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 aejpol-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/aejpol-robustness)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aejpol-robustness"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejpol-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/aejpol-robustness"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejpol-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 | $0.00063 | $0.01464 |
| Opus 5 | $0.00032 | $0.00732 |
| Sonnet 5 | $0.00013 | $0.00293 |
| Haiku 4.5 | $0.00006 | $0.00146 |
Grade A, and why
aejpol-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 7d 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 — Defending the Policy Estimate (aejpol-robustness)
When to trigger
- The headline causal estimate moves across specifications, or you do not yet know if it does
- A referee will ask "is this robust?" and you have no organized answer
- Inference (clustering, few clusters, multiple outcomes) is not yet airtight
- You need to show the policy conclusion, not just a coefficient, survives stress
Principle: robustness defends the policy conclusion, not the coefficient
At AEJ: Policy, robustness is judged by whether the policy takeaway is stable — if the headline estimate is the cost-per-job or the MVPF, show that number is stable, with its uncertainty, not merely that a regression coefficient stays significant. Organize the robustness program around the threats that would change the policy conclusion, and report enough that a skeptical referee can see each threat addressed.
Robustness by threat (each maps to a concrete check)
| Threat to the policy conclusion | Check |
|---|---|
| Functional form / controls drive the result | Specification ladder; show the estimate across a coherent set, not a single lucky spec |
| Pre-trends / parallel-trends violation | Honest-DID (Rambachan–Roth) sensitivity bounds; placebo pre-period "effects" |
| Estimator bias under staggered timing | Re-estimate with ≥1 heterogeneity-robust DID estimator (CS / SA / BJS / dCDH) |
| Bandwidth / kernel (RDD) | Bandwidth sweep + bias-corrected CIs; donut-RDD if heaping at the cutoff |
| Weak / invalid instrument | Effective F; AR-robust CI; over-ID test if available |
| Wrong inference / few clusters | Wild-cluster bootstrap; report clustering level sensitivity |
| Multiple outcomes / specifications | Romano–Wolf / sharpened q-values; a specification curve where many specs are run |
| Confounding by an omitted policy/shock | Controls for co-timed policies; event-study around the focal reform only |
| Selection on unobservables | Oster (2019) δ / bounds; argue the implied selection is implausible |
| Sample composition / outliers | Drop influential jurisdictions; winsorize; alternative sample windows |
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.
- 7d ago First seen · 95 lines · 0 tokens per session scan A db2dab02cc86
aejpol-robustness is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,142 stars, last pushed 7d ago), licensed MIT. It adds 63 tokens to every session and 1,464 once invoked, about $0.0003 per session on Opus 5. 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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