Skill UI

This skill provides a comprehensive guide for authors targeting the Journal of Marketing Research (JMR). It analyzes the journal's demanding standards for methodological rigor, covering quantitative modeling, behavioral experiments, and design validity. Use this tool to assess if your manuscript's contribution, structure, and evidence base meet JMR's high academic bar, or to suggest more appropriate alternative venues.

A comprehensive guide for analyzing multi-study packages for high-level academic journals. This skill focuses on executing internal meta-analyses, reporting standardized effect sizes with confidence intervals (CIs), establishing honest robustness checks, and reconciling confirmatory versus exploratory findings, meeting demanding journal standards.

This guide provides comprehensive methodology for advanced empirical analysis in Public Economics, particularly for manuscripts using large administrative or register microdata. It covers advanced topics like estimating behavioral elasticities, calculating welfare metrics (MVPF, DWL), and ensuring robust causal identification strategies. It emphasizes translating raw coefficients into policy-relevant quantities with rigorous uncertainty quantification.

This guide establishes rigorous methodological standards for incorporating forecasting and prediction claims in academic manuscripts. It emphasizes the necessity of genuine out-of-sample evaluation, comparison against robust benchmarks (like Random Walk or AR models), and utilizing advanced statistical tests (e.g., Diebold-Mariano) to prove prediction superiority, moving beyond simple in-sample fit reporting.

A rigorous methodological framework for identifying core risk and uncertainty parameters in academic research. This guide details how to design experiments or structural models to explicitly separate confounds (e.g., utility curvature vs. probability weighting, preferences vs. beliefs) and ensure that the measured parameter is genuinely identified, thereby enhancing the scientific rigor of behavioral finance and econometrics studies.

A comprehensive framework for conducting and structuring robustness checks in academic papers, particularly in econometrics and behavioral economics. It guides researchers on identifying specific threats to the core parameter (e.g., functional form dependence, incentive bias, sample selection) and organizing tests by threat, ensuring the findings are statistically reliable and withstand expert scrutiny.

This guide provides rigorous standards for transforming complex behavioral and risk data into clear, statistically sound, and academically rigorous tables and figures. It ensures that key decision-theoretic content, such as choice patterns, parameter estimates, and uncertainty, are visibly and interpretably presented for academic publication.

A comprehensive guide for researchers developing models in decision theory under risk and uncertainty. It outlines how to strengthen theoretical representations by ensuring the model is anchored by clear axiomatic foundations, a mathematically tractable functional form, and crucially, verifiable behavioral content (i.e., what the model predicts or forbids). This helps bridge the gap between abstract theory and empirical data.

This guide helps researchers determine if their core contribution is focused on measuring or refining a specific risk or uncertainty primitive (e.g., ambiguity attitudes, VSL, probability weighting). It provides a framework to assess scope fit and distinguish between behavioral anomalies, methodological findings, and foundational risk theory, ensuring the paper is positioned correctly for high-tier academic journals like JRU.

A comprehensive guide for structuring the empirical section of high-level machine learning papers (e.g., KDD). It details the 'four-axis evidence plan'—Quality, Scale, Efficiency, and Mechanism—and emphasizes critical data hygiene practices. Learn how to prevent temporal leakage, structure ablations in a matrix format, and design post-launch measurements to ensure academic rigor and reproducibility.

This guide provides comprehensive guidelines for authors preparing linguistic manuscripts, focusing on data documentation, reproducibility, and ethical sharing practices. It details how to properly share quantitative data, elicited fieldwork corpora, and phonetic measurements while adhering strictly to community consent, licensing terms, and best academic practices. It emphasizes that transparency must be coupled with ethical responsibility.

Systematically transforms high-level ideas or goals into a detailed, structured, and executable taskbook designed for AI agents. This process involves deep research, validation against existing codebases, adherence to strict formatting rules, and generating a single, copy-pastable block of instructions for immediate agent execution. It distinguishes between execution and exploration tasks, ensuring all steps are verifiable and anti-cheating measures are included.