Skill UI

This skill assesses whether a research question or study aligns with the scope of top-tier public economics journals, such as JPubE. It guides the user to evaluate the government's economic role, the welfare implications, and the international generalizability of the findings, providing a critical review pass before submission.

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 comprehensive guide for structuring a point-by-point rebuttal letter to highly selective academic journals. It addresses core methodological concerns such as parameter sensitivity, out-of-sample evaluation, CGE reproducibility, and data transparency. It also provides strategic advice on re-framing causality claims into robust measurement contributions.

A comprehensive guide detailing how to structure, package, and validate research artifacts for major machine learning conferences like KDD. It covers best practices for creating anonymous, reproducible repositories, handling scale claims, and providing robust evidence for different research tracks (Research, ADS, Datasets).

A rigorous pre-writing framework used by researchers to stress-test a study's significance against the high standards of top-tier medical journals (e.g., The Lancet). It assesses whether the work is clinically important, globally relevant, likely to change policy, and addresses health equity, preventing wasted research efforts on unsuitable manuscripts.

A comprehensive guide for linguists on determining if their research project has sufficient theoretical scope and general significance for submission to a generalist, high-impact journal. It helps researchers transition from descriptive findings to theoretically grounded, cross-subfield claims, improving their structuring and argumentation before submission.

This skill guides authors on whether their linguistics research is broad enough for Language (LSA's flagship journal). It checks for a clearly stated, generalizable claim backed by rigorous, cross-linguistic data, and adherence to high academic standards, ensuring the work transcends a single subfield.

A comprehensive guide designed to help authors assess the suitability of their manuscript for Limnology and Oceanography. It focuses on evaluating whether the work provides rigorous process insight by coupling physical, chemical, and biological processes in aquatic systems, going beyond mere descriptive surveys. This tool covers scope, required quantitative evidence, biogeochemistry, and journal-specific house style.

This guide helps researchers evaluate if their operations management (OM) paper is suitable for the Manufacturing and Service Operations Management (M&SOM) journal. It provides detailed criteria on scope, required operational insight, method rigor (analytical or empirical), and structural house style, ensuring the core contribution addresses practical operations problems rather than just technical methodology.

This guide details the complex, multi-stage review process for high-level academic journals, such as Management Science. It explains critical stages like department-specific desk screening, double-anonymous refereeing, and assessing cross-departmental fit. It is essential for authors who need to understand how submissions are evaluated, interpret rejection letters (e.g., fit vs. rigor), and plan their next steps for revision or re-aiming.

This guide details the process of preparing complex research artifacts (simulators, configurations, traces, and scripts) for post-acceptance evaluation, especially for microarchitecture papers. It outlines best practices for achieving ACM badges like 'Available' and 'Reproducible,' focusing on structuring the code base with tiered entry points (smoke, key, full) and handling dependencies, licensed data, and simulation time constraints.

This guide provides a rigorous framework for evaluating computer architecture designs. It covers critical steps including selecting the appropriate simulation instrument (from analytical models to silicon measurements), constructing robust baselines, performing ablation studies, and reporting comprehensive metrics like geomeans and full overhead accounting to ensure scientific validity.