AISTATS Skills

A 12-skill depth pack for AISTATS submissions: topic fit, OpenReview submission checks, author discussion, camera-ready, artifacts, reproducibility, supplementary material, review process, writing style, related work, experiments, and workflow. Grounded in official AISTATS 2026 CFP, OpenReview group, PMLR proceedings pages, and Code of Conduct checked on 2026-06-01.

by @brycewang-stanford 12 skills

Install

npx skillmds@latest add plugin:brycewang-stanford/aistats-skills

One command, no setup. In Claude Code it lands as a managed plugin you can enable, disable or remove with /plugin. On any other agent it installs the 12 skills into that agent's own folder.

Or inside Claude Code

In the Plugins tab, add this marketplace:

skillmds/skillmd
/plugin install brycewang-stanford-aistats-skills@skillmd

Adds the marketplace once; later plugins need the second line only.

Or install by hand

Download the plugin and unzip its skills/ folder into your agent's skills directory. Works with every agent, no CLI needed.

Download plugin Paths per agent

Just the markdown? SKILL.md files only.

This plugin

skills: 12
authors: 1

Published by a registry member. Review its skills before installing.

Skills in this plugin

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Aistats Workflow · brycewang-stanford
Use when planning an AISTATS project timeline from venue fit through abstract submission, full-paper upload, supplementary material, review release, author-reviewer discussion, decision, camera-ready, PMLR publication, registration, presentation, and artifact release, with backward-planning offsets for theory-plus-experiments papers.

1k repo stars
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Aistats Submission · brycewang-stanford
Use when auditing an AISTATS submission for OpenReview readiness, abstract/full-paper deadlines, the 8-page submission body, double-blind anonymity, supplementary material, reproducibility checklist, dual-submission policy, reviewer volunteer requirements, desk-reject triggers, and final-week submission sequencing.

1k repo stars
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Aistats Experiments · brycewang-stanford
Use when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.

1k repo stars
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Aistats Camera Ready · brycewang-stanford
Use when preparing accepted AISTATS papers for PMLR camera-ready submission, covering the 9-page final body, two-column layout reflow, author de-anonymization, proceedings forms, metadata, final appendix handling, registration, in-person presentation obligations, and public artifact release.

1k repo stars
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Aistats Related Work · brycewang-stanford
Use when positioning an AISTATS submission against AI, machine-learning, statistics, and uncertainty literature, including arXiv preprints, workshop versions, concurrent submissions, prior conference versions, PMLR archival status, and the two-community citation coverage that AISTATS reviewers expect.

1k repo stars
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Aistats Supplementary · brycewang-stanford
Use when preparing AISTATS supplementary material, appendices, proof details, code/data archives, simulation scripts, additional tables, and anonymized artifacts under deadline, size, anonymity, and reviewer-discretion constraints, including how to split a theory-plus-experiments paper between body and supplement.

1k repo stars
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Aistats Writing Style · brycewang-stanford
Use when revising an AISTATS paper for concise AI-statistics framing, theorem-and-experiment clarity, 8-page two-column compression, double-blind wording, reproducibility clarity, statistically careful claims, and assumption-labeling discipline that survives statistician reviewers.

1k repo stars
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Aistats Review Process · brycewang-stanford
Use when explaining or planning around AISTATS peer review, OpenReview review release, author-reviewer discussion, reviewer volunteer expectations, reviewer confidentiality, decision criteria, meta-review dynamics, the statistician-heavy reviewer pool, and PMLR proceedings outcomes.

1k repo stars
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Aistats Author Response · brycewang-stanford
Use when drafting AISTATS author responses or author-reviewer discussion replies under OpenReview, covering text-only discussion, no-link guidance, no revised-paper upload, anonymity requirements, statistician-reviewer pushback patterns, and decision-focused clarification strategy for theory-plus-experiments papers.

1k repo stars
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Aistats Reproducibility · brycewang-stanford
Use when strengthening AISTATS reproducibility evidence, including the official reproducibility checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, code/data release statements, and checklist-to-claim consistency audits.

1k repo stars
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Aistats Topic Selection · brycewang-stanford
Use when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying the statistical primitive of the contribution, and sharpening the AI-statistics framing before writing begins.

1k repo stars
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Aistats Artifact Evaluation · brycewang-stanford
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.

1k repo stars