Skill UI

A comprehensive guide for submitting high-quality replication packages for academic journals like the American Economic Journal (AEJ: Micro). This package covers two essential components: self-contained, verifiable proofs for theoretical claims (the Proof Appendix), and a fully reproducible code and data deposit (the Code/Data Repository). It ensures that all numerical results, simulations, and empirical findings can be regenerated by the journal's editor, maintaining the highest standards of scientific transparency.

This guide outlines best practices for authors presenting theoretical and empirical results for top-tier journals like the American Economic Journal: Microeconomics (AEJ: Micro). It covers structuring propositions, building transparent numerical examples, creating schematic figures, and formatting tables (including mandated use of standard errors over significance asterisks) to ensure maximum clarity and adherence to academic publishing standards.

Provides a detailed guide on the mandatory pre-publication replication and verification process for American Journal of Political Science (AJPS). It instructs authors on building a robust, reproducible package—including code, data, and documentation—that an independent third party can re-run to confirm all numerical findings match the main text before publication.

This guide provides a rigorous framework for designing and interpreting quantitative case studies in academic manuscripts. It instructs authors on justifying parameter sources (real data, literature, policy), structuring comprehensive experiment matrices (effectiveness, sensitivity, boundaries, context), and moving beyond mere result description to deep mechanism-based analysis.

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.

A detailed guide for authors submitting theoretical machine learning papers (like COLT) regarding the appropriate inclusion and design of numerical simulations. It emphasizes that numerics should only visualize proved bounds, phase transitions, or separations, maintaining the focus on theoretical proofs rather than empirical data. Provides rules for plotting, reporting variability, and maintaining academic honesty.

This guide outlines the stringent standards for academic paper reproducibility in theoretical machine learning (e.g., COLT). It provides a comprehensive pre-submission audit checklist covering formal proof completeness, assumption bookkeeping, constant tracking, and ensuring numerical illustrations are fully traceable. Use this methodology to strengthen your theoretical claims and withstand rigorous peer review.

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.

Provides rigorous guidelines for analyzing experimental game data and constructing numerical examples for high-level theoretical journals. Emphasizes that analysis must be tightly linked to theoretical claims, such as matching observed outcomes to predicted equilibria, rather than serving as general causal evidence. Covers session-level clustering, structural fitting, and ensuring computational reproducibility.

This guide provides strict guidelines for authors submitting highly theoretical papers (e.g., to Journal of Economic Theory) regarding computational, numerical, or empirical evidence. It enforces the 'theory-first' principle, ensuring that all quantitative work—including simulations, examples, and counterexamples—must serve to illustrate, test, or deepen the main theoretical contribution, rather than standing as a standalone finding. Key requirements include maintaining reproducibility, keeping examples minimal, and proper structural placement.

Provides comprehensive guidelines for authors submitting to the Journal of Economic Theory (JET). It details mandatory data sharing (Elsevier Option C), ensuring computational reproducibility for numerical examples, and proper declaration of generative AI usage. This playbook helps authors structure their submissions to meet high academic standards of verifiable evidence and transparency.

A comprehensive guide for planning and stress-testing empirical analysis in financial intermediation, specifically for high-impact journals. It covers the construction of bank/loan-level panel datasets, defining variables (growth vs. levels), implementing advanced econometric specifications (e.g., fixed effects), and executing substantial robustness checks (placebo periods, alternative clustering). It also guides the planning of reproducible numerical illustrations for theoretical models.