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 asplos-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/asplos-camera-ready)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/asplos-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/asplos-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/asplos-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/asplos-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 | $0.00060 | $0.01468 |
| Opus 5 | $0.00030 | $0.00734 |
| Sonnet 5 | $0.00012 | $0.00294 |
| Haiku 4.5 | $0.00006 | $0.00147 |
Grade A, and why
asplos-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 today.
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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASPLOS Camera-Ready
The camera-ready deadline at ASPLOS 2027 sits roughly six weeks after notification — verified indirectly, via the CFP's statement that Major Revisions are resubmitted "at the camera ready deadline (6 weeks from notification)" (checked 2026-07-08; the exact date and page allowance for camera-ready are 待核实 and arrive with the acceptance email). The window is shared by two very different tasks, so the first step is knowing which track you are on.
Two tracks through the same deadline
| Track | What is due | Judged by |
|---|---|---|
| Accepted paper | Final, de-anonymized, ACM-formatted PDF + forms | Publication staff + shepherd if assigned (shepherding for 2027: 待核实) |
| Major Revision | The revised submission — still a reviewed document | The program committee, as a submission on record |
Do not apply camera-ready instincts to a Major Revision: it stays within submission rules (anonymity unless told otherwise — confirm in the decision letter), and its companion document is a change note keyed to the decision letter, item by item, with pointers to where each required change landed. If this revision follows an earlier revision, the note describes deltas relative to the previous revision, per the CFP's revision-counts-as-submission rule.
De-anonymization, done as a diff
Reversing double-blind is not just adding an author block. Work from the submission-time anonymity sweep, inverting each entry:
- Restore author names, affiliations, and emails in the ACM template's format.
- Convert third-person self-citations back to natural first-person where it reads better — and check none of them still says "the authors of [12]" about yourself.
- Restore acknowledgments and grant numbers; keep the GenAI disclosure placed per ACM policy (immediately before References when in Acknowledgments).
- Re-point anonymized supplemental citations to their real, now-citable targets.
- Replace neutralized infrastructure descriptions with the real testbed identity where confidentiality allows.
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.
- today First seen · 128 lines · 60 tokens per session scan A 5e1b35be84a4
asplos-camera-ready is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,109 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 1,468 once invoked, about $0.0003 per session on Opus 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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