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-related-workgit 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-related-work)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acl-related-work"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-related-work/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-related-work"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-related-work.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 | $0.00065 | $0.01319 |
| Opus 5 | $0.00032 | $0.00660 |
| Sonnet 5 | $0.00013 | $0.00264 |
| Haiku 4.5 | $0.00006 | $0.00132 |
Grade A, and why
acl-related-work 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 12d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Related Work
Use this to audit positioning and citation hygiene. NLP moves fast enough that related-work sections age between ARR cycles; the section must prove the authors know the field as of this cycle, not as of the project's start.
The Anthology-first rule
- When a paper exists in the ACL Anthology (or another archival venue), cite that version, not its arXiv preprint — Anthology entries are the community's citation of record and carry stable BibTeX.
- The Anthology is fully open access (post-2016 materials under CC BY 4.0), so "couldn't access the published version" is never a defense here.
- For findings-tier work, cite it as Findings of ACL/EMNLP — it is archival and reviewers treat miscrediting it as sloppiness.
Freshness pressure unique to this field
- Expect at least one reviewer to name a baseline or dataset from the last two conference rounds. Sweep ACL, EMNLP, NAACL, EACL, TACL, and CL for the nearest neighbors published since your project began.
- LLM-era concurrency is the norm: assume 2-4 similar preprints exist. Cite the ones findable at submission, contrast on technique rather than date, and avoid priority wars that reviewers cannot adjudicate.
- If a newer model release makes your comparison set look dated mid-cycle, address it in the author response and plan the upgrade for camera-ready or resubmission — silence reads as unawareness.
Positioning moves that work at ACL
| Contribution type | The contrast reviewers want spelled out |
|---|---|
| Method | Nearest prior method + the mechanism-level difference, not just scores |
| Resource | Existing datasets' coverage gaps: languages, domains, phenomena, license |
| Analysis / evaluation | What prior evaluations measured incorrectly or could not see |
| Metric | Failure cases of the incumbent metric, with correlation evidence |
| Theme-track paper | How the work answers the year's theme question (ACL 2026: explainability) |
One paragraph of typed contrast per lane beats an undifferentiated citation inventory; ACL's compressed format cannot afford taxonomy for its own sake.
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.
- 12d ago First seen · 120 lines · 65 tokens per session scan A c42d9d251a70
acl-related-work is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,097 stars, last pushed 16d ago), licensed MIT. It adds 65 tokens to every session and 1,319 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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