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csv-data-visualizer

Generate interactive Plotly visualizations, statistical profiles, and dashboards from CSV files.

2|1|Updated Jun 12, 2025
Search Tags:#data visualization#dashboard#pandas#csv#plotly#exploratory data analysis#data profiling
One-click install
npx skills add https://github.com/agent-trust-protocol/atp-core --skill csv-data-visualizer-agent-trust-protocol
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csv-data-visualizer
Source: https://github.com/agent-trust-protocol/atp-core/tree/main/.claude/skills/csv-data-visualizer
Command: npx skills add https://github.com/agent-trust-protocol/atp-core --skill csv-data-visualizer-agent-trust-protocol

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, plotly, numpy, kaleido, and includes scripts (resource) and references (resource) components.

What problem does it solve? Exploring CSV data and producing charts normally requires writing repetitive pandas and Plotly code by hand. This Skill automates exploratory data analysis by generating statistical plots, data quality profiles, and multi-plot dashboards directly from any CSV file. ## Core Features & Use Cases - Individual Visualizations: Create histograms, box plots, violin plots, scatter plots with trend lines, correlation heatmaps, line charts, bar charts, and pie charts via a single CLI command, exported as HTML, PNG, PDF, or SVG. - Automatic Data Profiling: Generate text, HTML, or JSON reports covering column types, missing data, statistical summaries, and data quality issues like duplicates and high-cardinality columns. - Multi-Plot Dashboards: Build automatic dashboards based on detected data types, or custom dashboards defined by a JSON configuration file. - Use Case: Given an unfamiliar sales dataset, first run the profiler to understand column types and data quality, then generate an automatic dashboard for an overview, and finally create targeted scatter plots and grouped box plots for a presentation. ## Quick Start Profile the attached sales_data.csv file and then create an interactive dashboard showing the key distributions and correlations.

Frequently Asked Questions about csv-data-visualizer

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

FAQPage Schema
How do I create a histogram from a CSV file in Python?▼

Run the visualize_csv.py script with the --histogram flag followed by the column name, for example: python3 scripts/visualize_csv.py data.csv --histogram age --bins 30. The output defaults to an interactive HTML file, or specify a .png or .pdf extension for static images.

How to generate a data profiling report for a CSV file?▼

Run data_profile.py with your CSV path to get a text report covering column types, missing data, statistics, and quality issues. Use -f html or -f json with -o to save the report in HTML or JSON format instead.

What chart types does Plotly support for CSV visualization?▼

This Skill supports histograms, box plots, violin plots, scatter plots with optional trend lines, correlation heatmaps, line charts for time series, bar charts, and pie charts. Charts can be grouped by categorical columns and exported as HTML, PNG, PDF, or SVG.

Why does Plotly image export fail with PNG or PDF output?▼

Static image export requires the kaleido package, which is not installed by default. Run pip install kaleido to enable PNG, PDF, and SVG output; HTML export works without it.

Can I customize which plots appear in a dashboard?▼

Yes, create a JSON config file listing plot types, columns, and group-by fields, then pass it with --config to create_dashboard.py. Without a config, the script auto-selects plots based on detected numeric and categorical columns.

What are the limitations of automatic CSV dashboards?▼

Automatic dashboards select at most two numeric columns for distributions and one categorical column for bar charts, capped by the --max-plots setting. Datasets needing specific column combinations or more than nine plots require a custom JSON configuration.

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