aistats-artifact-evaluation
Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge. Covers what statistically minded AISTATS reviewers inspect first and how to make Monte Carlo studies turnkey.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-artifact-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 | |---|---
- Artifact plan
- What AISTATS evidence reviewers open first
- Worked vignette: packaging a Monte Carlo study
- Calibration anchors
- Output format
What does the aistats-artifact-evaluation skill do?
Use when packaging AISTATS code, data, proofs, simulation scripts, notebooks, random seeds, and logs as anonymous supplementary evidence or public post-acceptance artifacts, even when there is no separate artifact badge. Covers what statistically minded AISTATS reviewers inspect first and how to make Monte Carlo studies turnkey.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-artifact-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.