USENIX proceedings model), so the metadata and cost steps are new relative to pre-2026 USENIX ATC.
Satisfy the shepherd first
- Treat the shepherd's required-revisions list as binding for acceptance. Fold each change into
the camera-ready and keep the description-of-changes (see
atc-author-response) in sync with what actually landed. - Do not use the de-anonymization pass to quietly re-expand scope; the shepherd is checking that the paper the PC accepted is the paper being published.
- Get explicit shepherd sign-off before treating the paper as final.
Systematic de-anonymization
The submission was double-blind; the camera-ready is not. Reverse every anonymization deliberately:
[Authors] add the author list, affiliations, and ORCIDs
[Self-cites] restore first-person references where accurate ("our earlier system X [12]")
[System name] restore the real system/product name if you used a neutral placeholder
[Artifact] swap anonymized/blind-mirror links for the public, DOI-issuing archive
[Acks] add acknowledgments and funding/grant lines held out for blind review
[Metadata] set real PDF author/title metadata (it was scrubbed for submission)
Cross-check: search the final PDF for leftover placeholders ("Anonymous," "System-X-anon," blind URLs) — a stray one is the classic camera-ready miss.
Format and production
- Keep the two-column format (178 × 229 mm text block, 10pt) and the page budget; the camera-ready may allow the same or a slightly extended limit — confirm the exact camera-ready page allowance on the call (待核实).
- Which template file the SIGOPS edition mandates (the USENIX template vs. ACM
acmart/equivalent) is 待核实 — verify before recompiling, since ACM production checks depend on it. - Embed all fonts, check figure resolution and color-blind legibility, and confirm the PDF passes the venue's automated production/format checker.
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 · 88 lines · 74 tokens per session scan A dd5125a144a3
atc-camera-ready is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,264 stars, last pushed 19d ago), licensed MIT. It adds 74 tokens to every session and 1,070 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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