dataviz

name	dataviz
description	Design guidance for charts, graphs, dashboards, maps, and data visualizations, including a local palette validator.
when_to_use	When creating or revising charts, graphs, dashboards, maps, plots, inline SVG, D3, Plotly, Recharts, matplotlib, or any Artifact page that visualizes data.
allowedTools	["read_file"]

Dataviz

Use this skill before producing a chart, dashboard, map, or data visualization.

Workflow

1. Identify the analytic task: comparison, trend, distribution, relationship, ranking, part-to-whole, geography, or status monitoring.
2. Choose the simplest chart form that answers that task. Read references/choosing-a-form.md when the form is not obvious.
3. Write the finding into the title, subtitle, axis label, or direct annotation. A viewer should know what changed, what is high or low, or what decision the chart supports.
4. Pick colors from references/palette.md, or validate any custom palette with the script below.
5. Check references/anti-patterns.md before finalizing the design.

Palette Validation

Resolve paths relative to the skill base directory shown above this skill body. Do not assume $QWEN_SKILL_ROOT is set for normal shell commands.

Run:

node <skill-base-directory>/scripts/validate_palette.js '#1d4ed8,#b45309,#166534' --mode light

Treat FAIL as a required palette change. Treat WARN as acceptable only when the chart also uses labels, shape, texture, ordering, or another secondary encoding.

Mark Rules

• Use categorical palettes for unordered groups; use sequential or diverging ramps only for ordered values.
• Prefer direct labels over legends when the chart has a small number of series.
• Keep gridlines subtle and fewer than the data marks.
• Avoid dual axes unless both series share a clearly explained transformation.
• Do not rely on color alone for critical distinctions.
• Keep dashboards scan-friendly: align cards, use consistent number formats, and reserve saturated color for important state changes.