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-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/aeja-robustness)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeja-robustness"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-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/aeja-robustness"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-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 | $0.01551 |
| Opus 5.5 | $0.00000 | $0.00620 |
| Sonnet 5.5 | $0.00000 | $0.00310 |
| Haiku 4.5 | $0.00000 | $0.00155 |
Grade A, and why
aeja-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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robustness Suite (aeja-robustness)
When to trigger
- The main estimate is in hand and you need to show it is not an artifact of one specification
- A referee asks "is this robust to [alternative controls / sample / functional form / inference]?"
- The result depends on a bandwidth, a clustering choice, or a sample-selection rule that could be questioned
- You suspect specification-search concerns and want to pre-empt them
The AEJ: Applied robustness bar
AEJ: Applied referees probe whether the headline number is stable, honestly inferred, and not the product of researcher degrees of freedom. Robustness here is not a wall of regressions — it is a targeted set of checks each tied to a specific threat to the design. Map every plausible objection to the one check that answers it, and report the checks so the reader sees the estimate barely moves.
| Threat to the result | The check that answers it |
|---|---|
| Omitted confounders | Oster δ / coefficient-stability bounds; added controls in steps |
| Specification search | a specification curve / multiverse; pre-registered primary spec |
| Functional form | levels vs logs, alternative outcome definitions, nonparametric version |
| Sample selection | drop influential units, alternative inclusion rules, balanced vs unbalanced panel |
| Inference too narrow | clustered SEs at the right level, wild-cluster bootstrap (few clusters), randomization inference |
| Design-specific fragility | DID: honest-DID bounds; RD: bandwidth/donut; IV: weak-IV-robust set |
| Multiple outcomes/subgroups | Romano–Wolf / List–Shaikh–Wooldridge MHT adjustment |
Robustness craft
- Lock the primary specification first. Everything else is a perturbation around it; do not present five co-equal specs and let the reader guess which is preferred.
- One threat → one check. A robustness table should read as "here is the worry, here is the evidence it is not a problem."
- Show stability, not just significance. The persuasive object is that the point estimate barely moves, not that it stays starred.
- Be honest about where it weakens. A check that shifts the estimate is information; report it and bound the implication rather than hiding it.
- Match inference to the data structure (clustering, spatial dependence, few clusters) — wrong SEs are the most common AEJ: Applied robustness failure.
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 · 95 lines · 0 tokens per session scan A 08102b6447a2
aeja-robustness 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 1,551 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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