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 acmmm-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/acmmm-author-response)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/acmmm-author-response"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-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/acmmm-author-response"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/acmmm-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 | $0.00068 | $0.00982 |
| Opus 5.5 | $0.00027 | $0.00393 |
| Sonnet 5.5 | $0.00014 | $0.00196 |
| Haiku 4.5 | $0.00007 | $0.00098 |
Grade A, and why
acmmm-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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ACM MM Author Response
Use this to write the ACM Multimedia rebuttal. It is optional and anonymous, it must not add new external links, and its real audience is the area chair who will weigh it against the reviews in discussion.
Triage before writing
Sort every review point into one of three buckets:
| Bucket | Example | Response |
|---|---|---|
| Factual error | "No ablation of the fusion" (there is one, in §4.3) | Point to it precisely; this is your strongest move |
| Fixable gap | "Missing a late-fusion baseline" | Add the small result if you can run it in time |
| Judgment call | "The delta is incremental" | Reframe the significance briefly, or concede narrowly |
Lead with the factual corrections — they are the points an AC can verify and act on fastest.
Structure for the AC
Write for a reader who is skimming several rebuttals. Make it navigable:
[R1, R2 shared concern: fusion not shown to matter]
-> Table A (new): leave-one-modality-out; audio-text term = +X of the +Y total gain.
[R1 factual: "no user study"]
-> §5 reports a user study, N raters, agreement kappa; see supplement S3.
[R3 judgment: "incremental over late fusion"]
-> Our gain over the late-fusion baseline is in Table 1; the mechanism (Table A) is the novelty.
[R2 request: clarify synchronization assumption]
-> Conceded; we will state it explicitly in §3 (one sentence).
Group shared concerns once; do not repeat the same answer per reviewer.
Small confirmatory results
- Add only results you can actually produce before the deadline — a leave-one-modality-out ablation, one requested baseline, or a small user-study extension.
- Describe new numbers in text/tables inside the rebuttal; you cannot add a link to an external file, and you cannot add pages to the paper.
- Do not promise experiments you cannot show; an AC discounts unbacked promises.
Calibrated concessions
Conceding a fair minor point raises credibility on the points you contest. State clearly what you will change (a sentence, a clarified assumption, a moved figure) and why the core claim survives it.
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 · 96 lines · 68 tokens per session scan A b519ba9816c5
acmmm-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 68 tokens to every session and 982 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…