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 ase-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/ase-writing-style)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ase-writing-style"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-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/ase-writing-style"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-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 | $0.00073 | $0.01343 |
| Opus 5.5 | $0.00029 | $0.00537 |
| Sonnet 5.5 | $0.00015 | $0.00269 |
| Haiku 4.5 | $0.00007 | $0.00134 |
Grade A, and why
ase-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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASE Writing Style
Write the paper so an automated-SE reviewer sees, on the first page, what task you automate, how
you automate it, and that it runs on real subjects. ASE rewards a clearly stated automation with
evidence proportional to the claim — not a systems win, not a leaderboard, and not a broad finding
that would read better at FSE. The worked example in resources/worked-examples/01-introduction.md
shows the arc before → after.
The ASE first-page arc
Lead with the automation, in this order:
- The automatable task — a software-engineering task the reader recognizes (detect X, generate Y, repair Z, comprehend W), named precisely enough that a reviewer knows what "success" is.
- Why current automation (or manual practice) is inadequate — what existing tools do not do, stated as a gap, not a literature tour.
- The contribution as an automation design — the technique (analysis, generator, synthesizer, repair, learned model) and, ideally, the tool that embodies it.
- Evidence on real subjects — real systems, credible tool baselines, and the metric that matches the task (not a proxy).
- What it automates for practice + threats posture — the payoff, with the central threat named where it lives, not deferred to a closing paragraph.
Put the automation and the first evidence within the first three pages; the early-rejection gate means a weak opening can end the process before rebuttal.
State the automated task precisely
- Name the input, the output, and the success criterion of the automation. "We improve code quality" is not a task; "given a flaky test, synthesize a patch that makes it deterministic without weakening its assertions" is.
- Say what the tool takes as input in practice (source? bytecode? traces? a repository?) and what it produces — reviewers map this straight to feasibility.
Keep the model-swap test in view
If a learned component is involved, write so the automation design is the contribution, not the
model. Report an ablation that isolates the learned part from the analysis/oracle, and phrase
claims so the software-engineering lesson survives a model swap. A paper whose lesson evaporates
when the model changes reads as an ML re-route (see ase-topic-selection).
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 · 104 lines · 73 tokens per session scan A 18cc66ec71a2
ase-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 adds 73 tokens to every session and 1,343 once invoked, about $0.0003 per session on Opus 5.5. 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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