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-related-workgit 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-related-work)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ase-related-work"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-related-work/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-related-work"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-related-work.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 | $0.00057 | $0.00984 |
| Opus 5.5 | $0.00023 | $0.00394 |
| Sonnet 5.5 | $0.00011 | $0.00197 |
| Haiku 4.5 | $0.00006 | $0.00098 |
Grade A, and why
ase-related-work 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASE Related Work
Position the paper against prior automation, not against a topic. At ASE the question a reviewer holds is "what does this tool/technique do that the existing automated approaches do not?" — so related work is a delta argument, and the strongest deltas are stated against named, runnable prior tools you can (and often must) compare against empirically.
Cover the ASE literature lanes
An automated-SE paper usually sits at the intersection of several lanes; name your position in each that applies:
- Program analysis — static/dynamic analysis, symbolic/concolic execution, abstract interpretation, invariant inference.
- Test generation and testing — search-based, specification-based, fuzzing, regression and flaky-test handling.
- Fault localization, debugging, and repair — spectrum-based localization, automated program repair, patch synthesis.
- Synthesis and recommendation — program/code synthesis, API recommendation, refactoring automation.
- Comprehension and documentation — summarization, code search, clone and change analysis.
- Learning-based / AI4SE — learned code representations, models applied to SE tasks, LLM-based automation — and SE4AI, applying SE automation to ML systems.
Delta-first positioning
Write each related-work cluster as: prior automation X does A; it cannot do B because C; we do B. Then, where B is comparable, compare against X empirically — ASE reviewers expect a head-to-head against the closest runnable baseline, not just a prose distinction.
[Cluster] spectrum-based fault localization
Prior: ranks statements by suspiciousness from pass/fail spectra.
Gap: assumes a fixed test suite; degrades when tests are flaky.
Delta: our technique estimates and corrects for flakiness before ranking.
Evidence of delta: head-to-head on the same faulty subjects, §5.2, Table 3.
Fair head-to-head framing (the ASE credibility test)
- Compare against the strongest, most recent automated baseline you can run, at an equal, documented budget (time, iterations, tuning). An under-configured baseline is the classic soundness objection and a Revision criterion waiting to happen.
- If a baseline tool cannot run on your subjects, say why explicitly and choose the nearest viable alternative — do not quietly omit the obvious competitor.
- Distinguish reimplementation from the original tool, and note version/commit — reviewers who built the baseline will check.
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 · 87 lines · 57 tokens per session scan A 6c0162cb8879
ase-related-work is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It adds 57 tokens to every session and 984 once invoked, about $0.0002 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…