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-author-responsegit 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-author-response)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acl-author-response"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-author-response/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-author-response"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acl-author-response.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 | $0.00063 | $0.01455 |
| Opus 5.5 | $0.00025 | $0.00582 |
| Sonnet 5.5 | $0.00013 | $0.00291 |
| Haiku 4.5 | $0.00006 | $0.00145 |
Grade A, and why
acl-author-response 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACL Author Response
Use this when ARR reviews land. The response is written inside the review cycle, before the meta-review is finalized — its primary reader is the area chair who will summarize your paper for whatever conference you later commit to. Reopen aclrollingreview.org/reviewing for the current cycle's exact window and rules.
What the response can actually move
- ARR assigns at least three reviewers; after the author response there is a reviewer-discussion phase in which reviewers are expected to update reviews. A response that gives a reviewer a concrete reason to revise is worth more than one that argues.
- The meta-review travels with the paper to commitment. Senior area chairs at the venue read the meta-review first, so your real goal is to shape what the AC writes, not to win a debate transcript.
- Scores are not the only currency: a meta-review that says "the main weakness was resolved in discussion" can rescue a committed paper that scores alone would sink.
Triage order
- Factual errors in reviews (wrong dataset, misread table, missed section).
- Misunderstandings a two-sentence clarification can dissolve.
- Requests satisfiable from submitted material — appendix tables, checklist answers, the supplement archive.
- Requests needing new experiments: promise only what is genuinely small, or explicitly defer to a revision cycle (ARR is built for resubmission).
- Taste disagreements: answer once, briefly, without heat.
NLP-reviewer objection patterns
| Objection | Underlying worry | Response that works at ACL |
|---|---|---|
| "Only evaluated on English" | Overclaimed generality | Scope the claim explicitly; cite the multilingual appendix run if one exists |
| "Gains may be contamination" | Test data in pretraining | Point to the decontamination check or concede and describe the planned audit |
| "No error analysis" | Numbers without understanding | Quote the analysis section or summarize 2-3 systematic error classes from logged outputs |
| "Human eval lacks agreement stats" | Unreliable judgments | Report the IAA already computed; never invent a number in the response box |
| "Missing recent baseline X" | Stale comparison set | Distinguish X technically, or accept it as revision work with a stated plan |
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 · 126 lines · 63 tokens per session scan A ee78c90153e2
acl-author-response is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,320 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 1,455 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.
Other skills, from other repositories
alterlab-qiime2-amplicon
Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 distribution (2026.7; the "amplicon" distribution was renamed "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier…
research-manager
Use this when starting a new research project or managing a complex, multi-step research workflow.
alterlab-phylogenetics
Build phylogenetic trees end-to-end from raw sequences — MAFFT multiple sequence alignment, optional TrimAl trimming, IQ-TREE 3 maximum-likelihood inference with model selection and bootstraps, FastTree for large datasets, then visualize with ETE3 or FigTree. Use when reconstructing trees from sequences (FASTA) for…
alterlab-pydicom
Reads, writes, and manipulates DICOM (Digital Imaging and Communications in Medicine) medical imaging files with the pydicom Python library. Use when reading/writing/modifying DICOM data, extracting pixel data from CT, MRI, X-ray, or ultrasound images, anonymizing DICOM files, working with DICOM metadata and tags…
alterlab-pyhealth
Develops, tests, and validates clinical machine learning models with the PyHealth 2.x healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, length of stay, drug recommendation), medical coding systems (ICD, NDC, ATC, CCS), physiological signals…
alterlab-citation-verifier
Verifies that every bibliography entry actually exists by cross-checking Crossref, OpenAlex, Semantic Scholar, and arXiv (no API key required) plus doi.org DOI registration, fuzzy-matching title and authors (difflib ratio >= 0.70), flagging retractions recorded by Crossref (including Retraction Watch data) or…