Skill UI

A comprehensive guide designed to help authors determine the scope fit, required methodology, and stylistic conventions for submitting manuscripts to Physical Review D (PRD). It covers high-energy physics, cosmology, gravitation, and particle astrophysics, ensuring the work is positioned correctly for an archival academic audience.

Provides comprehensive guidelines for conducting highly rigorous, reproducible statistical analysis for complex survey data (e.g., weights, strata, clusters). This guide ensures that analyses meet the standards of top academic journals, emphasizing design-based inference, honest uncertainty reporting (CIs, MOE), and thorough robustness checks. Use this when you need to build a master script that regenerates every published table and figure.

This guide provides expert guidelines for designing high-impact, concise figures specifically for academic publications with strict length limits (e.g., PRL). It emphasizes that every figure must convey a single, central result at a glance. The strategy covers lead figure principles, print standards (single-column legibility, color-blind safety), and panel discipline to ensure maximum clarity and minimal word count consumption.

This guide details best practices for creating high-impact tables and figures for psychological science manuscripts, adhering to APA 7th edition guidelines. It emphasizes showing effect sizes, uncertainty (like confidence intervals), and embedding visuals directly within the main text near their first discussion, moving beyond traditional end-of-document placement. It covers data visualization principles and manuscript triage.

A comprehensive guide for designing and documenting systematic literature searches for meta-analyses or systematic reviews. It ensures reproducibility by covering multiple databases (e.g., PsycINFO, PubMed), controlled vocabulary, grey literature, and adhering strictly to PRISMA-S guidelines. It helps build a robust, traceable research pool.

Provides comprehensive guidelines for designing tables and figures (exhibits) for academic manuscripts. The focus is on creating self-contained, highly accessible visualizations that effectively communicate quantitative effect magnitude and uncertainty to both specialized scholars and public practitioners, optimizing impact for publication.

A comprehensive skill designed to help users assess the suitability of their economics or management manuscript for 'Research on Financial and Economic Issues'. It provides detailed guidelines on journal positioning, submission requirements, thematic preferences, and common rejection pitfalls, ensuring authors align their work with the journal's scope and style.

Provides comprehensive guidelines for designing high-impact scientific figures for major academic journals. It covers technical aspects like optimal sizing, font legibility, and colorblind safety, while also enforcing strict data visualization practices (e.g., using dot plots over bars, defining 'n', ensuring data integrity). Use this as a final quality check before submission to guarantee scientific rigor and maximize publication chances.

This skill provides comprehensive guidelines for conducting and reporting high-stakes quantitative data analysis in the social sciences. It emphasizes methodological rigor, requiring honest reporting of uncertainty, probing robustness (specs that could break the result), and ensuring full reproducibility. It covers advanced topics like design-based inference, cluster corrections, and handling missing data, ensuring results withstand expert review.

This skill provides comprehensive guidance for authors submitting highly rigorous theoretical computer science manuscripts to top-tier venues like SIAM Journal on Computing (SICOMP). It covers required standards for algorithmic design, computational complexity, formal proofs, house style, and the high bar for theoretical contribution necessary for foundational academic publishing.

Provides expert guidelines for creating high-rigor methodological exhibits for the Sociological Methods & Research (SMR) journal. Focuses on constructing self-contained simulation grids, method-comparison tables, and diagnostic figures that clearly communicate methodological arguments, ensuring reviewers can audit claims directly without relying on surrounding prose.

This guide provides comprehensive best practices for designing and auditing cloud system evaluations, particularly for academic submissions. It mandates that claims must be supported by measured evidence from real or realistic deployments, moving beyond simple simulations. Key metrics emphasized include tail latency (p99), operational cost modeling, and verifiable behavior under scale and multi-tenancy.