Skill UI

A comprehensive guide for constructing rigorous and auditable exhibits (tables and figures) in academic manuscripts, particularly for Organization Studies and process modeling. Learn how to structure data-to-theory evidence tables, process models with explicit mechanisms, and data structure figures to allow reviewers to clearly trace the inference from raw data to theoretical constructs, significantly enhancing the paper's methodological rigor.

This skill acts as a comprehensive workflow router for authors preparing Operations Research (OR) manuscripts for top-tier journals. It guides the user through the entire modeling-to-submission lifecycle, mapping the necessary stages—from initial theory development and literature positioning to computational data analysis and final contribution framing. Its purpose is to ensure methodological rigor and adherence to the journal's specific, multi-stage norms.

This guide details the rigorous and single-shot review process for ACM's Principles of Distributed Computing (PODC). Unlike venues with major revision rounds, PODC focuses entirely on the strength of the proof, the significance of the result, and the honesty of the model. It outlines the criteria (correctness, novelty, tightness) and emphasizes that all necessary work must be completed pre-submission, as there are no post-acceptance revision cycles.

This guide assists researchers in selecting the most appropriate and rigorous methodology for Production and Operations Management (POM) manuscripts. It covers various research tracks, including analytical modeling, stochastic process theory, game theory, causal empirical identification (DiD, IV, RD), behavioral experiments, simulation, and operations data science (ML/forecasting). It ensures the research method is tightly linked to the core operational question and possesses significant practical value.

This skill serves as a comprehensive router for developing academic manuscripts in Production and Operations Management (POM). It does not replace specialized skills but guides users through the entire scholarly process—from initial topic selection and theoretical modeling to data analysis, contribution framing, polishing, and submission. It ensures users maintain structural integrity and identify the correct next specialized skill to invoke, guiding the research journey from concept to publication.

This skill guides the rigorous defense of research design for demographic studies, particularly those linking population dynamics to development outcomes. It assists in selecting and justifying appropriate methods—such as life tables, decomposition, event-history modeling, and age-period-cohort analysis—and critically addresses the identification of causal links between population structure and development progress. It focuses on strengthening the argument by confronting rival hypotheses and establishing methodological rigor, without writing code.

Provides a comprehensive guide to the entire academic publication review lifecycle, from initial submission to acceptance. It models the key stages—including editorial screening, expert review, revision, and final check—identifying critical decision criteria, common rejection reasons, and actionable leverage points for authors to significantly improve manuscript quality and navigate the publishing process.

Provides rigorous statistical guidance for conducting meta-analyses in academic settings. This skill covers the calculation of standardized effect sizes (e.g., Hedges' g, log odds ratio), selecting appropriate models (defaulting to random-effects or multilevel models), and accurately quantifying between-study heterogeneity (Q, I², τ², and prediction intervals). It ensures the analysis meets state-of-the-art standards for high-impact psychological journals.

This comprehensive guide details the rigorous standards required to defend the identification of parameters, estimands, or causal effects in quantitative economic research. It provides specific, advanced methodologies and checklists for three major branches: structural modeling, empirical causal design (e.g., DID, IV, RDD), and experimental studies. It ensures the work meets the high quantitative bar of top-tier journals by demanding explicit data-to-object mapping and robust diagnostic testing.

This guide outlines the rigorous standards for quantitative analysis required in academic economic research, particularly dynamic modeling. It provides a framework covering systematic model calibration, moment estimation, numerical solution discipline, and establishing fully reproducible computation workflows, ensuring results are highly auditable and verifiable.

A detailed guide for structuring the introduction and related work sections of manuscripts submitted to Review of Economic Dynamics (RED). It teaches authors how to accurately map their contribution within established quantitative modeling traditions (e.g., DSGE, HANK), explicitly identifying the quantitative gap and the marginal theoretical advance for a specialized readership.

Guides authors through the rigorous, end-to-end lifecycle of submitting a paper to the Review of Economic Dynamics (RED). It acts as an orchestration checklist, covering critical stages from initial scope definition and quantitative modeling to data archiving, manuscript formatting, fee payment, and handling revisions. It ensures authors maintain methodological rigor and code-first discipline throughout the entire academic publishing process.