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aejmac-literature-positioning

Use when the contribution of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript relative to the macro frontier is fuzzy, contested, or undersold.

Install / Use

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aejmac-literature-positioning

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Supported Platforms

Universal

Tags

Our assessment of aejmac-literature-positioning

aejmac-literature-positioning scores 85/100 on our quality scale, 2231st of 4,610 Development & Engineering skills we index (top 49%).

Its SKILL.md is 4.0 KB long, well organised into 11 sections with 1 code example: a solid amount of guidance for an agent.

With 1,158 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
17/20
Description
15/15
Adoption
13/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 18 days ago, so aejmac-literature-positioning is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

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Frequently asked questions

How do I install aejmac-literature-positioning?
Run npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aejmac-literature-positioning. The install tabs above show the steps for each supported agent.
Which AI agents does aejmac-literature-positioning work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is aejmac-literature-positioning safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is aejmac-literature-positioning still maintained?
The repository was last updated 18 days ago, so aejmac-literature-positioning is actively maintained.

name: aejmac-literature-positioning description: Use when the contribution of an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript relative to the macro frontier is fuzzy, contested, or undersold. Frames the marginal contribution against quantitative-theory and identified-empirical literatures; it does not fix the identification or the model itself.

Literature Positioning (aejmac-literature-positioning)

When to trigger

  • The introduction cites many papers but never says what is new
  • A referee could ask "how is this different from [the obvious prior paper]?" and you lack a one-line answer
  • The paper sits between a theory literature and an empirical literature and the bridge is unclear
  • The contribution is real but framed as incremental ("we extend X by adding Y")

The AEJ: Macro positioning bar

AEJ: Macro is a broad-interest macro journal, so positioning must locate the paper for a general macro reader, not only for the three people working on the exact model or shock series. Two literatures usually bound a macro paper, and you must place the contribution against both:

  • the quantitative-theory frontier (DSGE / HANK / heterogeneous-agent / structural) — what does your model deliver that the canonical model cannot?
  • the identified-empirical frontier (SVAR / local projections / narrative / proxy-VAR / micro-data macro) — what does your evidence pin down that prior designs left open?

A hybrid paper (model matched to identified evidence) must say why the combination answers a question neither side resolved alone — this is often the strongest AEJ: Macro contribution.

Positioning moves

Move 1 — Name the closest 2–3 papers, then the wedge

Do not list 30 citations. Name the 2–3 papers a referee would call "basically the same," and state in one sentence what your paper does that each cannot. The wedge should be a quantity, mechanism, or identification gain, not "a different dataset."

Move 2 — Place against both frontiers

  • vs. theory frontier: "The canonical [RBC/NK/HANK] model predicts/assumes Z; we relax/measure Z and the macro implication changes by ___."
  • vs. empirical frontier: "Prior [SVAR/LP] designs cannot separate channel A from channel B; our [narrative/high-frequency/sign-restriction] identification does."

Move 3 — State the contribution as a sentence with a quantity

"We show that [mechanism] accounts for [X%] of [aggregate], overturning/refining the prior view that ___." A magnitude makes the contribution legible to a general reader and to the AEA process.

Move 4 — Calibrate the claim

Say what the paper does not establish: the regime, sample, or country where the result holds, and what remains open. Over-claiming is the fastest path to a referee's "the contribution is oversold."

Checklist

  • [ ] The 2–3 nearest papers named, each with a one-line wedge
  • [ ] Positioned against the quantitative-theory frontier (what the model adds)
  • [ ] Positioned against the identified-empirical frontier (what the design pins down)
  • [ ] Hybrid papers: why model + evidence beats either alone
  • [ ] Contribution stated as one sentence with a quantity/mechanism
  • [ ] Scope limits stated; no over-claiming beyond regime/sample
  • [ ] Broad-interest framing: a general macroeconomist sees why it matters

Anti-patterns

  • A "literature wall" — long paragraphs of citations with no stated wedge
  • Positioning only against the subfield, invisible to general macro readers (reads as a field paper, wrong for AEJ: Macro)
  • "We are the first to…" claims that a single prior paper falsifies
  • Framing the contribution as a method tweak rather than a macro answer
  • Burying the contribution sentence on page 4 instead of paragraph 1–2 of the intro

Output format

【Nearest papers + wedge】1) ... 2) ... 3) ...
【Vs. theory frontier】what the model/structure adds
【Vs. empirical frontier】what the identification pins down
【Contribution sentence (with quantity)】...
【Scope / what it does NOT claim】...
【Next step】aejmac-identification (empirical) and/or aejmac-theory-model (quantitative)

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryDevelopment
Updated18d ago
Forks153

Languages

Stata

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions