data-visualization-color
Sub-skill of data-visualization: Color (+3).
Best use case
data-visualization-color is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of data-visualization: Color (+3).
Teams using data-visualization-color should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/color/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-visualization-color Compares
| Feature / Agent | data-visualization-color | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of data-visualization: Color (+3).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Color (+3) ## Color - **Use color purposefully**: Color should encode data, not decorate - **Highlight the story**: Use a bright accent color for the key insight; grey everything else - **Sequential data**: Use a single-hue gradient (light to dark) for ordered values - **Diverging data**: Use a two-hue gradient with neutral midpoint for data with a meaningful center - **Categorical data**: Use distinct hues, maximum 6-8 before it gets confusing - **Avoid red/green only**: 8% of men are red-green colorblind. Use blue/orange as primary pair ## Typography - **Title states the insight**: "Revenue grew 23% YoY" beats "Revenue by Month" - **Subtitle adds context**: Date range, filters applied, data source - **Axis labels are readable**: Never rotated 90 degrees if avoidable. Shorten or wrap instead - **Data labels add precision**: Use on key points, not every single bar - **Annotation highlights**: Call out specific points with text annotations ## Layout - **Reduce chart junk**: Remove gridlines, borders, backgrounds that don't carry information - **Sort meaningfully**: Categories sorted by value (not alphabetically) unless there's a natural order (months, stages) - **Appropriate aspect ratio**: Time series wider than tall (3:1 to 2:1); comparisons can be squarer - **White space is good**: Don't cram charts together. Give each visualization room to breathe ## Accuracy - **Bar charts start at zero**: Always. A bar from 95 to 100 exaggerates a 5% difference - **Line charts can have non-zero baselines**: When the range of variation is meaningful - **Consistent scales across panels**: When comparing multiple charts, use the same axis range - **Show uncertainty**: Error bars, confidence intervals, or ranges when data is uncertain - **Label your axes**: Never make the reader guess what the numbers mean
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