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 acl-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/acl-writing-style)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acl-writing-style"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-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/acl-writing-style"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-writing-style.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.01288 |
| Opus 5 | $0.00034 | $0.00644 |
| Sonnet 5 | $0.00014 | $0.00258 |
| Haiku 4.5 | $0.00007 | $0.00129 |
Grade A, and why
acl-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 13d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Writing Style
Use this on the manuscript itself. ACL reviewers are NLP specialists who read for whether the paper understands language as well as models; the style that survives them is concrete, example-grounded, and precisely scoped.
First-page contract
- Open with the task or linguistic phenomenon, not the model family: what goes in, what comes out, why it is hard, and for whom.
- State the contribution as a typed claim by paragraph two: new method, new resource, new analysis, or new finding — ACL reviews are calibrated per type.
- Give one real example (input, desired output, failure of the status quo) on page one; abstract problem statements without an example read as vague at this venue.
- Say what languages the paper covers in the abstract if the answer is not "English only" — and if it is, say that too.
Claim scoping in the LLM era
| Reflex phrasing | ACL-safe phrasing |
|---|---|
| "LLMs cannot do X" | "The five models tested fail X under these prompts" |
| "Our method understands Y" | "Improves the Y benchmark by n points; error classes A, B shrink" |
| "Works across languages" | "Evaluated on de/hi/sw/zh/ar; typological coverage discussed in §7" |
| "Significantly better" | Reserve for tested significance; give the test and p-value or interval |
| "State-of-the-art" | Scope to the exact setting, model scale, and date checked |
Reviewers increasingly ask whether a result is a property of the task, the model snapshot, or the prompt; write so each claim names which.
Examples and error analysis as prose
- Every qualitative example must be attached to a number: how often the illustrated behavior occurs, in which slice, under which condition. Cherry-picked generations presented as evidence is a named reject pattern.
- Use interlinear glosses or transliteration conventions correctly for non-English examples; sloppy linguistics costs credibility with exactly the reviewers who like the paper's topic.
- Name error categories functionally ("negation-scope errors") rather than narratively ("the model gets confused").
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.
- 13d ago First seen · 121 lines · 69 tokens per session scan A c389e92adefa
acl-writing-style is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,097 stars, last pushed 16d ago), licensed MIT. It adds 69 tokens to every session and 1,288 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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