Skill UI

A guide for structuring the theoretical framework and hypotheses section of a psychological science manuscript. It emphasizes academic rigor, research transparency, and the necessity of clearly separating confirmatory (preregistered) predictions from purely exploratory findings. Learn how to state a clear mechanism, derive directional hypotheses, and avoid 'HARKing' to strengthen your scientific argument.

Provides rigorous statistical guidance for conducting meta-analyses in academic settings. This skill covers the calculation of standardized effect sizes (e.g., Hedges' g, log odds ratio), selecting appropriate models (defaulting to random-effects or multilevel models), and accurately quantifying between-study heterogeneity (Q, I², τ², and prediction intervals). It ensures the analysis meets state-of-the-art standards for high-impact psychological journals.

This skill guides researchers in transforming raw meta-analysis results (e.g., pooled effects, moderator analyses) into a genuine, high-impact theoretical contribution suitable for integrative review journals. It teaches how to interpret conflicting findings, build or refine theoretical models, clearly state scope conditions (boundary conditions), and set concrete future research agendas, ensuring the findings advance the field beyond mere statistics.

A comprehensive guide for defining the scope, limitations, and identifiability of a scientific theory. It instructs researchers on how to explicitly state what phenomena a theory explains (in scope), where it fails (out of scope), and how to address structural identifiability for formal models, thereby enhancing the theory's falsifiability and scientific rigor.

A comprehensive guide and style checker for revising applied economics manuscripts (e.g., for REStat). It ensures the prose is 'empirical-first,' immediately presenting the applied question, the headline estimate (with units and SE), and the design. It mandates a concise abstract (≤100 words) that clearly states the finding with a number, moving the writing away from vague, passive academic language toward plain, quantitative prose.

This guide provides a rigorous framework for stress-testing and implementing state-of-the-art causal identification strategies required for high-impact empirical economics. It covers advanced methodologies including Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), Synthetic Control, and Structural Modeling. It emphasizes modern estimators (e.g., Callaway-Sant'Anna, effective F) and robust diagnostic checks to ensure the causal claims are methodologically sound and defensible.

This skill is designed to help authors evaluate the suitability of submitting an authoritative, comprehensive review article to Reviews of Geophysics. It guides the framing of high-impact syntheses across Earth and space sciences, focusing on integrating disparate subdisciplines, defining the state-of-the-art, and articulating major open research questions.

This guide details the comprehensive policy for preparing research replication packages for academic journals, especially in finance. It covers structuring code, handling proprietary data (pseudo-datasets, logs), drafting Data Availability Statements, and ensuring full traceability of results for successful publication.

This specialized tool analyzes Git diffs against a stated development intent to detect scope creep. It identifies overly broad pull requests, unrelated files, dependency additions, public API renames, or configuration changes. It guides users on whether changes should be kept, split into separate commits, or if the connection requires explicit justification, ensuring clean and focused code merges.

Provides comprehensive guidelines for authors preparing manuscripts for high-level academic publication, focusing specifically on data availability and transparency. It guides users through drafting the required data availability statement, depositing data and code in public repositories (e.g., OSF, Zenodo), and methods for documenting restricted or qualitative sources to ensure maximum reproducibility and ethical compliance.

A comprehensive guide for structuring research papers, particularly in computer graphics and visualization fields (like SIGGRAPH). It dictates how to match technical claims (e.g., 'higher quality,' 'faster') to verifiable evidence, including head-to-head comparisons against State-of-the-Art (SOTA), detailed metric reporting (PSNR, SSIM, Hausdorff), performance timing linked to specific hardware, and robust user studies. Essential for achieving academic rigor and pre-empting domain reviewer objections.

A comprehensive guide for authors revising performance-intensive academic papers, such as those for SIGMETRICS. It emphasizes the need to state a precise performance contribution on the first page, explicitly detail all modeling assumptions, and pair every quantitative claim with proportional evidence (proofs, simulations, and measurements).