Skill UI

Create truthful, accessible, publication-ready scientific figures using Matplotlib, Seaborn, or Plotly. Ensures data integrity, proper encoding, accessibility compliance, and journal export planning for research publications. Includes guidelines for uncertainty displays, color/contrast review, and metadata validation.

An expert skill designed to author comprehensive, implementation-ready design system guidelines for editorial content like books, magazines, and reports. It structures visual language across tokens, typography, spacing, and component states, while rigorously adhering to WCAG accessibility standards and defining clear content tone and anti-patterns for developers and designers.

This comprehensive tool runs content through six critical scoring dimensions: content quality, brand voice, hallucination risk, claim verification, output structure, and readability. It generates a composite score, flags specific issues with actionable fix suggestions, and ensures compliance against defined brand guidelines. Essential for pre-publication reviews, client sign-offs, and continuous quality tracking.

A comprehensive set of guidelines for generating maps, figures, and tables for academic journals. This guide emphasizes cartographic standards, statistical rigor, and maximizing the visual argument, ensuring that all exhibits are reproducible, legible in print, and adhere to strict word count limitations. It covers everything from projection choice and data normalization to model plot interpretation and handling spatial uncertainty.

Provides comprehensive guidelines on structuring academic manuscripts for Acta Automatica Sinica (AAS). Learn how to effectively separate core contributions (main text) from supporting details, such as lengthy proofs, supplementary simulations, and extensive datasets, ensuring the paper remains concise and highly readable while maintaining scientific completeness for reviewers.

A comprehensive guide for authors responding to reviewer reports and editor decisions (major/minor revision, rejection). It details how to structure a point-by-point rebuttal letter, what new data (e.g., stability, benchmarking) must be added, and how to ensure the revised manuscript maintains a clear scientific advance while adhering to publication guidelines.

This guide provides rigorous methodological guidelines for ensuring the identification strategy in high-impact empirical economics papers (AER: Insights). It covers key areas including Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), Instrumental Variables (IV), and structural parameter identification. It emphasizes the need for extreme methodological clarity, transparency, and conciseness required for top-tier academic publication.

A comprehensive guide for designing academic exhibits—including maps, photographs, artifact images, and transcripts—for anthropological manuscripts. It emphasizes making exhibits self-contained, ensuring ethical clearance (consent, permissions), and structuring the visual argument to advance scholarly interpretation while adhering to publication guidelines and word budgets.

Provides comprehensive guidelines for authors conducting systematic reviews and meta-analyses in psychology. It emphasizes documenting the literature search protocol, ensuring quantitative synthesis rigor (PRISMA flow, effect-size data), and making all findings open and reproducible for high-impact academic publication.

This guide provides comprehensive standards for designing, quantifying, and narrating scientific figures and tables for high-impact publications. It details best practices for maintaining image integrity (e.g., uncropped blots, linear adjustments), ensuring all data points are quantified across replicates, and structuring the visual evidence to advance a clear, mechanistic argument step-by-step.

Provides comprehensive guidelines for authors preparing high-impact front matter for leading cancer journals (e.g., Cancer Cell). It details the structure and content requirements for the Summary, Highlights, eTOC blurb, and Graphical Abstract, ensuring mechanistic accuracy, impact, and adherence to publication standards.

This guide outlines the standardized requirements for declaring data and code availability in major scientific publications. It details mandatory best practices, including depositing raw data (e.g., sequencing, structures) and analysis code into approved, citable public repositories (such as GEO, PDB, Mendeley Data, and Zenodo). It also provides the required standardized three-bullet statement format for ensuring reproducibility.