analyze-csv

Analyze CSV files to generate schema details, data quality signals, and statistical summaries.

Updated Apr 20, 2026

Search Tags:#data quality #data validation #data analysis #statistics #csv

Ready to run

npx skills add https://github.com/silva2kand/silva-ide --skill analyze-csv

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyze-csv
Source: https://github.com/silva2kand/silva-ide/tree/main/_cowork_os_pack/package/resources/skills/analyze-csv
Command: npx skills add https://github.com/silva2kand/silva-ide --skill analyze-csv

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Analyzes CSV files to deliver detailed schema, data quality assessments, and statistical summaries, aiding data review and validation.

Core Features & Use Cases

Schema Analysis: Identifies data types and column structures in datasets.
Data Quality Checks: Detects missing values, anomalies, and inconsistencies across columns.
Statistical Summaries: Provides key metrics such as mean, median, min, max for numeric data.
Use Case: A data analyst uploads a CSV with customer info to get insights on data health and irregularities before modeling.

Quick Start

Use analyze-csv to examine the structure and quality of your CSV file by specifying its path, for example, 'data/customers.csv'.

Frequently Asked Questions about analyze-csv

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

FAQPage Schema

What statistical summaries can I get from a CSV file?▼

Statistical summaries for CSV files provide key metrics such as mean, median, min, and max for numeric data. These calculations help you understand data distribution and identify irregularities within your tabular datasets.

Can I use CSV parsing for data validation on large tabular datasets?▼

You can identify column data types in a CSV dataset by running a schema analysis. This detects the data types and column structures automatically, allowing data analysts to verify tabular formats across various domains.

What is the best way to explore an unknown CSV file?▼

Yes, you can use CSV parsing for data validation on tabular datasets across various domains. It requires minimal setup and examines schema details, data quality signals, and statistical summaries to ensure data health.

Do I need any specific dependencies to analyze a CSV file?▼

The best way to explore an unknown CSV file is to run an automated schema analysis and data quality check. This quickly reveals column structures, detects missing values, and provides statistical summaries for immediate insights.

Why does my CSV data validation show inconsistencies across columns?▼

You do not need specific external dependencies to analyze a CSV file, as the setup is minimal. The tool relies on standard CSV parsing libraries to process your data and generate validation insights.

Why does my CSV data validation show inconsistencies across columns?▼

CSV data validation shows inconsistencies across columns when the analysis detects missing values, type mismatches, or anomalies within the dataset. These data quality signals highlight irregularities that require cleaning before modeling.