Skill UI

This guide details how to position academic manuscripts within a specific geographic scholarly debate, rather than simply citing domain literature. It teaches authors to identify the "closest prior account," articulate a cross-cutting geographic stake, and engage with core geographic theories to enhance the impact of their findings for an advanced scholarly audience.

This skill guides authors on how to position their manuscript within the complex, multidisciplinary literature of Agricultural Systems (AgSy). It ensures that the paper advances the conversation by engaging both the domain knowledge and the core modeling/systems methodologies. The process focuses on identifying unresolved systemic trade-offs, pinpointing the exact research gap, and clearly articulating the unique contribution among established alternatives.

A comprehensive guide for structuring and refining academic research topics in Public Administration. It assists users in moving from broad, vague domains to concrete, theory-grounded, and publishable research questions. The guide emphasizes theoretical positioning, defining distinct contributions, and matching the paper's nature to appropriate academic journals.

This comprehensive guide outlines the academic standards and best practices for drafting or revising papers in the Computer-Supported Cooperative Work (CSCW) domain. It provides detailed advice on managing effective word count, shifting focus from individual users to group dynamics, strengthening theoretical arguments, and presenting empirical evidence with high academic rigor. Essential for researchers aiming for top-tier publication.

This guide provides best practices for authors writing the "Related Work" section for interdisciplinary AI conferences. It teaches how to precisely position novelty across multiple domains—including ML fairness, law, HCI, and critical theory—by writing delta-first contrasts and properly attributing borrowed concepts to their original academic source.

This skill guides authors on how to position interdisciplinary manuscripts for journals covering global environmental change (GEC). It emphasizes connecting multiple knowledge domains (e.g., biophysical science, governance, social science) and reframing the research gap as a conceptual contribution, rather than merely pointing out an absence of study. Essential for drafting strong introductions and satisfying reviewer requirements for disciplinary breadth.

This guide ensures that manuscripts for Global Environmental Change (GEC) are rigorous and highly readable for an interdisciplinary, policy-relevant audience. It focuses on polishing prose, front-loading contributions, minimizing jargon, and structuring discussions to meet strict word and abstract caps, ensuring clarity across environmental science, governance, and policy domains.

This guide provides detailed instructions for authors on structuring the Related Work section for ACM IMC papers. It emphasizes that related work must cover not only prior claims but critically analyze prior datasets, vantage points, tools, and methodologies. It teaches techniques like delta-first positioning and proper venue attribution to demonstrate deep domain expertise and avoid common academic pitfalls.

This guide provides expert advice on structuring the literature review for top-tier finance journals (e.g., JFE). It teaches authors to move beyond simple chronological listing, instead mapping the core academic debates, pinpointing the exact boundaries of the closest existing work, and articulating concrete, theoretically motivated gaps that the new research fills. This is essential for convincing domain experts of novel contribution.

These guidelines provide best practices for creating self-contained, rigorous, and ethically sound tables, figures, and exhibits for academic manuscripts, particularly within the New Media & Society domain. It covers various exhibit types, including quote-to-claim tables and network diagrams, emphasizing anonymization, showing uncertainty, and adhering to strict word count limits.

A structured framework based on the Cynefin model to classify problems by their underlying cause-effect domain (Clear, Complicated, Complex, Chaotic). It guides users on selecting the correct response mode and methodology before attempting solutions, preventing failure due to domain mismatch. Useful when unsure how to approach novel or mixed problems.

A systematic methodology for solving complex, multi-domain, or high-stakes problems where a single mental model creates blind spots. This process guides users to select 2-3 complementary models, assign distinct roles, define a conflict rule, and synthesize findings into a single, decision-ready recommendation.