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csv-pipeline

Process, transform, and analyze CSV, TSV, and JSON tabular data.

16|1|Updated May 21, 2026
Search Tags:#automation#json#data analysis#etl#csv#data cleaning
One-click install
npx skills add https://github.com/antgroup/Agent3Sigma-Stage --skill csv-pipeline-antgroup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csv-pipeline
Source: https://github.com/antgroup/Agent3Sigma-Stage/tree/main/data/advance/skill_templates/Data_Analysis/benign_skills/gitgoodordietrying_csv-pipeline
Command: npx skills add https://github.com/antgroup/Agent3Sigma-Stage --skill csv-pipeline-antgroup

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the challenge of manually cleaning, transforming, and analyzing tabular data, which is often prone to human error and time-consuming when dealing with large or messy CSV, TSV, and JSON files.

Core Features & Use Cases

  • Data Transformation: Easily filter, join, aggregate, and deduplicate datasets using either command-line tools or Python.
  • Format Conversion: Seamlessly convert between CSV, TSV, JSON, and JSON Lines formats to ensure compatibility across different systems.
  • Use Case: A user can take a raw, messy sales export file, deduplicate entries, calculate total revenue by category, and generate a clean Markdown summary report in a single workflow.

Quick Start

Use the csv-pipeline skill to read the file data.csv, filter for rows where the amount column is greater than 100, and save the result to filtered.csv.

Frequently Asked Questions about csv-pipeline

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

FAQPage Schema
How do I automate ETL workflows for CSV and JSON files?▼

Automate ETL workflows for CSV and JSON files by applying filtering, joining, aggregating, and deduplication operations to transform raw tabular data into clean datasets for reporting.

What is the best way to clean messy CSV data and generate a summary report?▼

The best way to clean messy CSV data is to deduplicate entries, calculate aggregations like total revenue by category, and generate a clean Markdown summary report within a single automated workflow.

Can I convert JSON to CSV format without writing custom scripts?▼

You can convert JSON to CSV format seamlessly alongside TSV and JSON Lines formats, ensuring compatibility across different systems without needing custom conversion scripts.

Does Python 3 support complex tabular data transformations like joining and aggregating?▼

Python 3 supports complex tabular data transformations like joining and aggregating datasets using built-in csv, json, and collections modules without requiring external dependencies.

How do I filter rows in a CSV file based on a specific column value?▼

Filter rows in a CSV file by reading the dataset, applying a condition to match rows where the target column value meets your criteria, and saving the filtered results to a new file.

What are the limitations of processing large CSV files with built-in Python modules?▼

Processing large CSV files with built-in Python modules requires a standard Python 3 environment and handles complex ETL workflows, but may face memory constraints when loading exceptionally large datasets entirely into memory.

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