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 aeja-theory-modelgit 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/aeja-theory-model)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeja-theory-model"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-theory-model/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-theory-model"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-theory-model.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 | $0.01096 |
| Opus 5.5 | $0.00000 | $0.00438 |
| Sonnet 5.5 | $0.00000 | $0.00219 |
| Haiku 4.5 | $0.00000 | $0.00110 |
Grade A, and why
aeja-theory-model 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.
Theory & Model for Interpretation (aeja-theory-model)
When to trigger
- A referee asks "what is the mechanism / what model rationalizes this?"
- The reduced-form estimate is credible but its economic meaning is ambiguous
- You want a welfare statement, an elasticity, or a counterfactual the raw estimate cannot deliver
- You are tempted to lead the paper with a full structural model and need to right-size it for AEJ: Applied
The AEJ: Applied theory dial
AEJ: Applied is empirical-first. Theory earns its place only when it interprets the estimate, sharpens the estimand, or unlocks a magnitude the design cannot deliver alone — never as the headline. Pick the lightest tool that does the job and keep the empirical estimate the star.
| Theory's job | Right amount of model | Where it goes |
|---|---|---|
| Name the mechanism | a few equations / a conceptual framework | short section before results |
| Map a reduced-form coefficient to a structural parameter | a sufficient-statistic / envelope argument | inline derivation + appendix |
| Deliver a welfare or counterfactual number | a calibrated or partially-structural model | a dedicated section, clearly bounded |
| Discipline heterogeneity / sign predictions | a simple model generating testable comparative statics | framework section, tested in results |
Sufficient-statistic style (often the AEJ: Applied sweet spot)
Where possible, express the welfare/policy object as a function of estimable elasticities (a Harberger/Chetty-style sufficient statistic) rather than estimating a full structural model. This keeps the credibility in the reduced-form design while delivering an economic magnitude. State the assumptions under which the sufficient statistic is valid and what it omits.
When a fuller model is warranted
If the question genuinely requires out-of-sample counterfactuals or unobservable primitives, a small structural model is acceptable — but tie each parameter to a data feature, validate against an untargeted moment, and never let the model's assumptions silently replace the identification the design provided.
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 ce3e26c706ab
aeja-theory-model 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 1,096 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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