csv-analyst

Analyze CSV and XLSX files to generate statistical profiles and visual insights.

15|3|Updated Aug 28, 2025

Search Tags:#charts #analysis #data #statistics #spreadsheet #csv

Ready to run

npx skills add https://github.com/cheatcode-ai/cheatcode --skill csv-analyst

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: csv-analyst
Source: https://github.com/cheatcode-ai/cheatcode/tree/main/skills/csv-analyst
Command: npx skills add https://github.com/cheatcode-ai/cheatcode --skill csv-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the manual effort required to profile, clean, and visualize tabular data, allowing you to move from raw files to business insights without writing boilerplate code.

Core Features & Use Cases

Automated Profiling: Instantly generate row counts, missingness reports, and type distributions for any CSV or XLSX file.
Reproducible Analysis: Create charts and statistical summaries that are saved directly to your workspace for auditability.
Use Case: Use this to perform cohort analysis on user retention data or to quickly identify outliers in a large financial dataset.

Quick Start

Ask the agent to analyze the uploaded sales data file and generate a summary of trends and key findings.

Frequently Asked Questions about csv-analyst

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

FAQPage Schema

How do I generate statistical profiling and visual insights from a CSV file?▼

Statistical profiling and visual insights for a CSV file are generated by analyzing tabular data to produce row counts, missingness reports, and type distributions. The process creates reproducible charts and statistical summaries saved directly to your workspace.

Can I perform cohort analysis on spreadsheet data without writing Python code?▼

Cohort analysis on spreadsheet data can be performed without manual Python coding by utilizing automated analysis scripts. You can analyze user retention data or identify outliers in financial datasets by asking the agent to process the uploaded files.

Does CSV analysis support XLSX formats or only CSV files?▼

CSV analysis supports both CSV and XLSX file formats. It processes these tabular data files to provide data quality assessments, trend identification, and business intelligence insights.

What is the best way to assess data quality and missingness in a large dataset?▼

The best way to assess data quality and missingness in a large dataset is through automated profiling. This process instantly generates missingness reports and type distributions, eliminating manual effort and identifying data gaps.

Do I need a specific sandbox environment to run reproducible analysis scripts?▼

A sandbox environment with Python data processing tools is required to execute reproducible analysis scripts. This setup ensures that the statistical profiling and visual insights are generated reliably and audited.

Why use automated tabular data profiling instead of manual spreadsheet analysis?▼

Automated tabular data profiling is used instead of manual analysis to eliminate boilerplate code and manual effort. It allows you to move directly from raw files to actionable business insights by automating trend identification and statistical summaries.