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 aeri-writing-stylegit 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/aeri-writing-style)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeri-writing-style"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeri-writing-style/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/aeri-writing-style"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeri-writing-style.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 | $0.01091 |
| Opus 5.5 | $0.00000 | $0.00436 |
| Sonnet 5.5 | $0.00000 | $0.00218 |
| Haiku 4.5 | $0.00000 | $0.00109 |
Grade A, and why
aeri-writing-style 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Style — Lead With the Result, Fit the Cap (aeri-writing-style)
When to trigger
- The intro buries the headline result under setup, literature, or methods
- The draft is over the word cap and needs cutting, not just polishing
- The abstract exceeds 100 words or does not state the result
- Late-stage polish once identification, robustness, and exhibits have settled
The two hard constraints (检索于 2026-06；以官网为准)
- Word cap: main text ≤7,000 words with no exhibits; each exhibit subtracts 200 words; ≤5 exhibits (so ≥6,000 at the cap). The count includes body, footnotes, endnotes, and in-paper appendices; it excludes title, authors, abstract, acknowledgement footnote, references, exhibits, and the Supplemental Appendix. Over the cap = returned unreviewed.
- Abstract: ≤100 words. It must state the result, not promise it.
Treat the cap as a design constraint from the first draft, not a final trim. Every sentence competes with the insight for space.
The AER: Insights arc (lead with the result)
Unlike a long paper that builds to its result, an AER: Insights paper states the answer in the first paragraph:
- Sentence 1–2: the question and the headline result with its magnitude and uncertainty (estimate + SE / confidence set), no asterisks.
- Sentence 3–4: why it matters / the prior it overturns (the surprise).
- One short paragraph: how it is identified (the design or the model's key restriction), in plain terms.
- One sentence: the single most important robustness/scope caveat, with a pointer to the appendix.
- Brief roadmap or none — the paper is short enough that heavy signposting wastes words.
The body then delivers exactly that arc: setup (tight), identification, result, the one or two checks, a short discussion. No second result, no survey, no extended derivations in-text.
Cutting to the cap (in priority order)
- Cut secondary results to the Supplemental Appendix (
aeri-robustness). - Compress the literature to one or two paragraphs (
aeri-literature-positioning). - Move proofs/derivations and estimation detail to the appendix (
aeri-theory-model). - Delete throat-clearing ("It is well known that…", over-signposted roadmaps).
- Merge exhibits to reclaim 200 words each (
aeri-tables-figures). - Tighten prose last — shorter sentences, fewer hedges, active voice.
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 · 77 lines · 0 tokens per session scan A a66b9e449e03
aeri-writing-style is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,091 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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