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-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/aejmac-theory-model)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aejmac-theory-model"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-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/aejmac-theory-model"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-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 | $0.01248 |
| Opus 5.5 | $0.00000 | $0.00499 |
| Sonnet 5.5 | $0.00000 | $0.00250 |
| Haiku 4.5 | $0.00000 | $0.00125 |
Grade A, and why
aejmac-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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantitative Theory & Model Discipline (aejmac-theory-model)
When to trigger
- Parameters are calibrated or estimated but it is unclear what disciplines each one
- A DSGE/HANK model is solved but the solution method / accuracy is unstated
- A counterfactual or welfare number is reported with no validity argument (Lucas critique)
- Untargeted moments are never shown, so the model's fit is asserted not demonstrated
- You are unsure the model clears AEJ: Macro's quantitative-discipline bar
The AEJ: Macro model bar
AEJ: Macro welcomes quantitative-theoretical macro, but the standard is discipline, not decoration: a calibration or structural estimate must be tied to data, the solution must be accurate enough for the claim, and the counterfactual must be defensible. The model exists to deliver a broad-interest macro quantity (a multiplier, a welfare cost, a share of inequality, a propagation magnitude), not to display machinery.
Discipline paths
Path A: Calibration discipline
- Source every parameter. Externally calibrated (cited micro/macro estimates) vs. internally calibrated (matched to targeted moments) — label each and give the target.
- Targeted moments table. Show data vs. model on the moments you matched.
- Untargeted-moment validation. Show the model matches moments it was not asked to match — this is the credibility payoff for calibration.
- Sensitivity. Report how the headline quantity moves with the key parameters (and which moment moves which parameter).
Path B: Structural estimation discipline
- Name what identifies each parameter — the data feature / moment, not "the likelihood." Report a sensitivity / informativeness measure (e.g., a sensitivity matrix) so readers see which data move which parameter.
- Estimator stated (MLE / GMM / SMM / indirect inference / Bayesian) with priors (if Bayesian), starting values, tolerances, and multi-start evidence of a global enough optimum.
- Monte Carlo recovery: simulated data return the true parameters.
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 · 78 lines · 0 tokens per session scan A 6004f8e63867
aejmac-theory-model 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 1,248 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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