Skill UI

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

This skill serves as a comprehensive guide for authors submitting manuscripts to highly selective pure mathematics journals like Acta Mathematica. It assesses the work's significance, intellectual fit, required level of rigor, structural integrity, and adherence to top-tier academic standards, ensuring the research is ready for the global mathematical research community.

This guide provides comprehensive best practices for preparing supplementary materials for major academic conferences like AISTATS. It details the proper structure and handling of appendices, mathematical proofs, code archives, and additional data tables, ensuring that the main paper remains fully understandable even if the supplementary material is ignored by reviewers. Essential for publishing high-quality, robust research.

A comprehensive skill designed for authors targeting Classical and Quantum Gravity (CQG). It guides users on the journal's specific scope, methodological rigor (including mathematical proofs, numerical analysis, and observational constraints), and appropriate academic framing for manuscripts in general relativity, cosmology, and quantum gravity. Use this to assess article suitability before submission.

This guide outlines the rigorous process of formalizing cognitive theories for academic manuscripts. It teaches researchers how to structure a computational or mathematical model, define interpretable parameters, and derive discriminating predictions that differentiate their theory from rival accounts. Essential for moving from a verbal hypothesis to a testable, falsifiable scientific model.

A specialized guide for papers targeting top-tier econometric theory journals. This skill helps authors determine if their work's core contribution is a rigorous, non-trivial theoretical theorem (focusing on asymptotics, limit theory, or mathematical foundations) rather than an empirical application. It outlines the required mathematical rigor, structure, and scope necessary for acceptance in theory-heavy venues.

A comprehensive guide for structuring, auditing, and writing mathematically rigorous proofs for advanced academic papers, particularly in econometrics and statistics. It covers everything from defining assumptions and stating self-contained theorems to applying advanced proof techniques (e.g., fixed-point theorems, CLT). Use this when your central result requires impeccable rigor and completeness for top-tier publication.

This guide is designed for authors targeting top-tier journals like Foundations of Computational Mathematics (FoCM). It outlines the journal's high academic standards, requiring deep mathematical rigor, established theorems, and proofs (e.g., convergence, complexity bounds) at the math-computation interface. It emphasizes theoretical foundations over empirical results, making it ideal for advanced research in numerical analysis, optimization theory, and computational geometry.

This skill provides comprehensive guidance for authors seeking to submit manuscripts to the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). It helps evaluate the fit of research in signal processing, speech, audio, and image processing. It guides the framing, required empirical evidence (mathematical models, reproducible validation), and optimal positioning of the paper to maximize acceptance chances.

This guide provides a rigorous framework for constructing formal academic models, tailored for top-tier management journals. It emphasizes the four essential components—decision variables, constraints, objective functions, and assumptions—while providing detailed guidelines on ensuring model completeness, mathematical solvability, and, most critically, the defensibility of every assumption against academic scrutiny.

A guide for designing rigorous numerical experiments and simulations for academic manuscripts in management science. This methodology focuses on validating proven theoretical properties, assessing algorithm performance, exploring parameter sensitivity, and extracting actionable managerial insights. It emphasizes that simulations must complement, but never replace, formal mathematical proofs.

This guide outlines the rigorous formal requirements for econometric papers, focusing on achieving the level of mathematical rigor expected by top academic journals. It covers proving identification, verifying primitive assumptions, constructing asymptotic derivations (consistency, rate, distribution), and structuring proofs for maximum verifiability in causal inference and time series analysis.