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 aejmac-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/aejmac-referee-strategy)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aejmac-referee-strategy"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-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/aejmac-referee-strategy"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-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 | $0.01157 |
| Opus 5.5 | $0.00000 | $0.00463 |
| Sonnet 5.5 | $0.00000 | $0.00231 |
| Haiku 4.5 | $0.00000 | $0.00116 |
Grade A, and why
aejmac-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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Referee Strategy (aejmac-referee-strategy)
When to trigger
- The draft is near-complete and you want to find the holes a referee will exploit
- You are gauging desk-reject risk or whether the paper is "ready"
- You need to decide which robustness checks to run before submitting vs. hold in reserve
- You want to pre-empt the predictable macro objections in the paper itself
How AEJ: Macro review works (verified 2026-06; re-confirm on the official AEA pages)
- Managed by the Editor (Ayşegül Şahin, 检索于 2026-06；以官网为准) and coeditors; single-blind external peer review (the author is known to the referee; referees are anonymous to the author). Note this is single-blind, unlike some AEJs/economics venues that anonymize the author.
- Desk rejection is possible without referees; editorial decisions weigh the probability of meeting AEJ: Macro standards, breadth of topic, and interest to the AEJ: Macro audience.
- After conditional acceptance, the AEA Data Editor runs the reproducibility check before publication (see
aejmac-replication-package).
The predictable macro objections (pre-empt each in the paper)
| Objection a macro referee raises | Where it lands | Pre-emption |
|---|---|---|
| "The identifying assumption is not defended" (Cholesky ordering, sign set, exclusion) | identification | Argue the assumption economically; show robustness to alternatives (aejmac-identification) |
| "The shock is anticipated / endogenous" | identification | Show unpredictability from macro history; use narrative/high-frequency instruments |
| "This is a Great-Moderation / pre-ZLB artifact" | robustness | Sample splits across the breaks, baseline shown alongside (aejmac-robustness) |
| "Calibration is undisciplined / free parameters" | theory-model | Source every parameter; targeted + untargeted moments; sensitivity |
| "The counterfactual ignores the Lucas critique" | theory-model | Argue policy-invariance of estimated parameters |
| "SVAR and LP would disagree" | robustness | Run the cross-method check and show agreement |
| "Set-identified results reported as a point" | identification / tables | Report the identified set / median-target with a band |
| "Not broad enough for AEJ: Macro" | topic / positioning | Re-frame for general interest; name the audience (aejmac-topic-selection) |
| "Results not reproducible" | replication | Build the openICPSR package early, incl. simulation code |
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 · 73 lines · 0 tokens per session scan A 45e70384fbd9
aejmac-referee-strategy is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,264 stars, last pushed 19d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,157 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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