# Data Visualization

Data visualization with chart selection, color theory, and annotation best practices.

- Repository: https://github.com/inference-sh/skills (795 stars)
- Category: Design
- License: see repository
- Last updated: 2026-10-15
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add inference-sh/skills --skill data-visualization -g -a claude-code -y`
Codex: `npx skills add inference-sh/skills --skill data-visualization -g -a codex -y`
Cursor: `npx skills add inference-sh/skills --skill data-visualization -g -a cursor -y`
Gemini CLI: `npx skills add inference-sh/skills --skill data-visualization -g -a gemini-cli -y`

## Description

Data visualization with chart selection, color theory, and annotation best practices. Covers chart types (bar, line, scatter, heatmap), axes rules, and storytelling with data. Use for: charts, graphs, dashboards, reports, presentations, infographics, data stories. Triggers: data visualization, chart, graph, data chart, bar chart, line chart, scatter plot, data viz, visualization, dashboard chart, infographic data, data presentation, chart design, plot, heatmap, pie chart alternative

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

> **Install the belt CLI skill:** `npx skills add belt-sh/cli`

# Data Visualization

Create clear, effective data visualizations via [inference.sh](https://inference.sh) CLI.

## Quick Start

> Requires inference.sh CLI (`belt`). [Install instructions](https://raw.githubusercontent.com/inference-sh/skills/refs/heads/main/cli-install.md)

```bash
belt login

# Generate a chart with Python
belt app run infsh/python-executor --input '{
  "code": "import matplotlib.pyplot as plt\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\nmonths = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\"]\nrevenue = [42, 48, 55, 61, 72, 89]\n\nfig, ax = plt.subplots(figsize=(10, 6))\nax.bar(months, revenue, color=\"#3b82f6\", width=0.6)\nax.set_ylabel(\"Revenue ($K)\")\nax.set_title(\"Monthly Revenue Growth\", fontweight=\"bold\")\nfor i, v in enumerate(revenue):\n    ax.text(i, v + 1, f\"${v}K\", ha=\"center\", fontweight=\"bold\")\nplt.tight_layout()\nplt.savefig(\"revenue.png\", dpi=150)\nprint(\"Saved\")"
}'
```


## Chart Selection Guide

### Which Chart for Which Data?

| Data Relationship | Best Chart | Never Use |
|------------------|-----------|-----------|
| **Change over time** | Line chart | Pie chart |
| **Comparing categories** | Bar chart (horizontal for many categories) | Line chart |
| **Part of a whole** | Stacked bar, treemap | Pie chart (controversial but: bar is always clearer) |
| **Distribution** | Histogram, box plot | Bar chart |
| **Correlation** | Scatter plot | Bar chart |
| **Ranking** | Horizontal bar chart | Vertical bar, pie |
| **Geographic** | Choropleth map | Bar chart |
| **Composition over time** | Stacked area chart | Multiple pie charts |
| **Single metric** | Big number (KPI card) | Any chart (overkill) |
| **Flow / process** | Sankey diagram | Bar chart |

### The Pie Chart Problem

Pie charts are almost always the wrong choice:

The rest: https://github.com/inference-sh/skills/blob/HEAD/guides/design/data-visualization/SKILL.md