Generate Vega-Lite and Vega charts from structured records with valid JSON specifications.
npx skills add https://github.com/aryanshrai03/readme-ai --skill vega-aryanshrai03
Please help me install this Agent Skill. Skill: vega Source: https://github.com/aryanshrai03/readme-ai/tree/main/api/skills/vega Command: npx skills add https://github.com/aryanshrai03/readme-ai --skill vega-aryanshrai03
Vega and Vega-Lite specs turn your raw numeric and categorical data into clear, reusable visualizations so you can explore trends and communicate results without manual chart tinkering.
Create a Vega-Lite chart by structuring your data as an array of objects, selecting an appropriate mark (bar, line, point, area, rect, or arc), mapping fields to encodings (x, y, color, size), and wrapping the resulting spec in a vega-lite code block while including the required $schema.
High-intent search queries and answers about installing and using this skill.
You can generate data visualizations from structured JSON records by declaring a Vega-Lite or Vega chart specification, mapping your data fields to visual encodings like x, y, and color to produce statistical charts.
Vega-Lite is best for standard statistical charts like bar, line, and scatter plots, while Vega provides programmatic control for advanced layouts and custom visuals like radar charts and word clouds.
To map data fields to visual encodings in a Vega-Lite specification, select a mark type like bar or point, then assign your data fields to encoding channels such as x, y, color, and size within the JSON schema.
Your Vega chart specification likely fails to render due to invalid JSON syntax, incorrect $schema declaration, case-sensitive field name mismatches, or unsupported data types in the encoding channels.
Yes, you can build heatmap and dual-axis compositions using Vega-Lite by applying the correct mark types like rect and mapping your data fields to the appropriate encoding channels within the JSON specification.
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