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-artifact-evaluationgit 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-artifact-evaluation)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/ase-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-artifact-evaluation/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-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/ase-artifact-evaluation.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 | $0.00072 | $0.00958 |
| Opus 5.5 | $0.00029 | $0.00383 |
| Sonnet 5.5 | $0.00014 | $0.00192 |
| Haiku 4.5 | $0.00007 | $0.00096 |
Grade A, and why
ase-artifact-evaluation 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ASE Artifact Evaluation
Convert the accepted paper's package into badges. ASE runs an Artifact Evaluation track offering the Artifacts Available and Artifacts Reusable badges (ACM scheme). Because ASE proceedings are indexed in both IEEE Xplore and the ACM Digital Library, an earned badge appears on the paper's front page in both. Evaluation happens on the track's own deadline, separate from the research-track notification — stage the package before then.
The two badges (verify the current call)
- Artifacts Available — the artifact is placed in a publicly accessible archival repository with a DOI (Zenodo, figshare, Software Heritage, or an institutional/ACM repository). A personal GitHub link alone is not archival; mint a DOI.
- Artifacts Reusable — the artifact significantly exceeds minimal functionality: it is carefully documented and well-structured so a third party can reuse the tool, not merely reproduce your tables. This is the higher bar and where automated-SE tools usually need the most work.
- Whether Functional and Results Reproduced badges are also offered at a given edition is 待核实 — confirm on the current Artifact Evaluation call.
From the submission artifact to the badge artifact
The review-time (anonymized) artifact and the badge artifact are the same package matured. After
acceptance you can de-anonymize it, but the substance should already be there if you followed
ase-reproducibility.
[De-anonymize] restore the real tool name, authors, repository, license.
[Archive] deposit in a DOI-issuing archive; the DOI is what "Available" certifies.
[Document] README with exact run path, expected outputs, and a small worked example.
[Environment] container/lockfile pinning deps + the exact tool commit; note hardware needs.
[Reuse story] show how to run the tool on a NEW input, not just replay your experiments.
Reusable is about strangers, not your tables
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 · 81 lines · 72 tokens per session scan A 72225aabb395
ase-artifact-evaluation is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,289 stars, last pushed 26d ago), licensed MIT. It adds 72 tokens to every session and 958 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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