Data Visualization

Copyright (c) 2026 KunAgent. Licensed under the MIT License.

Purpose

Design and generate accurate statistical visualizations with readable scales, labels, annotations, and source context.

Tool routing

Tool or skill	Use
render_chart	Render governed charts directly from structured data.
bash	Run Python visualization code for file-based figures.
design_create_diagram	Create chart-like explanatory diagrams on canvas.

Workflow

1. Identify the analytical question and comparison structure.
2. Validate data types, units, missing values, and aggregation.
3. Select the simplest chart that preserves the relationship.
4. Use direct labels, honest scales, accessible color, and source notes.
5. Export at the requested dimensions and inspect the result.

Completion gates

• Check totals, ordering, axes, units, legends, and annotations against source data.
• Use zero baselines where magnitude comparison requires them.
• Avoid dual axes unless the relationship and scale are unambiguous.

Boundaries

• Do not smooth, truncate, or aggregate in ways that change the conclusion without disclosure.
• Do not use decorative charts when a table or metric is clearer.

Delivery

Lead with the outcome, name the evidence used for verification, and disclose any real limitation that remains.