Agent skill · data analytics · ailabs-393
csv-data-visualizer
This skill should be used when working with CSV files to create interactive data visualizations, generate statistical plots, analyze data distributions, create dashboards, or perform automatic data profiling. It provides comprehensive tools for exploratory data analysis using Plotly for interactive visualizations.
Why this skill is useful
Adds executable scripts for interactive visualizations and comprehensive data profiling that enhance the AI's capabilities in data analysis.
What it needs
Requires plotly installed locally. About 5k tokens when loaded. Last updated 2025-11-11. 438 stars on the source repository.
What this skill does
CSV Data Visualizer Overview This skill enables comprehensive data visualization and analysis for CSV files. It provides three main capabilities: (1) creating individual interactive visualizations using Plotly, (2) automatic data profiling with statistical summaries, and (3) generating multi-plot dashboards. The skill is optimized for exploratory data analysis, statistical reporting, and creating presentation-ready visualizations. When to Use This Skill Invoke this skill when users request: "Visualize this CSV data" "Create a histogram/scatter plot/box plot from this data" "Show me the distribution of [column]" "Generate a dashboard for this dataset" "Profile this CSV file" or "Analyze this data" "Create a correlation heatmap" "Show trends over time" "Compare [variable] across [categories]" Core Capabilities 1. Individual Visualizations Create specific chart types for detailed analysis using the visualizecsv.py script. Available Chart Types: Statistical Plots: Relationship Analysis: Time Series: Categorical Data: Output Formats: Specify output file with desired format extension: 2. Automatic Data Profiling Generate comprehensive data quality and statistical reports using the dataprofile.py script. Text Report (default): HTML Report: JSON Report: What the Profiler Provides: File information (size, dimensions) Dataset overview (shape, memory usage, duplicates) Column-by-column analysis (types, missing data, unique values) Missing data patterns and completeness Statistical summary for numeric columns (mean, std, quartiles, skewness, kurtosis) Categorical column analysis (frequency counts, most/least common values) Data quality checks (high missing data, duplicate rows, constant columns, high cardinality) When to Use Profiling: Always recommend running data profiling BEFORE creating visualizations when: User is unfamiliar with the dataset Data quality is unknown Need to identify appropriate visualization types Exploring a new dataset for the first time 3. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills ailabs-393/csv-data-visualizer--be86af