본문으로 건너뛰기
cccskills
무료GitHub에 공개

aeri-topic-selection

Use when deciding whether a project is the ONE crisp, important, well-identified insight that an American Economic Review: Insights (AER: Insights) short-format manuscript requires, rather than a multi-result paper for AER or a field paper for the AEJ family. Tests single-insight fit; it does not run the analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Topic Selection — Single-Insight Fit (aeri-topic-selection)

When to trigger

  • A project has a strong main result but also two or three secondary results
  • Unsure whether to send to AER: Insights, AER, or an AEJ field journal
  • The draft is creeping past the word cap because there is more than one idea
  • A result feels surprising and clean but you are unsure it is "important enough"

The AER: Insights bar: one idea, AER-level importance, short

AER: Insights publishes short, self-contained papers built around a single important, well-executed idea. The decision is not "is this good?" but "is there exactly ONE crisp, surprising, well-identified insight that can stand alone in a short paper at AER-level importance?" Three filters:

  1. Single-insight test. State the paper in one sentence with one headline object (one estimate, one theorem, one fact, one method-changes-the-answer result). If you need "and" to describe the contribution, you likely have an AER paper or two AER: Insights papers, not one.
  2. Importance test. The single insight must clear AER-level general interest — a non-specialist economist should care. AER: Insights is short, not minor; a small or incremental result is a reject regardless of length.
  3. Self-containment test. The whole argument — setup, identification, headline result, the one or two checks that matter — must fit in ≤7,000 words (minus 200 per exhibit, ≤5 exhibits) with everything else in the online appendix. If the core argument cannot be made short, it is not an AER: Insights paper.

Where it goes if it is NOT AER: Insights

SymptomBetter venue
Multiple inter-dependent results that need each otherAER (long, multi-result flagship)
One result but field-specific, not general interestAEJ: Applied / Micro / Macro / Policy
Incremental / a robustness note on existing worka field journal or a comment
Pure methods with no substantive payoff in a short papera methods/econometrics journal

Sharpening a candidate into a single insight

  • Pick the headline. Of the results you have, which ONE is most surprising and most cleanly identified? Make that the paper; demote the rest.
  • Cut, do not summarize. Secondary results become appendix material or a future paper — not a compressed in-text paragraph.
  • Name the surprise. AER: Insights rewards a result that overturns or sharply refines a prior belief; write the prior you are correcting.
  • Confirm identification early (aeri-identification) — a clean insight with a shaky design is not publishable short, because there is no room to defend a weak design.

Common AER: Insights archetypes (and what makes each "one insight")

ArchetypeThe single insight is…Failure mode
One clean causal estimatea single headline effect from a credible designreporting every heterogeneity split in-text
A striking new fact / measurementone important fact a non-specialist did not knowa descriptive paper with no single takeaway
A sharp theory resultone surprising theorem / impossibilitya general framework with the result as a corollary
A method that flips an answerthe substantive answer it changes, in few pagesthe method shown without a substantive payoff

Worked vignette (illustrative)

A fictional project has (a) a clean RCT effect of posted wages on women's applications, (b) a heterogeneity split by occupation, and (c) a structural extension on wage offers. The AER: Insights paper is (a) alone — one surprising, general-interest, cleanly identified effect. (b) and (c) go to the Supplemental Appendix or a separate paper. Compressing all three into one short paper would be a "mini-AER" and likely a reject; the discipline is to make (a) the entire paper and defend it superbly in few pages.

Going-or-not-going decision

If you cannot pass all three filters — single insight, AER-level importance, self-containment — the honest move is to route elsewhere (AER for multi-result, an AEJ for field interest) rather than force-fit. The fast, no-R&R process (aeri-referee-strategy) punishes a borderline fit with a quick reject.

Checklist

  • The paper is stated in one sentence with a single headline object
  • The insight clears AER-level general-interest importance (not just length-appropriate)
  • A clear prior belief is named that the result overturns or sharply refines
  • The full argument plausibly fits the cap with secondary material in the appendix
  • Venue checked vs. AER (multi-result) and AEJ (field) — AER: Insights chosen deliberately
  • Identification is clean enough to defend in a short paper

Anti-patterns

  • A "mini-AER": one main result plus several supporting results kept in-text
  • A correct but incremental result pitched as short-because-small
  • A field-interest result with no general-interest hook
  • Splitting one idea across two thin papers, or fusing two ideas into one over-cap paper
  • Choosing AER: Insights for speed when the idea genuinely needs AER's length

Output format

【One-sentence insight】<single headline object>
【Importance】why a non-specialist cares (general interest, AER-level)
【Prior overturned】<belief the result corrects>
【Fits short format?】core argument under cap, rest to appendix? [Y/N]
【Venue check】AER: Insights vs AER vs AEJ — chosen because […]
【Next step】aeri-literature-positioning

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

brycewang-stanford/Awesome-Journal-Skills1.3천2026년 9월 27일 업데이트

brycewang-stanford의 스킬 모두 보기

이 스킬의 문제 신고