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-topic-selectiongit 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-topic-selection)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acl-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-topic-selection/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-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-topic-selection.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 | $0.00073 | $0.01390 |
| Opus 5.5 | $0.00029 | $0.00556 |
| Sonnet 5.5 | $0.00015 | $0.00278 |
| Haiku 4.5 | $0.00007 | $0.00139 |
Grade A, and why
acl-topic-selection 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Topic Selection
Use this before the first draft. ACL is the flagship of the *ACL family: broadest scope across computational linguistics and NLP, the most competitive main-program bar, and — under ACL Rolling Review — a venue choice you finalize at commitment time, which gives topic strategy an unusual second chance.
What ACL rewards
- A contribution about language: modeling it, measuring it, resourcing it, or explaining how systems process it — with the linguistic question visible, not incidental.
- Typed contributions reviewers can classify fast: method, resource, evaluation/metric, analysis, theory, or position. Papers that are half method and half unvalidated resource read as neither.
- Evidence proportional to breadth (see
acl-experiments) and an error analysis that says something about language, not just scores. - Work engaging the current field conversation — for ACL 2026, the special theme was explainability of NLP models, with a dedicated Thematic Paper Award; each edition names its own theme.
Family routing
| Signal | Better home |
|---|---|
| Core NLP contribution, broad audience, strongest possible reviews wanted | ACL (or whichever *ACL your ARR package is eligible to commit to) |
| Empirical, engineering-forward NLP; dense experimental papers | EMNLP — historically the empirical sibling, same ARR pipeline |
| Regional relevance, or timing fits its cycle windows | NAACL / EACL / AACL |
| Needs >9 pages, revision-based journal reviewing, no conference clock | TACL (journal, also Anthology-published) |
| Survey-scale or theoretical linguistics depth | Computational Linguistics (journal) |
| LLM-centric work thin on language questions | COLM or an ML venue (NeurIPS/ICML/ICLR) |
| Deployed-system lessons, product constraints | ACL industry track — separate CFP and deadlines |
| Early-stage, student-led | ACL Student Research Workshop |
Because commitment is decoupled, "ACL vs EMNLP" is often not a submission-time decision: submit to ARR when ready, then commit to the conference whose window and bar the finished package fits.
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 · 122 lines · 73 tokens per session scan A c1d2ebadffb1
acl-topic-selection is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 1,390 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 2026-08-30.
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