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 aaai-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/aaai-review-process)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aaai-review-process"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aaai-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/aaai-review-process"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aaai-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 | $0.00048 | $0.00836 |
| Opus 5 | $0.00024 | $0.00418 |
| Sonnet 5 | $0.00010 | $0.00167 |
| Haiku 4.5 | $0.00005 | $0.00084 |
Grade A, and why
aaai-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 12d 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AAAI Review Process
Use this to plan around AAAI review rather than treating it as a generic OpenReview rebuttal. Reopen the current review-process page and author FAQ before advising on timing or strategy.
Process model
- AAAI main technical track uses double-blind reviewing.
- AAAI-27 uses a two-phase review process. Phase 1 allocates three reviewers — two human reviews supplemented by one non-decisional AI-generated review. Papers with sufficiently negative reviews are rejected before author feedback (AAAI-27: notified 2026-09-24).
- Papers continuing to Phase 2 receive additional reviews, up to five in total, and one author feedback phase (AAAI-27: 2026-10-19 to 10-25, final decisions 2026-11-30). Phase 2 reviewers are not shown the Phase 1 reviews until they have submitted their own — so a Phase 2 review is an independent read, not a reaction to the earlier ones.
- Final decisions were made through reviewer discussion and senior program committee oversight, not by the AI review.
- Author feedback is short and constrained; it is mainly for correcting misunderstandings, not replacing the paper.
Author strategy
- Reduce Phase 1 reject risk before submission by making contribution, evidence, and checklist compliance obvious.
- When reviews arrive, distinguish human-review claims, AI-review errors, and AC/SPC decision questions.
- Use rebuttal to resolve the highest-impact factual issue under the character limit.
- Do not attack the AI review. Correct it when it contains consequential false statements.
- Avoid new experiments in rebuttal; use submitted evidence and camera-ready promises sparingly.
Stage-by-stage decision map
AAAI's pipeline differs from a single-round OpenReview venue, so plan actions per stage rather than treating every signal as a rebuttal opportunity.
| Stage | What is happening | Author leverage |
|---|---|---|
| Pre-submission | Phase-1 bar is set by clarity and checklist | maximal: fix the paper itself |
| Phase 1 | human reviews plus advisory AI review | none yet; summary reject possible |
| Phase 2 | additional reviews, one feedback round | one short response, no new results |
| Discussion | reviewers and SPC/AC weigh feedback | indirect: a clean correction can swing it |
| Decision | SPC/AC oversight, not the AI review | archive everything for appeal or journal |
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.
- 12d ago First seen · 73 lines · 48 tokens per session scan A c1594efc7ae7
aaai-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 48 tokens to every session and 836 once invoked, about $0.0002 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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