data-visualization Skill
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory. Published by anthropics in knowledge-work-plugins.
What is data-visualization Skill?
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory. Published by anthropics in knowledge-work-plugins. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Design and media
- Data analysis use cases
- Design and media use cases
Technical details
- Install or run with Copy skill directory
When to use data-visualization Skill
- Use it for data analysis.
- Use it for design and media.
Built with
Editorial notes
Source
- Creator: anthropics
- Repository: anthropics/knowledge-work-plugins
- Skill file: data/skills/data-visualization/SKILL.md
What it does
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Skill instructions
Data Visualization Skill Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations. Chart Selection Guide Choose by Data Relationship | What You're Showing | Best Chart | Alternatives | |---|---|---| | Trend over time | Line chart | Area chart (if showing cumulative or composition) | | Comparison across categories | Vertical bar chart | Horizontal bar (many categories), lollipop chart | | Ranking | Horizontal bar chart | Dot plot, slope chart (comparing two periods) | | Part-to-whole composition | Stacked bar chart | Treemap (hierarchical), waffle chart | | Composition over time | Stacked area chart | 100% stacked bar (for proportion focus) | | Distribution | Histogram | Box plot (comparing groups), violin plot, strip plot | | Correlation (2 variables) | Scatter plot | Bubble chart (add 3rd variable as size) | | Correlation (many variables) | Heatmap (correlation matrix) | Pair plot | | G
Explore related resources
Frequently asked questions
What is data-visualization?
data-visualization is a open-source AI agent skill with Copy skill directory. Create effective data visualizations with Python (matplotlib, seaborn, plotly).
Who is data-visualization best for?
data-visualization is best for reusing agent instructions, scripts, and references, data analysis workflows, design and media workflows.
How do I install data-visualization?
Install or run data-visualization using Copy skill directory. Check data-visualization for the latest setup command.
Is data-visualization actively maintained?
data-visualization may need a closer maintenance check before production use.
Auto-fetched from GitHub.
