本文へ移動
cccskills
無料GitHub で公開

aeri-writing-style

Use when revising an American Economic Review: Insights (AER: Insights) short-format manuscript to lead with the result, fit the hard word cap (7,000 words minus 200 per exhibit), and write the ≤100-word abstract. Enforces the length budget and the lead-with-the-result arc; it does not change the analysis.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md4.3 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Writing Style — Lead With the Result, Fit the Cap (aeri-writing-style)

When to trigger

  • The intro buries the headline result under setup, literature, or methods
  • The draft is over the word cap and needs cutting, not just polishing
  • The abstract exceeds 100 words or does not state the result
  • Late-stage polish once identification, robustness, and exhibits have settled

The two hard constraints (检索于 2026-06;以官网为准)

  • Word cap: main text ≤7,000 words with no exhibits; each exhibit subtracts 200 words; ≤5 exhibits (so ≥6,000 at the cap). The count includes body, footnotes, endnotes, and in-paper appendices; it excludes title, authors, abstract, acknowledgement footnote, references, exhibits, and the Supplemental Appendix. Over the cap = returned unreviewed.
  • Abstract: ≤100 words. It must state the result, not promise it.

Treat the cap as a design constraint from the first draft, not a final trim. Every sentence competes with the insight for space.

The AER: Insights arc (lead with the result)

Unlike a long paper that builds to its result, an AER: Insights paper states the answer in the first paragraph:

  1. Sentence 1–2: the question and the headline result with its magnitude and uncertainty (estimate + SE / confidence set), no asterisks.
  2. Sentence 3–4: why it matters / the prior it overturns (the surprise).
  3. One short paragraph: how it is identified (the design or the model's key restriction), in plain terms.
  4. One sentence: the single most important robustness/scope caveat, with a pointer to the appendix.
  5. Brief roadmap or none — the paper is short enough that heavy signposting wastes words.

The body then delivers exactly that arc: setup (tight), identification, result, the one or two checks, a short discussion. No second result, no survey, no extended derivations in-text.

Cutting to the cap (in priority order)

  1. Cut secondary results to the Supplemental Appendix (aeri-robustness).
  2. Compress the literature to one or two paragraphs (aeri-literature-positioning).
  3. Move proofs/derivations and estimation detail to the appendix (aeri-theory-model).
  4. Delete throat-clearing ("It is well known that…", over-signposted roadmaps).
  5. Merge exhibits to reclaim 200 words each (aeri-tables-figures).
  6. Tighten prose last — shorter sentences, fewer hedges, active voice.

Writing the ≤100-word abstract

  • State the result with its magnitude, the setting, and the identification in one breath.
  • No literature, no "we examine"; say what you found.
  • Verify the count; 100 words is a hard ceiling.

Checklist

  • First paragraph states the headline result with magnitude + SE/CI (no asterisks)
  • Main text under 7,000 − 200×(#exhibits) words (counted per AEA scope rules)
  • Abstract ≤100 words and states the result
  • One idea only in-text; secondary material in the Supplemental Appendix
  • Literature compressed to ≤2 paragraphs; no in-text survey
  • No significance asterisks/boldface anywhere
  • Roadmap minimal or absent; throat-clearing removed

Anti-patterns

  • A long build-up that reveals the result only in Section 4
  • Trimming prose while leaving a second result in-text (cut the result first)
  • An abstract over 100 words, or one that promises rather than reports
  • Counting the word budget only at the end and discovering you are 2,000 over
  • AER-length signposting in a short paper

Output format

【First paragraph】states result + magnitude + SE/CI? [Y/N]
【Word count】main text = __ ; cap = 7000 − 200×__ = __ ; under? [Y/N]
【Abstract】__ words (≤100?) and states the result? [Y/N]
【Cut list to hit cap】<secondary results / lit / proofs / exhibits merged>
【No-asterisk check】[Y/N]
【Next step】aeri-replication-package

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance artifact releases without violating double-blind or immutable-supplement rules.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when drafting an AAAI author response (rebuttal) under the single short character-limited author-feedback window, the no-URL rule, no-new-results guidance, AI-generated-review handling, and the AAAI two-phase review process where Phase-2 papers receive one feedback round before SPC/AC discussion.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.

日本語の概要は準備中です。原文の説明を表示しています。

brycewang-stanford/Awesome-Journal-Skills1,2482026年9月27日 更新

brycewang-stanford のスキルをすべて見る

このスキルの問題を報告する