Skill UI

A comprehensive guide for conducting and reporting rigorous experimental research in social and behavioral sciences. It covers various design types (vignette, lab, field), statistical requirements (effect sizes, power analysis), quality control measures (manipulation checks, confound control), and advanced techniques for establishing causality, ensuring high internal validity for academic publication.

This skill guides authors on how to strategically position their machine learning research against complex academic landscapes. It covers comparing work to neighboring conferences (ICML, ICLR), analyzing contemporaneous preprints, and defining the exact technical delta from prior methods to minimize novelty risk for top-tier AI venues.

A comprehensive skill for assessing whether a neuroscience manuscript meets the exacting standards of a top-tier journal. It guides authors through evaluating the conceptual advance, mandatory multi-level integration (molecular to behavioral), the necessity of causal intervention, and adhering to strict structural and methodological requirements (e.g., STAR Methods, data availability). Ideal for researchers aiming to refine their work before submission.

A specialized checklist and guide for authors targeting the high-theory academic journal, October (MIT Press). This skill helps authors determine if their manuscript offers an original, theoretically rigorous intervention into modern/contemporary art, film, or critical theory, ensuring adherence to the journal's demanding intellectual and stylistic standards.

A comprehensive guide for drafting responses to reviewers during the Revise & Resubmit (R&R) phase. It teaches authors how to transform developmental, theory-level critiques into a credible, point-by-point reply and improved manuscript. Focus is placed on maintaining a respectful, collaborative tone and demonstrating deep theoretical engagement.

This guide provides a comprehensive framework for authors responding to major journal revisions. It teaches how to structure a technical, point-by-point rebuttal, systematically closing proof gaps, strengthening assumptions, incorporating missing baselines, and preparing the entire codebase and data for professional reproducibility review.

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.

Guides the rigorous development of theoretical models and research frameworks for Production and Operations Management (POM) manuscripts. It covers analytical, empirical, and behavioral tracks, ensuring that all assumptions and mechanisms are deeply grounded in real-world operational constraints and decision-making processes, thereby building a defensible academic argument.

Provides a comprehensive guide for writing high-quality academic rebuttal letters and revised manuscripts, especially for top-tier journals. It covers structuring responses to detailed reviewer comments, addressing significant revisions, incorporating new experiments, and maintaining professional, evidence-based communication to maximize acceptance chances.

This skill helps authors determine the suitability of their research paper (analytical, empirical, or behavioral) for prestigious Operations Management journals like Production and Operations Management (POM). It provides detailed guidance on required scope, methodological rigor (e.g., identification strategies, model assumptions), structural requirements, and common pitfalls to ensure maximum publication potential.

A comprehensive guide detailing the rigorous data analysis and reporting standards required for top-tier psychological science journals. It emphasizes moving beyond p-values to report effect sizes with confidence intervals, ensuring full disclosure of exclusions, and maintaining a clear separation between confirmatory and exploratory findings to enhance research credibility and reproducibility.

A guide for structuring the theoretical framework and hypotheses section of a psychological science manuscript. It emphasizes academic rigor, research transparency, and the necessity of clearly separating confirmatory (preregistered) predictions from purely exploratory findings. Learn how to state a clear mechanism, derive directional hypotheses, and avoid 'HARKing' to strengthen your scientific argument.