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 ase-camera-readygit 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/ase-camera-ready)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ase-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-camera-ready/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/ase-camera-ready"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-camera-ready.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00074 | $0.01022 |
| Opus 5.5 | $0.00030 | $0.00409 |
| Sonnet 5.5 | $0.00015 | $0.00204 |
| Haiku 4.5 | $0.00007 | $0.00102 |
Grade A, and why
ase-camera-ready 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASE Camera-Ready
Deliver the final version after an Accept or a met Revision. ASE camera-ready has three jobs that trip authors up: de-anonymize systematically, spend the extra content page well, and pass dual production checks — because the paper is indexed in both IEEE Xplore and the ACM Digital Library, the metadata and template must satisfy the sponsoring publisher's pipeline. Every requirement below is a one-cycle snapshot; reopen the current camera-ready instructions first.
Systematic de-anonymization
Reverse every anonymization introduced for double-anonymous review — and only those:
[Authors] real names, affiliations, ORCID, and corresponding-author info restored.
[Tool] real tool name restored consistently throughout (it was anonymized at submission).
[Repository] Data Availability link points to the real, permanent archive (not the anon service).
[Self-cites] third-person self-citations relaxed to normal form where appropriate.
[Acks] funding, grants, and collaborators added (absent from the anonymized submission).
[Metadata] PDF author/producer fields set correctly; no residual "Anonymous".
Do a final pdfinfo / pdftotext pass to confirm no leftover "Anonymous" and no new leak (a
funding line that should not be there, a wrong affiliation).
Spend the extra content page
Accepted ASE papers are typically allowed one additional content page for revisions. Use it to incorporate review feedback — the new baseline, the requested ablation, the clarified construct, the strengthened threats — not to pad. If you received a Revision, the extra page is where the criteria you met become visible in the final paper.
Finalize the Data Availability Statement
- The statement moves from an anonymized link to the permanent, DOI-archived location.
- Confirm the archive is public and the DOI resolves; deposit in Zenodo / figshare / Software Heritage before submitting camera-ready if you have not already.
- Keep it after the Conclusions and within the (now +1) page budget.
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 · 85 lines · 74 tokens per session scan A fcb09acd2442
ase-camera-ready is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It adds 74 tokens to every session and 1,022 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 2026-09-15.
Other skills, from other repositories
research-manager
Use this when starting a new research project or managing a complex, multi-step research workflow.
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…
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…
alterlab-timesfm
Forecasts time series zero-shot with Google's TimesFM foundation models — TimesFM 2.5 (200M, Apache-2.0 weights; ForecastConfig API, XReg covariates) and TimesFM 3.0 (330M, multivariate with native past/future covariates; non-commercial weights) — producing point forecasts and quantile prediction intervals from…
alterlab-anndata
Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the…