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csv-processor

Parse, transform, clean, and analyze CSV files with statistical summaries.

27|4|Updated Oct 20, 2025
Search Tags:#data analysis#data processing#transformation#csv#data cleaning#parsing
One-click install
npx skills add https://github.com/CuriousLearner/devkit --skill csv-processor-curiouslearner
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csv-processor
Source: https://github.com/CuriousLearner/devkit/tree/main/skills/csv-processor
Command: npx skills add https://github.com/CuriousLearner/devkit --skill csv-processor-curiouslearner

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, csvkit, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the complex and time-consuming tasks of parsing, transforming, cleaning, and analyzing data within CSV files, making data manipulation accessible and efficient.

Core Features & Use Cases

  • Robust Parsing: Handles various delimiters, encodings, and complex field structures.
  • Flexible Transformation: Filter, select, sort, group, merge, and pivot data with ease.
  • Data Cleaning: Automates duplicate removal, missing value imputation, and data normalization.
  • Analysis & Reporting: Generates statistical summaries, data profiles, and comprehensive reports.
  • Use Case: You have a large CSV file with customer data and need to filter for customers in a specific region, calculate their average purchase value, and identify any missing contact information.

Quick Start

Use the csv-processor skill to parse the file 'customer_data.csv', filter for rows where 'country' is 'USA', and calculate the average 'order_total'.

Frequently Asked Questions about csv-processor

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

FAQPage Schema
How do I clean and normalize CSV data with missing values and duplicates?▼

You can parse CSV files to filter rows, select specific columns, and sort records. It supports various delimiters and complex field structures to ensure accurate data extraction and transformation.

Can I group, merge, and pivot data in a CSV file without writing complex code?▼

Yes, you can group, merge, and pivot CSV data to restructure datasets dynamically. It handles complex field structures and various encodings to transform your records without manual scripting.

What is the best way to generate statistical summaries and data profiles from a large CSV file?▼

Generating statistical summaries involves parsing the CSV to calculate metrics and produce data profiles. This skill automates statistical summarization and report generation to reveal data distribution and quality.

Does this data processing approach handle CSV files with non-standard delimiters and encodings?▼

Yes, robust parsing handles various delimiters, encodings, and complex field structures in CSV files. It ensures accurate data extraction and transformation regardless of non-standard formatting.

How do I filter customer data in a CSV and calculate the average order value?▼

Filter CSV rows by specific criteria like region and calculate average values for selected columns. This skill applies filtering and statistical calculations directly to parsed customer data.

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