Skill UI

Astropy is a Python library for astronomical research that handles unit-aware quantities, celestial coordinates, FITS files, cosmological models, precise time systems, and tabular data, letting you transform, convert, and analyze observational or theoretical datasets.

This skill safely randomizes the order of repetitive JSON objects within an array without corrupting the data or breaking the JSON syntax. It acts as a data engineer, rigorously validating the input schema (checking for property consistency and lack of nesting) before performing the shuffle. It ensures data integrity is maintained while reordering records, making it ideal for testing, simulation, or data preparation workflows.

This guide provides a methodology for integrating theoretical models into empirical economics papers, especially for journals emphasizing empirical findings (e.g., AEJ: Applied). It teaches researchers how to ensure that theoretical components—such as mechanisms, welfare statements, or counterfactuals—serve to interpret, sharpen, or guide the existing empirical estimates, rather than leading the paper or replacing the core identification provided by the design. Focus on sufficient statistics and minimal model assumptions is key.

A comprehensive guide for authors aiming to rigorously prove the validity and robustness of their economic findings for top-tier journals. It covers three major pillars: pure theory (identifying load-bearing assumptions), structural econometrics (mapping parameters to data features), and experimental design (defining the estimand). Learn how to move beyond 'convergence' and establish true identification.

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 provides a rigorous framework for strengthening economic manuscripts submitted to top journals, such as AEJ: Micro. It details how to systematically test the robustness of main results through theoretical extensions (e.g., relaxing assumptions, perturbations) and applied/experimental methods (e.g., sensitivity analyses, placebo tests). Learn how to identify extensions that truly 'earn their place' and significantly enhance the overall academic contribution of your work.

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.

A comprehensive framework for translating reduced-form causal estimates (e.g., elasticity, treatment effects) into explicit policy statements. It guides researchers on building clear welfare, cost-benefit, or optimal-policy models, defining sufficient statistics, and articulating distributional effects, ensuring policy claims are rigorously grounded in empirical data.

This skill guides authors in assessing the suitability of their research concerning agricultural or forest ecosystems for journals focused on land–atmosphere interactions. It emphasizes that the core contribution must be a biophysical process—such as the exchange of energy, water, or carbon—rather than purely agronomic or ecological observations. Use it to re-frame your work to highlight micrometeorological processes and robust flux measurement methodologies.

This guide provides strict guidelines for designing, auditing, and reporting empirical or simulation experiments, particularly for theoretical machine learning and statistics papers. It emphasizes mapping every empirical claim to specific evidence, ensuring statistical rigor (e.g., reporting uncertainty, baselines, seeds), and validating theoretical assumptions rather than optimizing for leaderboards. Essential for research reproducibility.

A comprehensive guide for academics submitting AI and statistical research papers. It details how to map every claim (theoretical, algorithmic, or empirical) to verifiable evidence, covering necessary details like datasets, hyperparameters, random seeds, compute resources, and uncertainty estimates. It outlines the standards for achieving high levels of reproducibility, from descriptive to turnkey, ensuring scientific rigor for top-tier conferences.

A comprehensive guide for strengthening the methodological rigor of academic manuscripts, particularly for high-stakes journals. It covers advanced techniques for causal identification (e.g., Difference-in-Differences, Instrumental Variables, Regression Discontinuity), pre-analysis planning, linking theory to empirical tests, and defending claims against skeptical reviewers. This skill focuses on methodological architecture, not code.