Skill UI

This workflow serves as a comprehensive router for authors preparing manuscripts for the MIS Quarterly (MISQ). It guides researchers through the entire academic writing lifecycle, ensuring systematic development from initial topic selection and identifying the core IS research tradition (behavioral, design science, economics, or organizational). It maps out the necessary sequence of steps, including theory development, literature positioning, methodology, data analysis, contribution framing, and final submission readiness.

This framework guides the creation of mandatory STAR Methods, ensuring scientific manuscripts meet the highest standards of transparency and reproducibility. It mandates the systematic inclusion of Key Resources Tables (KRT), detailed resource availability, experimental model specifications, step-by-step methods, and consolidated statistical analysis (QSA). Ideal for researchers needing to structure complex biological or chemical research findings for publication.

This guide provides a structured framework for responding to reviewers and editors regarding a meta-analysis or systematic synthesis. Because such revisions often involve expanding the search, changing eligibility criteria, or adding complex bias diagnostics, the response must maintain impeccable internal statistical consistency across all tables, figures, and estimates. It ensures all methodological changes are clearly propagated and justified.

This skill guides researchers on whether a proposed research topic is suitable for a high-level synthesis journal (like Psychological Bulletin). It assesses the existing literature's depth, general academic significance, and whether the contribution is synthesizing prior findings. It determines the most appropriate synthesis methodology—such as meta-analysis, systematic review, or qualitative meta-synthesis—by comparing the nature of the pooled data.

This tool serves as an essential orchestrator for crafting and submitting research syntheses (such as meta-analyses and systematic reviews) for Psychological Bulletin. It guides users through the entire rigorous research lifecycle—from initial topic selection and literature searching (PRISMA) to data coding, meta-analysis, theoretical integration, and final manuscript preparation. It ensures that the project is correctly framed as a synthesis of existing work, not a primary study.

A comprehensive guide detailing best practices for achieving high reproducibility in recommender system research. It covers critical areas such as dataset version pinning, defining symmetric tuning protocols, selecting full-ranking metrics, handling non-determinism via multiple seeds, and structuring rigorous divergence analysis for academic publications.

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 comprehensive guide detailing how to make systematic reviews and meta-analyses fully auditable and reproducible for academic publishing. It emphasizes adhering to standards like PRISMA and MARS, quantifying inter-coder reliability (e.g., Kappa/ICC), and sharing all open materials, including search strings, coding datasets, and analysis code (via OSF/DOI).

This router guides authors through the complex, multi-stage process of preparing a manuscript for a top-tier finance journal like the Review of Finance (RoF). It diagnoses current bottlenecks—whether related to contribution framing, data analysis, or rebuttal strategy—and systematically recommends the next specialized skill to ensure a complete and high-quality submission.

A rigorous framework for designing reproducible and scientifically sound experiments in robotics and AI, specifically for academic publication (e.g., RSS). It guides users to shift focus from simple system demonstration to testing a falsifiable scientific claim. Key areas covered include deriving conditions from a claim, establishing hardware trial protocols, managing simulation vs. real-world evidence splits, and performing detailed failure attribution analysis.

This skill provides comprehensive guidelines for submitting manuscripts to the journal *Science of Science and Management of S&T*. It helps researchers assess the article's fit, structure, required evidence (e.g., patent quality, network analysis), and common pitfalls associated with technology policy and innovation systems research, ensuring academic rigor.

This comprehensive guide outlines best practices for designing data systems evaluations, ensuring results are robust, reproducible, and convincing to expert reviewers. It covers workload realism, baseline fairness, necessary ablation studies, and advanced metric visualization techniques (e.g., sweeping curves, failure analysis) required for top-tier data systems research.