Skill UI

This guide assists researchers in sharply defining and positioning their academic contributions within the highly specialized field of international finance. It provides frameworks for moving beyond simple extensions, instructing authors on how to identify a precise, novel margin against existing literature and competing scholarly programs to maximize publication credibility.

This skill guides the structuring of theoretical and conceptual frameworks for academic manuscripts, particularly for journals focusing on family science. It helps researchers move beyond simply listing correlations by defining explicit concepts, identifying underlying family mechanisms (e.g., stress process, life course, ecological), and deriving testable hypotheses and scope conditions. It is crucial for transforming empirical findings into a robust, theory-driven contribution.

A comprehensive guide for building robust, testable theoretical frameworks for submissions to the Journal of Marketing Research (JMR). It distinguishes between deriving behavioral mechanisms (mediators, processes) and specifying structural/econometric primitives and identification arguments, ensuring the research contribution meets academic rigor.

This guide helps authors structure the Related Work section for top-tier academic journals like the Journal of Software. It focuses on demonstrating incremental contribution (delta-first positioning), structuring the review across multiple software disciplines (e.g., SE, Systems, Databases), ensuring balanced citations (international vs. domestic), and building comprehensive classification frameworks for review papers.

A comprehensive guide to strengthening research designs for public administration manuscripts, particularly for rigorous journals that emphasize experimental and causal identification. It covers experimental designs, causal observational methods (DID, IV, RDD), multilevel modeling, and mixed methods, providing frameworks to defend against common methodological biases and rival explanations.

A comprehensive guide for developing rigorous spatial economic models, designed for manuscripts needing deep theoretical interpretation (e.g., JUE submissions). It instructs users on how to build quantitative spatial models (QSMs), sorting models, and spatial-equilibrium frameworks to interpret reduced-form estimates, establish counterfactual policies, and ensure that theory rigorously disciplines the empirical findings. Focus areas include general-equilibrium reallocation, policy invariance, and identifying parameters.

This skill guides the structuring of the literature review section for general linguistics journals. Instead of confining the argument to a single subfield, it teaches how to frame the research contribution by engaging with major theoretical debates and rival accounts across diverse linguistic frameworks, thereby maximizing visibility and impact for a broad, expert audience.

This guide teaches how to elevate descriptive linguistic data (such as glossed patterns or corpus trends) into robust, theoretically grounded claims. It outlines the necessary structure for presenting an argument with explicit assumptions, testable predictions, and critical engagement with competing theoretical frameworks, moving beyond mere description to achieve genuine theoretical depth.

This skill polishes the prose and ensures the house style of a linguistics manuscript, making it legible to a general audience across diverse subfields and frameworks. It focuses on structuring the argument for clarity, defining jargon, leading with the main claim, and conforming citations to the specific Language Style Sheet, rather than building the core theoretical argument itself.

A comprehensive guide for integrating Markstream, a high-performance Markdown renderer, into modern frontend frameworks like React, Vue, Svelte, and Next.js. It handles complex streaming scenarios, ensuring minimal peer dependencies, correct CSS ordering, and proper Server-Side Rendering (SSR) boundaries for dynamic content and AI chat interfaces.

Provides comprehensive guidelines for achieving high reproducibility in Machine Learning Systems (MLSys) performance claims. It details how to rigorously control sources of non-determinism (e.g., thermal noise, random seeds) and pin the entire technical stack—from hardware and drivers to frameworks and serving policies—to ensure results can be validated by third parties.

A comprehensive guide for researchers writing about Manufacturing and Service Operations Management (M&SOM). It mandates that the method chosen must be dictated by the core operational decision. Provides detailed frameworks for both the analytical lane (e.g., stochastic optimization, queueing theory, game theory) and the empirical/causal lane (e.g., Difference-in-Differences, Instrumental Variables), ensuring the research delivers actionable operational insights.