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 aeri-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/aeri-topic-selection)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aeri-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeri-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/aeri-topic-selection"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aeri-topic-selection.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 | $0.00000 | $0.01380 |
| Opus 5.5 | $0.00000 | $0.00552 |
| Sonnet 5.5 | $0.00000 | $0.00276 |
| Haiku 4.5 | $0.00000 | $0.00138 |
Grade A, and why
aeri-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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Topic Selection — Single-Insight Fit (aeri-topic-selection)
When to trigger
- A project has a strong main result but also two or three secondary results
- Unsure whether to send to AER: Insights, AER, or an AEJ field journal
- The draft is creeping past the word cap because there is more than one idea
- A result feels surprising and clean but you are unsure it is "important enough"
The AER: Insights bar: one idea, AER-level importance, short
AER: Insights publishes short, self-contained papers built around a single important, well-executed idea. The decision is not "is this good?" but "is there exactly ONE crisp, surprising, well-identified insight that can stand alone in a short paper at AER-level importance?" Three filters:
- Single-insight test. State the paper in one sentence with one headline object (one estimate, one theorem, one fact, one method-changes-the-answer result). If you need "and" to describe the contribution, you likely have an AER paper or two AER: Insights papers, not one.
- Importance test. The single insight must clear AER-level general interest — a non-specialist economist should care. AER: Insights is short, not minor; a small or incremental result is a reject regardless of length.
- Self-containment test. The whole argument — setup, identification, headline result, the one or two checks that matter — must fit in ≤7,000 words (minus 200 per exhibit, ≤5 exhibits) with everything else in the online appendix. If the core argument cannot be made short, it is not an AER: Insights paper.
Where it goes if it is NOT AER: Insights
| Symptom | Better venue |
|---|---|
| Multiple inter-dependent results that need each other | AER (long, multi-result flagship) |
| One result but field-specific, not general interest | AEJ: Applied / Micro / Macro / Policy |
| Incremental / a robustness note on existing work | a field journal or a comment |
| Pure methods with no substantive payoff in a short paper | a methods/econometrics journal |
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 · 90 lines · 0 tokens per session scan A 946ae6ce2060
aeri-topic-selection is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,380 tokens. 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…