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 aeri-referee-strategygit 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/aeri-referee-strategy)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeri-referee-strategy"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeri-referee-strategy/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/aeri-referee-strategy"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeri-referee-strategy.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 | $0.00000 | $0.01109 |
| Opus 5.5 | $0.00000 | $0.00444 |
| Sonnet 5.5 | $0.00000 | $0.00222 |
| Haiku 4.5 | $0.00000 | $0.00111 |
Grade A, and why
aeri-referee-strategy 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Referee Strategy & the Fast Review Model (aeri-referee-strategy)
When to trigger
- Before submission, to understand how AER: Insights decides and what referees punish
- Deciding whether the paper can survive a process with no traditional R&R
- Anticipating the one or two objections that would sink a short paper
- Choosing whether to risk AER: Insights vs. a journal that offers multiple rounds
The AER: Insights process (official AEA baseline checked 2026-06-20)
AER: Insights runs single-blind AEA peer review: authors are visible to referees, while referees remain anonymous to authors. It is deliberately fast and decisive:
- First decision is conditional accept or reject — there is no traditional revise-and-resubmit category.
- Conditional accept means the editor expects to publish subject to changes in the decision letter; authors usually get about 8 weeks to return the revision, which is normally not sent back to referees for a second round.
- Reject is reject — there is no iterative path to wear referees down. The screening is sharp: a paper over the word/exhibit cap is returned unreviewed.
- Editor as of 2026: Matthew Gentzkow (lead editor beginning January 2024; founding editor Amy Finkelstein). Aim for AER-level importance in a short, clean package.
Strategic implication: because there is no R&R to fix things later, the paper must be submission-ready and objection-proofed up front. You get essentially one substantive look. A weak design, an overclaim, or a missing key check that an AER R&R would let you repair will instead produce a reject here.
Pre-empt the objections a short paper cannot survive
A short paper has no room to answer everything during review, so answer the decisive objections before submitting:
| Likely objection | Pre-empt before submission |
|---|---|
| "Not important / not general interest" | Lead with the surprise and the prior overturned (aeri-topic-selection) |
| "Already known / incremental" | Confront the closest paper directly (aeri-literature-positioning) |
| "Design not credible" | Put the single strongest diagnostic in-text (aeri-identification) |
| "Result not robust" | One in-text check + a signposted appendix battery (aeri-robustness) |
| "Overclaimed" | Calibrate the claim to exactly what is identified |
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 · 74 lines · 0 tokens per session scan A 4fd577e7eef8
aeri-referee-strategy is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,109 tokens. 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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