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-reproducibilitygit 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-reproducibility)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ase-reproducibility"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-reproducibility/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-reproducibility"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-reproducibility.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 | $0.00069 | $0.00947 |
| Opus 5.5 | $0.00028 | $0.00379 |
| Sonnet 5.5 | $0.00014 | $0.00189 |
| Haiku 4.5 | $0.00007 | $0.00095 |
Grade A, and why
ase-reproducibility 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASE Reproducibility
Build the reproducibility story at data-collection time, not at submission. ASE requires a mandatory Data Availability Statement in the paper and expects an anonymized, runnable artifact at review time; automated-SE artifacts are usually tools, so "runnable" means a reviewer can actually execute the automation on stated subjects. What is not pinned when you collect it cannot be reconstructed later.
The mandatory Data Availability Statement
- Required, placed after the Conclusions and inside the 10-page limit (it is not free appendix space).
- State what exists — the tool, the dataset, the subject systems, the scripts, the logs — and where it will live after acceptance (an archival DOI target).
- Provide an anonymized link or upload now; "available upon request" reads as a scored weakness, not a neutral placeholder.
- Match the statement to what the archive actually contains — an overclaiming statement is worse than a modest, honest one.
Anonymized-but-runnable tools
- Re-host the tool and dataset behind an anonymizing service; strip repository owner, commit
author metadata, and any path revealing your identity (
/home/<you>/, institutional URLs). - Include a minimal run path: exact commands, expected inputs, and a small sample so a reviewer can execute the automation without your machine.
- Pin the environment: dependencies with versions, a container or lockfile, and the exact tool commit — automated-SE tools rot fast against moving toolchains.
Provenance pinning (do this at collection time)
For the tool:
- Exact commit SHA, build instructions, dependency versions, and configuration/flags used in the experiments (including seeds for randomized components).
For subject systems and datasets:
- Names, versions, and SHAs of every subject; the corpus extraction date; query/filter criteria; and any manual labeling protocol with inter-rater agreement.
- A regeneration script and a versioned snapshot — live scraping re-samples a moving target.
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 · 80 lines · 69 tokens per session scan A c42061eed39b
ase-reproducibility is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It adds 69 tokens to every session and 947 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.
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