Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use to understand how Mind evaluates a submission — triple-anonymous peer review, quality as the sole criterion, the role of expert referees, and what it means for a journal that receives over 800 submissions a year. Sets expectations and shapes the paper to survive review; it does not contact editors.
Use when targeting Mining Software Repositories (MSR) or deciding whether a computer-science manuscript fits this venue. Encodes conference fit, framing, evidence bar, submission-cycle checks, rebuttal posture, and desk-reject risks for software analytics.
Use when positioning a MIS Quarterly manuscript inside the right information-systems conversation — engaging canonical IS work, showing the IT artifact is central, and distinguishing the paper from adjacent IS venues (ISR, JMIS, JAIS) and from the parent reference disciplines (management, economics, computer science).
Use when choosing and defending the research design for a MIS Quarterly manuscript — a behavioral survey/experiment, an economics-of-IS identification strategy, a design-science build-and-evaluate cycle, or an organizational/qualitative design. Matches the method to the IS claim and the manuscript category; it designs the study and hands estimation/evaluation to misq-data-analysis.
Use to understand how MIS Quarterly's editorial process works and to read a decision letter — the double-anonymous model, the Senior-Editor/Associate-Editor routing where the SE owns the decision, what a major/minor revision versus reject signals, and what to expect across one or more revision rounds. Sets expectations and interprets decisions; the response itself is drafted with misq-rebuttal.
Use when shaping or stress-testing a research question for MIS Quarterly — confirming it is a genuine information-systems question, naming which of the four IS traditions (behavioral, design science, economics, organizational) it lives in, and choosing the matching manuscript category before any data are collected or any artifact is built.
Use when estimating and validating the model for a Marketing Science manuscript — running structural estimation (GMM/MLE/SMM/Bayes), checking identification empirically, assessing model fit, computing counterfactuals, and preparing the replication package. Executes the analysis; it does not design the model (mksc-theory-development) or choose the genre (mksc-methods).
Use when positioning a Marketing Science manuscript in its literature — locating the contribution among structural and analytical modeling precedents and the relevant substantive stream (pricing, advertising/digital, channels/retail, branding, platforms, analytics), and disclosing self-overlap. Positions the paper; it does not state the headline contribution (mksc-contribution-framing).
Use when the empirical/analytical approach is the bottleneck for a Marketing Science manuscript — choosing among structural econometrics, analytical modeling, and model-disciplined causal/ML methods, and making the model estimable and identified. Designs the approach; it does not execute the estimation and counterfactuals (mksc-data-analysis).
Use when understanding how Marketing Science evaluates a manuscript — the double-anonymous Senior-Editor / Associate-Editor routing, what the first-pass and review look for, and how to read a decision letter. Explains the process; it does not draft the revision response (mksc-rebuttal).