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home / skills / jeremylongshore / claude-code-plugins-plus-skills / creating-data-visualizations

creating-data-visualizations skill

/plugins/ai-ml/data-visualization-creator/skills/creating-data-visualizations

This skill converts data into visuals by automatically selecting the best plot type and generating clear charts and graphs.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill creating-data-visualizations

Review the files below or copy the command above to add this skill to your agents.

Files (7)
SKILL.md
3.1 KB
---
name: creating-data-visualizations
description: |
  Generate plots, charts, and graphs from data with automatic visualization type selection. Use when requesting "visualization", "plot", "chart", or "graph". Trigger with phrases like 'generate', 'create', or 'scaffold'.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <[email protected]>
license: MIT
---
# Data Visualization Creator

This skill provides automated assistance for data visualization creator tasks.

## Overview

This skill empowers Claude to transform raw data into compelling visual representations. It leverages intelligent automation to select optimal visualization types and generate informative plots, charts, and graphs. This skill helps users understand complex data more easily.

## How It Works

1. **Data Analysis**: Claude analyzes the provided data to understand its structure, type, and distribution.
2. **Visualization Selection**: Based on the data analysis, Claude selects the most appropriate visualization type (e.g., bar chart, scatter plot, line graph).
3. **Visualization Generation**: Claude generates the visualization using appropriate libraries and best practices for visual clarity and accuracy.

## When to Use This Skill

This skill activates when you need to:
- Create a visual representation of data.
- Generate a specific type of plot, chart, or graph (e.g., "create a bar chart").
- Explore data patterns and relationships through visualization.

## Examples

### Example 1: Visualizing Sales Data

User request: "Create a bar chart showing sales by region."

The skill will:
1. Analyze the sales data, identifying regions and corresponding sales figures.
2. Generate a bar chart with regions on the x-axis and sales on the y-axis.

### Example 2: Plotting Stock Prices

User request: "Plot the stock price of AAPL over the last year."

The skill will:
1. Retrieve historical stock price data for AAPL.
2. Generate a line graph showing the stock price over time.

## Best Practices

- **Data Clarity**: Ensure the data is clean and well-formatted before requesting a visualization.
- **Specific Requests**: Be specific about the desired visualization type and any relevant data filters.
- **Contextual Information**: Provide context about the data and the purpose of the visualization.

## Integration

This skill can be integrated with other data processing and analysis tools within the Claude Code environment. It can receive data from other skills and provide visualizations for further analysis or reporting.

## Prerequisites

- Appropriate file access permissions
- Required dependencies installed

## Instructions

1. Invoke this skill when the trigger conditions are met
2. Provide necessary context and parameters
3. Review the generated output
4. Apply modifications as needed

## Output

The skill produces structured output relevant to the task.

## Error Handling

- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps

## Resources

- Project documentation
- Related skills and commands

Overview

This skill automates generation of plots, charts, and graphs from raw data, choosing visualization types that best reveal patterns and relationships. It speeds analysis by inspecting data structure, recommending chart types, and producing ready-to-use visual outputs. Use it to turn tables, time series, or categorical summaries into clear visualizations quickly.

How this skill works

The skill analyzes input data to infer types, distributions, and key variables. It selects an appropriate visualization (bar, line, scatter, histogram, heatmap, etc.) and builds the plot using standard plotting libraries and visualization best practices. It can accept dataframes, CSVs, or direct arrays and returns the generated chart plus suggestions for labels, color schemes, and refinements.

When to use it

  • You need a quick, sensible visualization from a dataset or table.
  • You want the system to pick an effective chart type automatically.
  • You need exploratory visuals to reveal trends, outliers, or correlations.
  • You want code or image output for reports, dashboards, or presentations.
  • You’re prototyping visualizations and need scaffolding or alternatives.

Best practices

  • Provide clean, well-labeled data: columns should have descriptive names and consistent types.
  • Specify the goal (compare categories, show trend, explain distribution, reveal correlation).
  • Include any filters, grouping, or aggregation requirements up front.
  • Request desired output format (PNG, SVG, notebook code, or interactive plot) if you have a preference.
  • Review axis labels, units, and legends for clarity and adjust color palettes for accessibility.

Example use cases

  • Generate a bar chart of sales by region from a sales CSV for a weekly report.
  • Create a line chart of daily stock prices for the past year to visualize trends.
  • Produce a scatter plot with regression fit to examine correlation between marketing spend and conversions.
  • Draw a histogram of response times to identify performance bottlenecks.
  • Build a heatmap of feature correlations to guide feature selection for modeling.

FAQ

What input formats are supported?

Common inputs include pandas DataFrames, CSV files, JSON arrays, and simple arrays of x/y values. Provide headers and types for best results.

Can I get the plotting code as well as the image?

Yes. The skill can return executable plotting code (Python/matplotlib or seaborn) alongside image outputs so you can modify or reproduce the visualization.