agentleFS
Sign inStart free
NikoxkxNikoxkx/Agent-Skills/skills/data-analytics/visualization-generator/SKILL.md

visualization-generator

Skill1 starsChanged 5 months agoApache-2.0View on GitHub

Creates data visualizations using matplotlib, seaborn, plotly, or D3.js. Use when generating charts, graphs, dashboards, or exploratory data plots.

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use seaborn.set_theme() or a custom style.

    line 41
  2. Distinctive:

    Avoid 3D, excessive animation, pie charts for >5 categories.

    line 65
  3. Distinctive:

    All with proper titles, labels, colorblind palettes, and export code.

    line 79
  4. Distinctive:

    Run the code — chart renders without error.

    line 90

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Overview

Generates appropriate, accessible, and publication-quality data visualizations. Includes a chart type selection guide, matplotlib/seaborn best practices, Plotly Express quick interactive charts, D3.js patterns for custom web visualizations, colorblind-safe palettes, proper labeling, and export options (PNG, SVG, HTML, interactive).

When to Use This Skill

  • Exploratory data analysis.
  • Creating reports, dashboards, or presentations.
  • The user asks for "a chart of...", "visualize this data", "plot the trend", etc.
  • Need interactive web charts or static figures for papers/slides.

Prerequisites

  • Data in a pandas DataFrame, list of dicts, or JSON/CSV.
  • Python environment with matplotlib, seaborn, plotly (or Node for D3).
  • Understanding of the story the visualization should tell.

Steps

  1. Chart type selection (use the guide):
    • Comparison: bar, column, grouped bar.
    • Trend over time: line, area.
    • Distribution: histogram, box, violin, density.
    • Relationship: scatter, bubble, heatmap.
    • Composition: stacked bar, pie (use sparingly), treemap, sunburst.
    • Geospatial: choropleth, point map.
  2. Matplotlib / Seaborn setup:
    • Use seaborn.set_theme() or a custom style.
    • Figure size appropriate for medium (6x4 to 10x6 inches for print).
    • Clear titles, axis labels, units.
    • Legend only when necessary; direct labels when possible.
  3. Color:
    • Colorblind-safe palettes (seaborn colorblind, ColorBrewer, viridis, cividis).
    • Consistent colors across related charts.
    • High contrast for important categories.
  4. Plotly Express (for interactive / dashboards):
    • px.bar, px.line, px.scatter, px.histogram, etc.
    • Faceting, animation, hover templates.
    • Export to HTML for embedding.
  5. D3.js (for custom web viz):
    • Data join pattern (selectAll().data().join()).
    • Scales, axes, transitions.
    • Provide a minimal self-contained example (index.html + script).
  6. Accessibility & clarity:
    • Sufficient contrast.
    • Text labels (not just color or position).
    • Alt text or aria descriptions for web charts.
    • Avoid 3D, excessive animation, pie charts for >5 categories.
  7. Output:
    • Complete, runnable code for the chosen chart(s).
    • The figure saved to file or displayed.
    • Interactive HTML version when using Plotly.
    • Notes on how to update with new data.

Examples

  • Seaborn bar + line combo for sales by category over time.
  • Plotly interactive scatter with hover and animation by year.
  • D3.js bar chart with tooltips and responsive sizing.
  • Heatmap for correlation matrix.
  • All with proper titles, labels, colorblind palettes, and export code.

Edge Cases & Error Handling

  • Very large datasets: Sample or use datashader / WebGL in Plotly.
  • Many categories: Group "other" or use a treemap/sunburst.
  • Negative + positive values: Diverging color scale.
  • Time series with gaps: Connect or show gaps explicitly.

Verification

  1. Run the code — chart renders without error.
  2. Visual inspection: labels readable, no overlapping text, colors distinguishable.
  3. Colorblind simulation (e.g., Coblis or browser extension) — still interpretable.
  4. For interactive: hover, zoom, legend toggle work.
  5. Export and re-open the image/SVG/HTML — quality preserved.
  6. Success: The visualization accurately and clearly communicates the intended insight.

References

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free