Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when estimation and results are the bottleneck for a Journal of Management (JOM) manuscript — SEM/CFA, HLM/multilevel, regression and interactions, mediation/moderation, and meta-analytic estimation with artifact corrections. Runs and validates the analysis; it does not design the study (jmgmt-methods) or frame the contribution (jmgmt-contribution-framing).
Use when research design and measurement are the bottleneck for a Journal of Management (JOM) manuscript — matching design to the theoretical claim, construct validity, common-method bias, endogeneity, multilevel structure, and (for meta-analyses) coding/artifact corrections. Designs the study; it does not run the estimation (jmgmt-data-analysis).
Use when you need to understand or set expectations for the Journal of Management (JOM) editorial and masked-review process — desk screening, the developmental R&R culture, the Review Issue track, and reading a decision letter. Explains the process; it does not draft the response letter (jmgmt-rebuttal).
Use when scoping the question and choosing the article type for a Journal of Management (JOM) manuscript — empirical vs. meta-analysis vs. systematic review, and which management subfield it speaks to. Locks fit; it does not build the theory (jmgmt-theory-development) or design the study (jmgmt-methods).
Use when the estimation, identification, construct validity, or modeling is the bottleneck for a Journal of Management Information Systems (JMIS) manuscript — econometrics on firm/platform data, SEM/PLS for behavioral constructs, analytical-model derivations, or ML/analytics evaluation. Executes and stress-tests the analysis the design (jmis-methods) chose; it does not redesign the study.
Use when choosing and defending the research design for a Journal of Management Information Systems (JMIS) manuscript — IT-value/platform econometrics, a behavioral survey or experiment, an analytical/economic model, or a design-science/data-science artifact. Matches the method to the IS claim and the ≤50-page budget; it designs the study and hands estimation/evaluation to jmis-data-analysis.
Use when calibrating expectations for the Journal of Management Information Systems (JMIS) editorial process — the EIC-led, email-intake, double-anonymized review by Associate Editors and expert referees, likely decision types, and timing. Sets expectations and revision posture; it does not draft the response letter (jmis-rebuttal).
Use when running and reporting the analysis for a Journal of Marketing Research (JMR) manuscript — selecting the estimator that matches the design, and meeting JMR's hard journal-level reporting mandate of exact p-values, standard errors, and effect sizes, plus replication-ready disclosure. Executes and reports; jmr-methods designs the study and jmr-contribution-framing states the payoff.
Use when matching the research design to the claim for a Journal of Marketing Research (JMR) manuscript — experimental design (lab and field), causal identification (IV/DiD/RDD/matching), or structural/analytical estimation. Adapts to JMR's dominant genres and to its journal-level rigor and replication expectations. It designs; jmr-data-analysis executes and reports.
Use when understanding how the Journal of Marketing Research (JMR) review process works — initial editorial evaluation and possible desk rejection, double-anonymized review, the EIC-plus-Coeditor domain-routing model, the requirement of two independent reviews to reach a Revise/Accept decision, and how to read a JMR decision letter. Sets expectations; jmr-rebuttal drafts the response.