data-visualization

--- name: data-visualization 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" allowed-tools: Bash(infsh *) --- # Data Visualization Create clear, effective data visualizations via [inference.sh](https://inference.sh) CLI. ## Quick Start > Requires inference.sh CLI (`infsh`). Get installation instructions: `npx skills add inference-sh/skills@agent-tools` ```bash infsh login # Generate a chart with Python infsh 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** | Hor [... prompt truncated for preview ...]