CLI Data Visualization Quickstart Skill - Skill

CLI Data Viz Quickstart

Create publication-ready charts and visualizations from CSV, JSON, and Excel data using Python (matplotlib/seaborn) or Node.js (vega/vega-lite). Generate bar charts, line plots, scatter plots, heatmaps, and statistical visualizations with custom styling. Export to PNG, SVG, PDF, or interactive HTML formats for reports, dashboards, and presentations.

"Create a sales dashboard from this data: 1. Line chart: monthly revenue trend 2. Bar chart: top 10 products by sales 3. Pie chart: sales by region 4. Table: key metrics summary Use a professional color scheme and save as sales_dashboard.png"

"Visualize this dataset statistically: 1. Histogram with distribution curve 2. Box plot showing quartiles 3. Scatter plot with correlation 4. Heatmap of correlations between variables Add statistical annotations and save as analysis.png"

import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('timeseries.csv', parse_dates=['date']) df.set_index('date')['value'].plot(figsize=(12, 6)) plt.title('Time Series Data') plt.xlabel('Date') plt.ylabel('Value') plt.tight_layout() plt.savefig('timeseries.png', dpi=300)

const fs = require('fs'); const { compile } = require('vega-lite'); const { render } = require('vega'); const spec = { data: { values: JSON.parse(fs.readFileSync('data.json')) }, mark: 'bar', encoding: { x: { field: 'category' }, y: { field: 'value' } } }; const vegaSpec = compile(spec).spec; render(vegaSpec, { format: 'svg' }).then(svg => { fs.writeFileSync('chart.svg', svg); });

Skill Content

The main content for this skill.

cli-data-viz-quickstart.txt

"Create a sales dashboard from this data:
1. Line chart: monthly revenue trend
2. Bar chart: top 10 products by sales
3. Pie chart: sales by region
4. Table: key metrics summary
Use a professional color scheme and save as sales_dashboard.png"

Skill Content (2)

Additional code example for this skill.

cli-data-viz-quickstart.txt

"Visualize this dataset statistically:
1. Histogram with distribution curve
2. Box plot showing quartiles
3. Scatter plot with correlation
4. Heatmap of correlations between variables
Add statistical annotations and save as analysis.png"

Skill Content (3)

Additional code example for this skill.

cli-data-viz-quickstart.txt

"Compare Year 2024 vs 2025 data:
1. Side-by-side bar charts
2. Percentage change annotations
3. Highlight positive/negative changes with colors
4. Add summary statistics
Make it presentation-ready"

Skill Content (4)

Additional code example for this skill.

cli-data-viz-quickstart.txt

"Create a chart matching our brand:
- Primary color: #FF6B35
- Font: Arial
- Style: minimalist, no grid lines
- Background: white
- High DPI for print (300 dpi)
Show monthly data as area chart"

Features

Key capabilities and functionality

CLI-first workflows
Common chart templates (bar/line/scatter)
Headless export
Reproducible configurations
Multiple export formats (PNG, SVG, PDF, HTML)
Statistical visualization support (histograms, box plots, heatmaps)
Custom styling and branding options

Use Cases

Common scenarios and applications

Exploratory analysis
Executive snapshots
CI artifact generation
Automated report generation in CI/CD pipelines
Data exploration and analysis workflows
Presentation-ready visualizations with custom branding

Requirements

Prerequisites and dependencies

Python 3.11+ or Node.js 18+
matplotlib/seaborn or vega/vega-lite
Data file in supported format (CSV, JSON, Excel)
Code Interpreter enabled (for Python path)

Installation

Setup instructions and requirements

Claude Code Setup

For Node: npm i vega vega-lite

Use headless Chrome for SVG->PNG

Claude Desktop Setup

Install Python 3.11+

Step 2: Run command

claude desktop setup-2.sh

bash

pip install pandas matplotlib seaborn

Troubleshooting

Common issues and solutions

Fonts missing in headless mode
Install system fonts or specify a font path for rendering. For Linux: Install fonts via package manager (e.g., `apt-get install fonts-dejavu`). For Python: Set `matplotlib.rcParams['font.family']` or use `matplotlib.font_manager.findSystemFonts()`. For headless servers, install font packages or use font paths explicitly in code.
Skill activation fails with ModuleNotFoundError for matplotlib
Run `pip install matplotlib seaborn pandas` in Code Interpreter environment. Verify Python 3.11+ is active. Restart Claude Desktop after installation. For Claude Code: Packages install automatically on first use. Check Python version with `python --version` and ensure pip is available.
Chart export produces blank PNG files or corrupted images
Call `plt.tight_layout()` before `plt.savefig()`. Set explicit figure size: `plt.figure(figsize=(10,6))`. Use `bbox_inches='tight'` parameter in `savefig()`. Ensure backend is set correctly: `matplotlib.use('Agg')` for headless mode. Check file permissions and disk space.
Data visualization skill not appearing in available skills list
Enable Code Interpreter in Claude Desktop settings under Features. Upload data file (CSV/JSON) to conversation first to trigger skill recognition and activation. Verify skill is installed in `.claude/skills/cli-data-viz-quickstart/` directory with valid SKILL.md file.
Interactive plotly charts fail to render with JavaScript errors
Save as standalone HTML with `plotly.offline.plot(include_plotlyjs='cdn')`. Ensure output HTML file has proper DOCTYPE and charset UTF-8 declaration. For offline use, download plotly.js locally and use `include_plotlyjs='directory'` with local path.

Usage Examples

Practical code examples demonstrating common use cases and implementation patterns

Bar chart from CSV (Python)

bar-chart-from-csv-python.py

python

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data.csv')
df.groupby('category')['value'].sum().plot(kind='bar')
plt.tight_layout(); plt.savefig('chart.png', dpi=200)

Time series line plot (Python)

Create a time series line plot with date parsing and high-resolution export

time-series-line-plot-python.py

python

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('timeseries.csv', parse_dates=['date'])
df.set_index('date')['value'].plot(figsize=(12, 6))
plt.title('Time Series Data')
plt.xlabel('Date')
plt.ylabel('Value')
plt.tight_layout()
plt.savefig('timeseries.png', dpi=300)

Vega-Lite chart from JSON (Node.js)

Generate a bar chart from JSON data using Vega-Lite and export as SVG

vega-lite-chart-from-json-node-js.js

javascript

const fs = require('fs');
const { compile } = require('vega-lite');
const { render } = require('vega');

const spec = {
  data: { values: JSON.parse(fs.readFileSync('data.json')) },
  mark: 'bar',
  encoding: { x: { field: 'category' }, y: { field: 'value' } }
};

const vegaSpec = compile(spec).spec;
render(vegaSpec, { format: 'svg' }).then(svg => {
  fs.writeFileSync('chart.svg', svg);
});

CLI Data Viz Quickstart

Claude Code Setup

Claude Desktop Setup

Bar chart from CSV (Python)

Time series line plot (Python)

Vega-Lite chart from JSON (Node.js)

Reviews (0)

CLI Data Viz Quickstart

Claude Code Setup

Claude Desktop Setup

Bar chart from CSV (Python)

Time series line plot (Python)

Vega-Lite chart from JSON (Node.js)

Reviews (0)

Category

Source

DOCX Report Generator

CSV & Excel Data Wrangler

FastAPI Python Backend Expert

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