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 ccs-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/ccs-review-process)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ccs-review-process"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ccs-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/ccs-review-process"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ccs-review-process.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 | $0.00055 | $0.00718 |
| Opus 5 | $0.00028 | $0.00359 |
| Sonnet 5 | $0.00011 | $0.00144 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
ccs-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 13d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CCS Review Process
Use this to reason about review-stage strategy. Reopen the current CFP, the cycle's HotCRP site, the reviewer guidelines if posted, and the ethics policy before making process claims.
Process model
- CCS runs two review cycles per year on HotCRP, each with abstract registration, full-paper submission, review, a rebuttal window, and notification.
- Reviewers assess threat-model soundness, novelty, technical correctness, evidence quality, ethics and responsible disclosure, and significance to the security community.
- Decisions are not binary: CCS 2026 used accept, minor revision (authors revise for the same cycle under a shepherd or check), and reject. A first-cycle reject cannot return that year.
- An ethics or disclosure concern can sink an otherwise strong paper; the ethics reviewer's view carries weight independent of technical scores.
- Accepted papers publish in the ACM Digital Library proceedings, so camera-ready and metadata compliance matter as much as the initial decision.
Who reviews here
- The pool is adversarial by training: expect at least one reviewer to attack your threat model, look for the assumption that makes the attack easy, and probe whether the defense was tested against an adaptive attacker.
- CCS is broad, so a paper may draw reviewers from an adjacent sub-area; the threat model and intro must orient a web-security reviewer to a crypto result and vice versa.
- Borderline papers usually fall on one of three edges: an attacker assumption too strong to be interesting, a defense never adapted against, or an ethics gap left unaddressed.
Scoring leverage table
| Review dimension | What raises it | What sinks it |
|---|---|---|
| Threat model | A tight, realistic adversary stated up front | Capabilities that quietly grow to make the attack work |
| Novelty | An attack class or guarantee the literature lacked | An increment over a cited paper with no new idea |
| Evidence | End-to-end demonstration against a real target | Lab-only results against a strawman |
| Ethics | Disclosure done, harm minimized, reasoned in the paper | Real-world harm with no disclosure or IRB reasoning |
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.
- 13d ago First seen · 63 lines · 55 tokens per session scan A c3514580044e
ccs-review-process is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,097 stars, last pushed 16d ago), licensed MIT. It adds 55 tokens to every session and 718 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 2026-08-30.
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