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viz

Generate and save matplotlib/seaborn plots from data contexts.

Updated Aug 23, 2026
Search Tags:#plot#matplotlib#artifact#dataframe#notebook#marimo#visualize
One-click install
npx skills add https://github.com/robdmc/claude_tools --skill viz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: viz
Source: https://github.com/robdmc/claude_tools/tree/main/viz/skills
Command: npx skills add https://github.com/robdmc/claude_tools --skill viz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill enables quick, automated visualizations directly from your data context, whether it's a file, a notebook, or a live DataFrame, without leaving the chat.

Core Features & Use Cases

  • Build plots with matplotlib/seaborn from absolute data references.
  • Inspect DataFrames (columns, dtypes, first N rows) and manage artifacts in the .viz/ directory.
  • Marimo notebook support: extract and plot data from marimo notebooks while preserving the original notebook context.

Quick Start

Provide a visualization spec and data context to generate and save a plot. For example, show a line chart of a time series from /path/to/data.parquet.

Frequently Asked Questions about viz

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate and save matplotlib plots from a DataFrame without leaving my chat environment?▼

To visualize data from a DataFrame, you provide a visualization spec and data context so the skill generates matplotlib or seaborn charts and saves them as artifacts in the .viz/ directory for reuse and sharing.

Can I extract and plot data from a marimo notebook while preserving the original notebook context?▼

Yes, marimo notebook support allows you to extract data directly from the notebook context, generate plots, and manage the resulting artifacts in the .viz/ directory without altering the original notebook.

What Python environment and plotting libraries do I need to generate charts from data files?▼

Generating charts requires a Python environment with plotting libraries available, specifically matplotlib, seaborn, and pandas for DataFrame support, which are used to build and save your visualizations.

How do I inspect DataFrame columns and data types before creating a visualization?▼

You can inspect DataFrames by checking their columns, dtypes, and first N rows as part of the data context before generating and saving your plot, ensuring your visualization spec matches the underlying data structure.

What is the best way to manage and share automated visualizations created from data references?▼

The best way to manage and share automated visualizations is by saving generated plots as artifacts in the .viz/ directory, which organizes your charts for reuse and sharing across different data contexts.

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