visualization▌

Chart selection guidance, Python code patterns, and design principles for effective data visualizations. \n \n Comprehensive chart selection table covering 13+ chart types with guidance on when to use each and common anti-patterns to avoid (pie charts, 3D, dual-axis) \n Ready-to-use Python code examples for line charts, bar charts, histograms, heatmaps, small multiples, and interactive Plotly visualizations with professional styling \n Design principles covering color theory (sequential, divergi

Create clear, effective data visualizations via inference.sh CLI.

Data visualization transforms complex data into clear, compelling visual representations that reveal patterns, trends, and insights for storytelling and decision-making.

Comprehensive patterns for visualizing data on Mapbox maps. Covers choropleth maps, heat maps, 3D extrusions, data-driven styling, animated visualizations, and performance optimization for data-heavy applications.

Skill by ara.so — Daily 2026 Skills collection.

This skill provides a comprehensive workflow for transforming data into visual charts. It handles chart selection, parameter extraction, and image generation.

Transform and visualize interior spaces using each::sense. This skill takes photos of existing rooms and generates redesigned versions with different styles, furniture, colors, and layouts.

Intelligently selects from 26 chart types and generates visualization images from structured data. \n \n Covers 26 chart types across time series, comparisons, hierarchies, maps, relationships, and specialized visualizations (radar, funnel, sankey, network graphs, and more) \n Requires Node.js 18.0.0 or higher; generates charts by invoking a JavaScript script with JSON payload containing data, title, theme, and style parameters \n Workflow: analyze user data to select appropriate chart type, ext

Expert guidance on chart selection and data visualization design for clear data communication. \n \n Covers five core chart categories: comparison (bar/column), distribution (histograms/box plots), relationship (scatter/bubble), composition (pie/stacked bars), and trends (line/area) \n Emphasizes four foundational principles: clarity, honesty, simplicity, and accessibility for color-blind audiences \n Provides chart type recommendations with rationale, code examples using matplotlib and plotly,

Clear, effective data visualizations with chart selection rules, design principles, and storytelling techniques. \n \n Covers 10+ chart types with decision rules for when to use each (line for time series, bar for comparison, scatter for correlation, heatmap for patterns) \n Design guidelines for axes, color theory, typography, and annotations including colorblind-safe palettes and a strong stance against pie charts \n Includes ready-to-run Python/matplotlib recipes for line charts, bar charts,