Visualization
- 64 installs
- 7 repo stars
- Updated January 15, 2026
- eyadsibai/ltk
Helps with ai & agent building tasks.
About
visualization is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- visualization
- AI & Agent Building
- AI-coding skill
Visualization by the numbers
- 64 all-time installs (skills.sh)
- Ranked #6,110 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Jul 30, 2026 (Skillselion catalog sync)
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| Installs | 64 |
|---|---|
| repo stars | ★ 7 |
| Last updated | January 15, 2026 |
| Repository | eyadsibai/ltk ↗ |
What it does
Helps with ai & agent building tasks.
Files
Data Visualization
Python libraries for creating static and interactive visualizations.
Comparison
| Library | Best For | Interactive | Learning Curve |
|---|---|---|---|
| Matplotlib | Publication, full control | No | Steep |
| Seaborn | Statistical, beautiful defaults | No | Easy |
| Plotly | Dashboards, web | Yes | Medium |
| Altair | Declarative, grammar of graphics | Yes | Easy |
---
Matplotlib
Foundation library - everything else builds on it.
Strengths: Complete control, publication quality, extensive customization Limitations: Verbose, dated API, learning curve
Key concepts:
- Figure: The entire canvas
- Axes: Individual plot area (a figure can have multiple)
- Object-oriented API:
fig, ax = plt.subplots()- preferred over pyplot
---
Seaborn
Statistical visualization with beautiful defaults.
Strengths: One-liners for complex plots, automatic aesthetics, works with pandas Limitations: Less control than matplotlib, limited customization
Key concepts:
- Statistical plots: histplot, boxplot, violinplot, regplot
- Categorical plots: boxplot, stripplot, swarmplot
- Matrix plots: heatmap, clustermap
- Built on matplotlib - use matplotlib for fine-tuning
---
Plotly
Interactive, web-ready visualizations.
Strengths: Interactivity (zoom, pan, hover), web embedding, Dash integration Limitations: Large bundle size, different mental model
Key concepts:
- Express API: High-level, similar to seaborn (
px.scatter()) - Graph Objects: Low-level, full control (
go.Figure()) - Output as HTML or embedded in web apps
---
Chart Type Selection
| Data Type | Chart |
|---|---|
| Trends over time | Line chart |
| Distribution | Histogram, box plot, violin |
| Comparison | Bar chart, grouped bar |
| Relationship | Scatter, bubble |
| Composition | Pie, stacked bar |
| Correlation | Heatmap |
| Part-to-whole | Treemap, sunburst |
---
Design Principles
- Data-ink ratio: Maximize data, minimize decoration
- Color: Use sparingly, consider colorblind users
- Labels: Always label axes, include units
- Legend: Only when necessary, prefer direct labeling
- Aspect ratio: ~1.6:1 (golden ratio) for most plots
---
Decision Guide
| Task | Recommendation |
|---|---|
| Publication figures | Matplotlib |
| Quick EDA | Seaborn |
| Statistical analysis | Seaborn |
| Interactive dashboards | Plotly |
| Web embedding | Plotly |
| Complex customization | Matplotlib |
Resources
- Matplotlib: <https://matplotlib.org/stable/gallery/>
- Seaborn: <https://seaborn.pydata.org/examples/>
- Plotly: <https://plotly.com/python/>
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