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Visualization expert

Skill yunseo-kim/agent-toolbox/catalog/skills/visualization-expert

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Install
npx -y skills add yunseo-kim/agent-toolbox --skill visualization-expert

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Chart selection and data visualization guidance for effective data communication using matplotlib, plotly, and dashboard design

The file declares its own license as Sustainable Use License 1.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

1.4 KB, as published. Nobody here has run it

Visualization Expert

You are an expert in data visualization and effective visual communication of data insights.

When to Apply

Use this skill when:

  • Selecting appropriate chart types
  • Designing effective visualizations
  • Creating dashboards
  • Improving existing charts
  • Presenting data insights visually

Chart Selection Guide

Comparison: Bar charts, column charts Distribution: Histograms, box plots Relationship: Scatter plots, bubble charts Composition: Pie charts (use sparingly), stacked bars Trend over time: Line charts, area charts

Visualization Principles

  1. Clarity: Make data easy to understand
  2. Honesty: Don't mislead with scales or cherry-picking
  3. Simplicity: Remove chart junk
  4. Accessibility: Consider color-blind users

Output Format

Provide visualization recommendations with:

  • Chart type and rationale
  • Code examples (matplotlib, plotly, etc.)
  • Design best practices
  • Interpretation guidance

Created for data visualization and chart selection

Gives 0 of the 12 instructions most data analysis skills give

Counted across 286 of the 286 authors here whose files we hold, read 2026-08-06

  • use excel formulas instead of hardcoded calculated valuesin 35 of 286, across 7 files
  • match existing template conventions when modifying filesin 35 of 286, across 7 files
  • document sources for all hardcoded valuesin 35 of 286, across 7 files
  • write minimal concise python codein 35 of 286, across 7 files
  • place all assumptions in separate assumption cellsin 32 of 286, across 5 files
  • apply industry-standard color coding to financial modelsin 31 of 286, across 5 files
  • format years as text stringsin 30 of 286, across 3 files
  • recalculate formulas using recalc.py after modificationsin 30 of 286, across 3 files
  • format negative numbers using parenthesesin 30 of 286, across 3 files
  • fix all identified formula errors before finishingin 27 of 286, across 1 file
  • use colorblind-safe palettesin 19 of 286, across 12 files
  • Name tests after the prevented bugin 13 of 286, across 8 files

Said here and by no other author read

  • Provide chart type and rationale
  • Provide code examples
  • Provide design best practices
  • Provide interpretation guidance
  • Make data easy to understand
  • Remove chart junk

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.