qe-topic-selection
Use when first judging whether a project fits Quantitative Economics (QE) — a substantive economic question answered with serious quantitative methods (empirical, structural/computational, experimental, or simulation), sister to Econometrica and Theoretical Economics. Tests fit and sharpens the question; it does not design the estimation.
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill qe-topic-selection --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Topic Selection (qe-topic-selection) ## When to trigger - You have data, a model, or an experiment but are unsure QE is the right home - The project feels like "pure theory" or "pure method" and you suspect a sibling journal fits better - The economic question behind the quantitative exercise is not yet sharp - You are choosing between QE, Econometrica, and a top field journal ## The QE fit bar
What does the qe-topic-selection skill do?
Use when first judging whether a project fits Quantitative Economics (QE) — a substantive economic question answered with serious quantitative methods (empirical, structural/computational, experimental, or simulation), sister to Econometrica and Theoretical Economics. Tests fit and sharpens the question; it does not design the estimation.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill qe-topic-selection --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.