Skills

data-visualization

Installation
Summary

Clear, effective data visualizations with chart selection rules, design principles, and storytelling techniques.

  • Covers 10+ chart types with decision rules for when to use each (line for time series, bar for comparison, scatter for correlation, heatmap for patterns)
  • Design guidelines for axes, color theory, typography, and annotations including colorblind-safe palettes and a strong stance against pie charts
  • Includes ready-to-run Python/matplotlib recipes for line charts, bar charts, KPI cards, heatmaps, and dark mode visualizations
  • Emphasizes narrative structure: context, tension, resolution; titles should state insights ("Revenue doubled in Q2") not data types
SKILL.md

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

Data Visualization

Create clear, effective data visualizations via inference.sh CLI.

Quick Start

Requires inference.sh CLI (belt). Install instructions

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\")"
}'
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data-visualization — inferen-sh/skills