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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skillsnpx agentmods add skills/brycewang-stanford/awesome-journal-skills/aeja-referee-strategyWrote 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/aeja-referee-strategy)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeja-referee-strategy"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-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/aeja-referee-strategy"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-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 | $0.01088 |
| Opus 5.5 | $0.00000 | $0.00435 |
| Sonnet 5.5 | $0.00000 | $0.00218 |
| Haiku 4.5 | $0.00000 | $0.00109 |
Grade A, and why
aeja-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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Referee Strategy (aeja-referee-strategy)
When to trigger
- The paper is near submission and you want to stress-test it against the referees it will draw
- You need to decide which robustness checks to run before submitting vs. hold in reserve
- An R&R arrived and you must map referee concerns to concrete responses
- You want to understand how AEJ: Applied's single-blind peer-review process shapes strategy
How AEJ: Applied review works (plan around it)
AEJ: Applied uses single-blind review through the AEA ScholarOne system: author identities are visible to referees, while referee identities remain anonymous to authors. An editor assigns the paper to a coeditor or handles it directly, and before a first R&R the handling coeditor consults a second coeditor. Referees here are identification-literate applied microeconomists; the modal report attacks the credibility of the design first, then magnitude/mechanism, then external validity. The editor weighs whether the contribution is novel and the design clears the bar. Plan to pre-empt the predictable objections in the paper itself, leaving the response letter for genuinely new asks.
The objection map (pre-empt in the paper)
| Referee objection (by design) | Pre-emption to build in now |
|---|---|
| "TWFE is biased under staggered timing" | heterogeneity-robust estimator + Bacon decomposition already in the paper |
| "Parallel trends is not credible" | clean pre-trend leads + honest-DID sensitivity bound |
| "Instrument is not excludable" | institutional + theoretical exclusion argument + falsification tests |
| "RD jump is manipulation / bandwidth-driven" | density test + bandwidth sensitivity + donut |
| "Effect is just selection / confounding" | Oster δ / coefficient-stability bounds |
| "Local/ITT effect ≠ the policy-relevant effect" | explicit estimand + calibrated external-validity discussion |
| "Inference too narrow (few clusters)" | wild-cluster bootstrap / randomization inference |
| "Mechanism is a black box" | a test that distinguishes the proposed channel from rivals |
| "Not reproducible" | the openICPSR package built and referenced (aeja-replication-package) |
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 · 72 lines · 0 tokens per session scan A 89d1e6051404
aeja-referee-strategy is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,259 stars, last pushed 19d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,088 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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