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-identificationgit 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-identification)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aejmac-identification"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-identification/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-identification"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-identification.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 | $0.01677 |
| Opus 5.5 | $0.00000 | $0.00671 |
| Sonnet 5.5 | $0.00000 | $0.00335 |
| Haiku 4.5 | $0.00000 | $0.00168 |
Grade A, and why
aejmac-identification 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.
Empirical Identification (aejmac-identification)
When to trigger
- The macro effect rests on a recursive (Cholesky) SVAR with no defense of the ordering
- A monetary/fiscal "shock" is plausibly anticipated or endogenous to the cycle
- Local projections are run but lag length, controls, and inference are ad hoc
- A narrative or high-frequency instrument is used but its exogeneity/relevance is unargued
- You are unsure the design clears AEJ: Macro's identified-empirical bar
The AEJ: Macro identification bar
AEJ: Macro publishes identified-empirical macro, so the mapping from data to the dynamic causal object (an impulse response, a multiplier, a pass-through) must be explicit and defended. The aggregate, time-series setting makes identification harder than in micro: few effective observations, anticipation, simultaneity, and structural breaks. State the shock you claim to identify, the assumption that delivers it, and the horizon and object you report. Report standard errors / confidence bands (the AEA house style; significance asterisks are conventional in AEA tables but the band/SE must carry the inference, not the stars).
Branch paths
Branch A: Structural VAR (SVAR)
- Recursive (Cholesky): defend the ordering as an economic timing assumption, not a default; show robustness to plausible reorderings.
- Sign restrictions: state the full set; acknowledge set-identification (report the identified set / median-target with a credible band, not a point as if point-identified); address the "multiple models" critique.
- Long-run / Blanchard–Quah: justify the long-run neutrality assumption.
- Proxy-VAR / external instruments (SVAR-IV): show instrument relevance (reliability/F) and defend exogeneity; report weak-instrument-robust bands where relevance is marginal.
Branch B: Local projections (LP)
- Report the horizon-by-horizon IRF with bands; state lag length and control set and show robustness to them.
- Use Newey–West / HAC or LP-specific inference; for panel LP cluster appropriately.
- Consider LP-IV when the shock needs instrumenting; report the first-stage strength.
- Address the LP-vs-VAR bias/variance trade-off explicitly if both are plausible.
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 9ccaa5023993
aejmac-identification 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 1,677 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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