Skill UI

This skill is designed for advanced academic writing, helping users transform vague model findings into precise, testable mathematical propositions or theorems. It guides the rigorous structuring of proofs, ensuring logical completeness, correctly handling boundary conditions, and separating detailed derivations into an appendix, meeting the standards of top-tier academic journals.

This comprehensive pre-submission checklist is designed for authors preparing academic manuscripts for specific journals. It guides users through critical checks, including length constraints (e.g., $\le 8000$ chars), abstract/keyword requirements, proper citation styles (sequential numbering), and highly specific formatting for mathematical content (formula numbering, theorem placement). Use this tool to ensure your paper meets all structural and stylistic guidelines before submission.

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.

This tool guides researchers in assessing the methodological fit of their empirical findings for top-tier journals like the Journal of Econometrics (JoE). It pressures-tests the proposed work against rigorous academic standards, checking for mathematical novelty, clear economic motivation, and methodological advancement. Use it when unsure of the journal scope or the true contribution type.

This guide details the rigorous requirements for publishing in top-tier quantitative management journals. It specifically targets research outputs that utilize mathematical models, theoretical proofs, and computational algorithms (e.g., operations research, decision theory). It strongly advises against purely empirical surveys or case studies, emphasizing methodological contribution over policy implication.

This skill is a comprehensive guide for authors targeting the Journal of the American Mathematical Society (JAMS). It outlines the journal's extremely high standards for pure and applied mathematics, covering required levels of mathematical rigor, novelty, general significance, and specific formatting guidelines (AMS LaTeX, MSC codes, arXiv). Use this tool to assess the suitability of your manuscript for top-tier academic publication.

A comprehensive skill designed to guide researchers submitting solid mechanics manuscripts to the Journal of the Mechanics and Physics of Solids (JMPS). It helps authors elevate their work by focusing on establishing fundamental governing principles or physical mechanisms, moving beyond simple numerical simulations. Usage scenarios include pre-submission fit checks, determining the required mathematical rigor, and correctly framing the contribution against broader fields like plasticity or micro-mechanics.

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 comprehensive guide helps authors determine the suitability of their research for top-tier mathematical finance journals. It outlines the required level of mathematical rigor, covering topics like stochastic processes, derivatives pricing, and optimal control. The focus is on novel theorems and proofs with clear financial interpretation, not on empirical findings alone.

This guide teaches researchers how to structure and articulate the contribution of a mathematical finance manuscript. It advises authors to move beyond simply proving a theorem, instead focusing on framing the methodological novelty and its direct payoff to financial modeling problems like pricing, hedging, risk management, and portfolio choice. It ensures the paper is seen as relevant to practitioners, not just mathematicians.

This guide details how to conduct rigorous, reproducible numerical simulations for mathematical finance research. It emphasizes that numerical experiments must strictly support a theoretical theorem (e.g., convergence rates, error bounds) rather than performing empirical data fitting. Learn how to properly specify schemes, report theoretical vs. empirical error rates, and maintain full reproducibility to meet high academic standards.

This guide outlines the necessary structure and high level of mathematical rigor required for theoretical manuscripts in Mathematical Finance. It covers techniques for establishing precise assumptions (e.g., filtrations, integrability), stating theorems cleanly, and constructing self-contained proofs for results in stochastic control, no-arbitrage pricing, and PDEs. It emphasizes methodological novelty over empirical identification.