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 aaai-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/aaai-artifact-evaluation)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aaai-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aaai-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/aaai-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aaai-artifact-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00047 | $0.00752 |
| Opus 5 | $0.00023 | $0.00376 |
| Sonnet 5 | $0.00009 | $0.00150 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
aaai-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 13d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AAAI Artifact Evaluation
Use this to prepare artifacts that reviewers can use to assess reproducibility. AAAI supplementary material is part of the submission record; after review starts, do not assume it can be updated.
Artifact package
- Provide a technical appendix for proofs, algorithms, assumptions, hyperparameters, and extended experiments.
- Provide code/data ZIPs that reproduce main tables or figures, with a short README, environment, commands, seeds, expected outputs, and runtime.
- Provide multimedia appendices only when they support the technical claim.
- Remove author names, usernames, paths, repository history, cloud buckets, API keys, and metadata.
- Avoid web pointers in the reviewed submission unless current rules explicitly allow them.
- Include licensing and access notes for datasets, models, and third-party code.
AAAI-specific discipline
- Treat the supplementary deadline as final.
- Verify ZIP integrity before submission; missing or corrupted files may not be fixable during rebuttal.
- Make the reproducibility checklist consistent with the artifact package.
- Prepare a post-acceptance public release path but keep review artifacts anonymous.
What an AAAI reviewer actually opens
AAAI does not run a separate badged artifact-evaluation committee the way some systems venues do; the same broad-AI reviewer who scores the paper also inspects whatever supplement you attach. That reviewer may be a planning, knowledge-representation, or constraint-satisfaction specialist rather than a deep-learning engineer, so the artifact has to be legible without insider tooling. Optimize for a reviewer who skims, not one who will spend an afternoon configuring a cluster.
| Reviewer action | Passes | Fails |
|---|---|---|
| Opens the ZIP | sane tree, top README | nested archives, 0-byte files |
| Reads appendix | maps to numbered claims | contradicts the paper |
| Tries one command | reproduces one headline number | needs private data or credentials |
| Scans for identity | nothing reveals authors | Git logs or home paths leak |
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.
- 13d ago First seen · 72 lines · 47 tokens per session scan A b183843e19b0
aaai-artifact-evaluation is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,097 stars, last pushed 16d ago), licensed MIT. It adds 47 tokens to every session and 752 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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