data-visualization
Select chart types and design data encodings — marks, axes, labels, and accessible chart styling. Use when presenting data graphically. Owns chart selection and encoding only; the categorical colour ramp itself belongs to color-system.
- Installs
- 2.4K
- Last 24h
- +24
- Trending rank
- #5102
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/owl-listener/designer-skills --skill data-visualization- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads data-visualization automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Data Visualization
You are an expert in designing clear, accessible, and informative data visualizations.
What You Do
You design data visualizations that communicate insights effectively using appropriate chart types and styling.
Chart Selection
Comparison
Bar charts (categorical), grouped bars (multi-series), bullet charts (target vs actual).
Trend Over Time
Line charts (continuous), area charts (volume), sparklines (inline).
Part of Whole
Pie/donut (few categories), stacked bar (many categories), treemap (hierarchical).
Distribution
Histogram, box plot, scatter plot.
Relationship
Scatter plot, bubble chart, heat map.
Design Principles
- Data-ink ratio: maximize data, minimize decoration
- Clear axis labels and legends
- Consistent color encoding across views
- Start y-axis at zero for bar charts
- Use annotation to highlight key insights
Color in Data Viz
- Sequential: light to dark for ordered data
- Diverging: two-hue scale for above/below midpoint
- Categorical: distinct hues for unrelated categories
- Colorblind-safe palettes (avoid red-green only)
Accessibility
- Don't rely on color alone — use patterns, labels, or shapes
- Provide text alternatives for charts
- Keyboard navigable interactive charts
- Sufficient contrast for data elements
Responsive Data Viz
- Simplify at small sizes (fewer data points, larger labels)
- Consider alternative views for mobile (table instead of chart)
- Touch-friendly tooltips and interactions
Best Practices
- Choose the simplest chart that communicates the insight
- Label directly on the chart when possible (avoid legends)
- Provide context (benchmarks, targets, trends)
- Test with real data, not idealized samples
- Allow users to explore details on demand
FAQ
What does the data-visualization skill do?
Select chart types and design data encodings — marks, axes, labels, and accessible chart styling. Use when presenting data graphically. Owns chart selection and encoding only; the categorical colour ramp itself belongs to color-system.
How do I install data-visualization?
Run npx skills add https://github.com/owl-listener/designer-skills --skill data-visualization in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/data-visualization/ (personal) or .claude/skills/data-visualization/ (project).
Which AI agents can use data-visualization?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is data-visualization safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains data-visualization?
It is published by owl-listener in owl-listener/designer-skills. It has 2,384 recorded installs.
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