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 aistats-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/aistats-artifact-evaluation)<a href="https://agentmods.dev/skills/brycewang-stanford/awesome-journal-skills/aistats-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aistats-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/aistats-artifact-evaluation"><img src="https://agentmods.dev/badge/skills/brycewang-stanford/awesome-journal-skills/aistats-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 | $0.00070 | $0.00732 |
| Opus 5.5 | $0.00028 | $0.00293 |
| Sonnet 5.5 | $0.00014 | $0.00146 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
aistats-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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AISTATS Artifact Evaluation
Use this for evidence packaging around AISTATS. The venue centers on artificial intelligence, statistics, and machine learning, so artifacts should make statistical and computational claims inspectable.
Artifact plan
- Decide what evidence reviewers need: proof details, derivations, simulation scripts, benchmark code, datasets, preprocessing, hyperparameter sweeps, random seeds, logs, or qualitative examples.
- Keep decision-critical evidence in the main paper or appendix; optional run files can live in supplementary material.
- Anonymize repository history, paths, notebook metadata, license headers, organization names, cluster paths, grants, and commit authors.
- Include a minimal reproduction map: environment, dependencies, hardware, commands, expected outputs, runtime, seeds, and known nondeterminism.
- For restricted data, give enough provenance and processing detail for credible reproduction without violating data-use terms.
- After acceptance, replace anonymous archives with public, licensed, citable artifacts when feasible.
What AISTATS evidence reviewers open first
| Claim type | First artifact inspected | Common failure caught |
|---|---|---|
| Convergence rate or regret bound | Proof appendix and constants | Condition used in the proof but missing from the theorem statement |
| Monte Carlo simulation | Seeded simulation script | Plots cannot be regenerated because seeds and replication counts are absent |
| Benchmark comparison | Training and evaluation configs | Baseline tuning budget undocumented |
| Bayesian or MCMC method | Sampler diagnostics and chain logs | No convergence statistics or trace evidence anywhere |
Because AISTATS reviewers are often statisticians, they will rerun a small simulation far more readily than they will retrain a deep model, so make synthetic studies turnkey before polishing anything else.
Worked vignette: packaging a Monte Carlo study
A hypothetical submission proposes a doubly robust treatment-effect estimator with a root-n normality guarantee, validated on synthetic causal data plus two real benchmarks.
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 · 71 lines · 70 tokens per session scan A 05c4e47fa452
aistats-artifact-evaluation is a skill published in the GitHub repository brycewang-stanford/Awesome-Journal-Skills (1,259 stars, last pushed 19d ago), licensed MIT. It adds 70 tokens to every session and 732 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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