Agent skill · data analytics · aj-geddes
Data Visualization
Create effective visualizations using matplotlib and seaborn for exploratory analysis, presenting insights, and communicating findings with business stakeholders
Why this skill is useful
Provides specific implementation examples and visualization techniques using matplotlib and seaborn that enhance the AI's ability to generate effective visualizations.
What it needs
Requires matplotlib, numpy, pandas, seaborn installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Data Visualization Overview Data visualization transforms complex data into clear, compelling visual representations that reveal patterns, trends, and insights for storytelling and decision-making. When to Use Exploratory data analysis and pattern discovery Communicating insights to stakeholders Comparing distributions and relationships Presenting findings in reports and dashboards Identifying outliers and anomalies visually Creating publication-ready charts and graphs Visualization Types Distributions: Histograms, KDE, violin plots Relationships: Scatter plots, line plots, heatmaps Comparisons: Bar charts, box plots, ridge plots Compositions: Pie charts, stacked bars, treemaps Temporal: Line plots, area charts, time series Multivariate: Pair plots, correlation heatmaps Design Principles Choose appropriate chart type for data Minimize ink-to-data ratio Use color purposefully Label clearly and completely Maintain consistent scales Consider accessibility Implementation with Python Visualization Best Practices Choose chart type based on data type and question Use consistent color schemes Label axes clearly with units Include title and legend Avoid 3D charts when 2D suffices Make fonts large and readable Consider colorblind-friendly palettes Common Chart Types Bar charts: Categorical comparisons Line plots: Trends over time Scatter plots: Relationships between variables Histograms: Distributions Heatmaps: Matrix data Box plots: Distribution with quartiles Deliverables Exploratory visualizations Publication-ready charts Interactive dashboard mockups Statistical plots with annotations Trend analysis visualizations Comparative analysis charts Summary infographics
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/data-visualization