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-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/aeja-literature-positioning)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeja-literature-positioning"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-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/aeja-literature-positioning"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeja-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 | $0.01192 |
| Opus 5.5 | $0.00000 | $0.00477 |
| Sonnet 5.5 | $0.00000 | $0.00238 |
| Haiku 4.5 | $0.00000 | $0.00119 |
Grade A, and why
aeja-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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Positioning (aeja-literature-positioning)
When to trigger
- A referee or co-author asks "what is new here relative to [closely related paper]?"
- The introduction cites a wall of references but never states the marginal addition
- You are unsure whether your contribution is an answer, a design, a setting, or a measurement
- The paper risks reading as a replication or a marginal extension of an existing AEJ: Applied / AER paper
The AEJ: Applied contribution bar
At AEJ: Applied the contribution is judged as: a credibly identified answer to a question the applied-micro community has not yet settled, positioned against the closest prior work — not the whole field. Editors and referees reward a contribution that is specific, honest, and load-bearing. Name your contribution type, then position only against the papers that threaten it.
| Contribution type | What "new" must mean | Positioning move |
|---|---|---|
| New answer | a magnitude prior work did not credibly estimate | show why prior designs failed to identify it |
| New design / source of variation | a cleaner identification of a known question | contrast your design's assumptions vs. the standard one |
| New setting / population | external validity that changes the lesson | argue why this setting is informative, not just available |
| New mechanism | decomposing a known effect into channels | show the prior literature left the channel ambiguous |
| New measurement | a quantity nobody could measure before | establish the measure's validity, not just its novelty |
Positioning craft
- The three-paper test. Name the three papers closest to yours and write one sentence each on what they establish and what they leave open. Your contribution lives in the "leave open" gaps.
- The honest delta. State the marginal addition in one sentence the closest author would agree with. If they would say "we already did that," the delta is too thin.
- Position by design, not by topic. "First paper on X in country Y" is weak; "first credibly identified estimate of X because design D removes confound C" is strong.
- Cite siblings correctly. Distinguish what AER, AEJ: Policy, and field journals have established so you are not re-claiming settled ground.
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 · 79 lines · 0 tokens per session scan A bba945cfac95
aeja-literature-positioning 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 1,192 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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