What problem does it solve? Chat responses with dense data β tables, metrics, audit findings β are hard to read as plain markdown. This Skill lets the agent render analytical output as a live React app (a .canvas.tsx file) that opens beside the chat in Cursor, turning raw data into charts, tables, and interactive layouts. ## Core Features & Use Cases - Standalone Analytical Artifacts: Renders quantitative analyses, billing investigations, security audits, and architecture reviews as dedicated visual canvases instead of markdown dumps. - Rich Component SDK: Provides typed primitives via cursor/canvas β BarChart, LineChart, PieChart, Table, Stat, Card, DiffView, UsageBar, form controls, and theme tokens β with full TypeScript declarations in sdk/. - Design Guardrails: Enforces flat, minimal styling (no gradients, emojis, or box-shadows), mandatory chart labeling, and a pre-delivery self-check to prevent low-quality output. - Use Case: After querying Datadog or Databricks via MCP tools, the agent renders the results as a labeled multi-series line chart with stats and tables in a canvas the user opens beside the chat. ## Quick Start Ask the agent to analyze your service error rates over the last week and present the findings as a canvas.