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-robustnessgit 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-robustness)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aejmac-robustness"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-robustness/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-robustness"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-robustness.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 | $0.01484 |
| Opus 5.5 | $0.00000 | $0.00594 |
| Sonnet 5.5 | $0.00000 | $0.00297 |
| Haiku 4.5 | $0.00000 | $0.00148 |
Grade A, and why
aejmac-robustness 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robustness Program (aejmac-robustness)
When to trigger
- The headline number rests on one specification, one sample, one lag length, or one grid
- A referee could ask "is this an artifact of [choice]?" and you have no panel of alternatives
- The empirical IRF and the model-implied response are compared but only at the baseline
- A structural/calibrated result has never been re-run under alternative targets
The AEJ: Macro robustness bar
Macro inference is fragile in characteristic ways: short effective samples, structural breaks (Great Moderation, ZLB, COVID), specification forks (lag length, detrending, prior, calibration target), and method dependence (SVAR vs. LP; perturbation vs. global). The AEJ: Macro robustness bar is to show the headline quantity survives the choices a skeptical macro referee would flip, and to be honest where it does not. Robustness is not a graveyard of extra tables — it is a targeted defense of the specific number the paper claims.
A macro robustness program (build the panel)
Empirical (SVAR / LP / narrative)
- Sample splits: pre/post-1984 (Great Moderation), exclude/keep the ZLB period, exclude COVID; report whether the response is stable.
- Specification: lag length, detrending/filtering choice (HP vs. one-sided vs. none), control set, levels vs. differences.
- Method cross-check: if SVAR is baseline, corroborate with LP (and vice versa); agreement is strong evidence.
- Inference: alternative HAC bandwidths / clustering; weak-instrument-robust bands for proxy-VAR/LP-IV.
- Identification variants: alternative orderings / sign sets / instrument constructions.
Quantitative (DSGE / HANK / structural)
- Alternative calibration targets and parameter ranges; show how the headline quantity moves.
- Alternative solution method / accuracy (higher perturbation order, finer grid) where nonlinearity matters.
- Alternative model elements (Taylor-rule coefficients, adjustment costs, market structure) the referee will name.
- Estimation: alternative moments / priors; re-estimate on a subsample.
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 · 94 lines · 0 tokens per session scan A c8814999e2c1
aejmac-robustness 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 1,484 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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