Skill UI

Transforms raw datasets into charts, plots, and graphs by analyzing structure, picking suitable visualization types, and rendering them clearly so users can request visualizations or explore patterns and relationships.

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

PyGraphistry is a Python interface to the Graphistry platform for GPU-accelerated graph visualization, letting analysts render millions of nodes/edges in interactive dashboards to surface anomalies and relationships quickly.

Plotly is a powerful Python graphing library designed to create highly interactive, publication-quality visualizations. It supports over 40 chart types, including scatter plots, heatmaps, candlestick charts, and geographical maps. Ideal for building sophisticated dashboards, conducting deep exploratory data analysis, and creating rich, web-embeddable presentations with features like zooming, panning, and hover tooltips.

Guides Power BI visualization and UX decisions—capturing business context, selecting optimal charts, specifying layouts, color, typography, and interaction patterns to create accessible, insight-driven dashboards across audiences.

Matplotlib is a foundational Python library for creating static, animated, and interactive plots with full customization. It supports various plot types like line, scatter, bar, and heatmaps, suitable for scientific and statistical visualizations. Users can customize appearance, create multi-panel figures, and export to PNG, PDF, or SVG. Ideal for publication-quality graphics and integrating with Jupyter notebooks.

Seaborn is a Python visualization library built on matplotlib for creating statistical graphics. It integrates with pandas for dataset-oriented plotting, offering publication-quality defaults for distributions, relationships, and categorical comparisons. Ideal for exploratory data analysis, multivariate analysis, and complex multi-panel figures. Supports various plot types like box plots, violin plots, and heatmaps.

This advanced skill is an end-to-end tool for transforming complex research topics into professional, visualized knowledge graph PDFs. It automatically conducts multi-round web searches, extracts key entities, structures related knowledge, and designs the optimal visualization (supporting Graphviz, PlantUML, etc.). Ideal for deep academic analysis, mapping character relationships, or visualizing complex technical architectures where existing structured data is unavailable.

Generates sophisticated, editorial-grade data visualizations inspired by major news publications like The New York Times. Ideal for presenting complex data (CSV/JSON) or key conclusions as single frames or animations for video explainers, social media posts, or data reports. Features clean design, specific typography, and staggered reveal animations.

A comprehensive guide for creating self-contained, accessible, and budget-compliant scientific exhibits (figures, tables, and maps) for manuscripts, particularly in conservation biology. It covers best practices for data visualization, adhering to journal standards, ensuring accessibility (e.g., grayscale compatibility), and masking sensitive geographical data.

A comprehensive guide for authors designing figures and tables for Nature Geoscience manuscripts. It outlines strict rules for adhering to a limited figure budget (4-6 display items), ensuring the lead figure immediately conveys the core Earth-system advance and its uncertainty, and maintaining high standards of cartography and data visualization for non-specialist readership.

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.