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-literature-positioninggit 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-literature-positioning)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aejmac-literature-positioning"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-literature-positioning/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-literature-positioning"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aejmac-literature-positioning.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 | $0.00949 |
| Opus 5.5 | $0.00000 | $0.00380 |
| Sonnet 5.5 | $0.00000 | $0.00190 |
| Haiku 4.5 | $0.00000 | $0.00095 |
Grade A, and why
aejmac-literature-positioning 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Positioning (aejmac-literature-positioning)
When to trigger
- The introduction cites many papers but never says what is new
- A referee could ask "how is this different from [the obvious prior paper]?" and you lack a one-line answer
- The paper sits between a theory literature and an empirical literature and the bridge is unclear
- The contribution is real but framed as incremental ("we extend X by adding Y")
The AEJ: Macro positioning bar
AEJ: Macro is a broad-interest macro journal, so positioning must locate the paper for a general macro reader, not only for the three people working on the exact model or shock series. Two literatures usually bound a macro paper, and you must place the contribution against both:
- the quantitative-theory frontier (DSGE / HANK / heterogeneous-agent / structural) — what does your model deliver that the canonical model cannot?
- the identified-empirical frontier (SVAR / local projections / narrative / proxy-VAR / micro-data macro) — what does your evidence pin down that prior designs left open?
A hybrid paper (model matched to identified evidence) must say why the combination answers a question neither side resolved alone — this is often the strongest AEJ: Macro contribution.
Positioning moves
Move 1 — Name the closest 2–3 papers, then the wedge
Do not list 30 citations. Name the 2–3 papers a referee would call "basically the same," and state in one sentence what your paper does that each cannot. The wedge should be a quantity, mechanism, or identification gain, not "a different dataset."
Move 2 — Place against both frontiers
- vs. theory frontier: "The canonical [RBC/NK/HANK] model predicts/assumes Z; we relax/measure Z and the macro implication changes by ___."
- vs. empirical frontier: "Prior [SVAR/LP] designs cannot separate channel A from channel B; our [narrative/high-frequency/sign-restriction] identification does."
Move 3 — State the contribution as a sentence with a quantity
"We show that [mechanism] accounts for [X%] of [aggregate], overturning/refining the prior view that ___." A magnitude makes the contribution legible to a general reader and to the AEA process.
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 · 67 lines · 0 tokens per session scan A 7bf2d118f4d9
aejmac-literature-positioning 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 949 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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