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 ase-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/ase-review-process)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ase-review-process"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-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/ase-review-process"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-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 | $0.00081 | $0.01436 |
| Opus 5.5 | $0.00032 | $0.00574 |
| Sonnet 5.5 | $0.00016 | $0.00287 |
| Haiku 4.5 | $0.00008 | $0.00144 |
Grade A, and why
ase-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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASE Review Process
Model the pipeline before interpreting any single review. ASE's process has two distinctive gates that authors from a plain accept/reject conference miss: an early-rejection stage that ends the process before rebuttal for the weakest papers, and a first-class Revision outcome whose criteria are fixed in advance. Getting both wrong wastes the little leverage authors have.
Process model
- Submission and review run on HotCRP with double-anonymous review: identities are hidden from reviewers, and authors reference their own prior work in the third person.
- Each paper receives multiple reviews scoring the significance and soundness of the automation, the quality of the evaluation on real subjects, threats to validity, clarity, and the Data Availability / artifact posture.
- Early-rejection stage: papers whose initial scores are uniformly negative are rejected before the rebuttal period. This is a mercy and a filter — it gives those authors a fast answer and concentrates rebuttal effort where it can change an outcome. If your paper survives to rebuttal, at least one reviewer saw something.
- Rebuttal, then a final outcome of Accept, Revision, or Reject.
- Revision is criteria-bound: instead of rejecting, reviewers specify concrete, actionable revision criteria and agree to accept the paper in principle if those criteria are met. Authors return a revised paper with a point-by-point summary of changes, re-checked (often by a discussion lead) against the stated criteria.
- Accepted papers appear in the proceedings in both IEEE Xplore and the ACM Digital Library; camera-ready compliance and artifact follow-through matter after the verdict.
Reading a decision against the categories
| Decision | What it means | Author move |
|---|---|---|
| Accept | The automation and its evaluation hold; minor polish only | Camera-ready + artifact; do not reopen scope |
| Revision | Repairable gaps captured as explicit criteria: a missing baseline, an ablation, a clarified construct | Meet every stated criterion, or justify why one is infeasible; map each to a change in the summary-of-changes |
| Reject | Structural: the automation is unsound, the evaluation is not on real subjects, the delta is thin | Reframe or reroute (ICSE/FSE/ISSTA or a journal); do not lightly resubmit unchanged |
| Early reject (pre-rebuttal) | Uniformly negative initial scores | No rebuttal opportunity; diagnose honestly and rebuild before any resubmission |
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 · 99 lines · 81 tokens per session scan A 5fa946b4b116
ase-review-process is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It adds 81 tokens to every session and 1,436 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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