Agent skill

qe-contribution-framing

Use to sharpen the one-sentence contribution of a Quantitative Economics (QE) manuscript so a general-interest Econometric Society reader sees the quantitative payoff immediately. Frames the claim and its scope; it does not redo estimation or write full prose.

brycew6m878★ · +32/wk · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill qe-contribution-framing --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 5 KB
Bundled scripts: none
Path: Quantitative-Economics-Skills/skills/qe-contribution-framing/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Contribution Framing (qe-contribution-framing) ## When to trigger - The "so what?" of the paper is not crisp in one sentence - Reviewers (or co-authors) disagree about what the paper's main point actually is - The contribution is described as a method or a dataset rather than as an answer - The claim's scope (what it does and does not establish) is blurry ## Framing the QE contribution QE publis

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About this skill
What does the qe-contribution-framing skill do?

Use to sharpen the one-sentence contribution of a Quantitative Economics (QE) manuscript so a general-interest Econometric Society reader sees the quantitative payoff immediately. Frames the claim and its scope; it does not redo estimation or write full prose.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill qe-contribution-framing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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