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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

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awesome-journal-skills
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$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill aistats-writing-style --agent claude-code

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How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/aistats-writing-style

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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

SKILL.md

aistats-writing-style.SKILL.md
name: aistats-writing-style
description: 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.

AISTATS Writing Style

Use this when revising the main paper. AISTATS papers need compact statements of why a statistical or ML contribution matters and enough detail for technical validation.

Revision rules

  • Put the AI/statistics contribution in the first page: problem, gap, method or theorem,

evidence, and why existing methods are insufficient.

  • Make assumptions explicit. AISTATS reviewers are sensitive to hidden distributional,

asymptotic, optimization, or data-generating assumptions.

  • Pair every major claim with either proof structure, simulation design, empirical table, or

reproducibility detail.

  • Use the 8-page body for core logic; move long derivations, extra simulations, and extended

ablations to the appendix without making the main paper unintelligible.

  • Avoid overclaiming empirical wins when differences are small or variance is unreported.
  • Maintain double-blind style in self-citations, prior code references, acknowledgements,

funding, and artifact descriptions.

Theorem-presentation discipline

  • Number assumptions and cite them by label inside every theorem statement; AISTATS readers

audit assumption flows the way software reviewers audit imports.

  • Give each major theorem a proof sketch in the body; the appendix proof supports the sketch

but never replaces it.

  • Keep problem-dependent constants visible or explicitly deferred; silently absorbing

dimension or condition numbers into O-notation is a standing statistician complaint.

  • Define notation once in one location — two-column pages punish redundant redefinition.
  • Phrase remarks after theorems as interpretation, not as extra unproved claims; AISTATS

reviewers treat every declarative sentence near a theorem as something they may verify.

  • When a result is conjectured or only empirically supported, label it so; mixing proved and

observed statements in one paragraph is a credibility leak at this venue.

Sentence-level rewrites

| Draft pattern | AISTATS-safe rewrite | |---|---| | "Our method significantly outperforms..." | "reduces mean error by X (SE Y) over Z seeded runs" | | "Under mild conditions..." | "Under Assumptions 1-3 (boundedness, ...)" | | "It is easy to see that..." | "By Lemma 2 and the triangle inequality..." | | "Achieves state-of-the-art..." | Claim scoped to the regimes actually tested |

Vignette: compressing into eight two-column pages

A draft with three theorems, six figures, and a sprawling related-work section: keep all theorem statements, one sketch each, and the two decision-critical figures (the rate plot and the coverage plot); compress related work into contribution contrasts; move secondary lemmas and four figures to the appendix with explicit forward references. The test of a good cut: a reviewer should reconstruct the whole argument without ever opening the supplement.

Output format

[Writing diagnosis] clear / under-justified / overclaimed / overloaded
[First-page fix] <new framing>
[Claim discipline] <claim -> proof/experiment/limitation>
[Compression cuts] <move/delete/merge>
[Anonymity edits] <phrases to rewrite>
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