Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when results for a Journal of Economic Geography (JEG) manuscript may be sensitive to spatial scale, the weight matrix, spatial autocorrelation, sample, or specification. Organizes robustness by spatial threat; it does not invent results.
Use when choosing or sharpening the research question for a Journal of Economic Geography (JEG) manuscript so it genuinely bridges geographical economics and human geography. Tests two-community fit and scope; it does not invent evidence or citations.
Use when calibrating completeness vs. selectivity and ensuring fairness across schools, authors, and controversies in a Journal of Economic Literature (JEL) survey — including not over-promoting the author's own work. Audits coverage and even-handedness; it does not design the framework (jel-organizing-framework) or write prose (jel-writing-style).
Use when systematically gathering, reading, and synthesizing a large body of economic research for a Journal of Economic Literature (JEL) survey — coverage discipline and avoiding citation gaps. Builds the evidence corpus and synthesis notes; it does not impose the analytical spine (jel-organizing-framework) or write prose.
Use when imposing an analytical structure or taxonomy on a literature for a Journal of Economic Literature (JEL) survey — the "spine" that turns a reading list into an argument about the field. Designs the framework; it does not gather the literature (jel-literature-synthesis) or judge balance (jel-comprehensiveness-and-balance).
Use when judging whether a literature is a Journal of Economic Literature (JEL)-scale survey topic — mature, important, and genuinely in need of synthesis — rather than a narrow review. Decides fit and frames the survey's animating question; it does not gather or synthesize the literature (that is jel-literature-synthesis).
Use when interpreting the Journal of Economic Theory (JET) review pipeline — editor desk-screening, single-anonymized refereeing, at least two reviewers, editor decisions, and the generative-AI bar on referees.
Use when designing or stress-testing an asset-pricing test for a The Journal of Finance (JF) manuscript — factor models, Fama–MacBeth vs. panel, standard-error corrections, out-of-sample discipline. For corporate causal claims use jf-identification.
Use when the causal-identification strategy is the bottleneck for a corporate / empirical The Journal of Finance (JF) manuscript — natural experiments, IV, DID, RDD. Stress-tests the design; for asset-pricing tests use jf-empirical-design.
Use when settling the measurement and estimation choices of a Journal of Financial Economics (JFE) manuscript — factor construction, portfolio sorts, Fama-MacBeth/GMM, standard-error clustering, and multiple-testing discipline. Covers the design/estimator layer; for causal identification of corporate-finance effects use jfe-identification.