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csv-pipeline

Process CSV, TSV, JSON, and JSON Lines files with Python 3 standard library.

602|121|Updated Feb 10, 2026
Search Tags:#reporting#aggregation#csv#data cleaning#joining#format conversion#tabular data
One-click install
npx skills add https://github.com/AIDotNet/OpenCowork --skill csv-pipeline-aidotnet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csv-pipeline
Source: https://github.com/AIDotNet/OpenCowork/tree/main/resources/skills/csv-pipeline
Command: npx skills add https://github.com/AIDotNet/OpenCowork --skill csv-pipeline-aidotnet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill removes the manual overhead of inspecting, cleaning, reshaping, and summarizing tabular data files when you need reliable results from CSV, TSV, JSON, or JSON Lines sources.

Core Features & Use Cases

  • Data Inspection: Quickly check row counts, column names, and non-empty value coverage to understand messy datasets.
  • Transformation Workflows: Filter, sort, deduplicate, join, convert formats, and clean common data quality issues using one consistent pipeline.
  • Reporting and Aggregation: Generate grouped summaries and Markdown reports for analytics, operations, and lightweight ETL tasks.
  • Use Case: A data analyst receives multiple exports from different systems and uses this Skill to standardize the files, merge them by key, and produce a concise summary report for stakeholders.

Quick Start

Use the csv-pipeline skill to inspect the attached data file and then clean, filter, or summarize it based on the fields I care about.

Frequently Asked Questions about csv-pipeline

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

FAQPage Schema
How do I clean and summarize messy CSV exports without manual effort?▼

To clean and summarize CSV exports, you can inspect row counts, deduplicate entries, filter, and aggregate grouped summaries into Markdown reports using a consistent Python 3 data pipeline.

Can I join and convert TSV files to JSON format in one workflow?▼

Yes, you can join TSV files by key and convert them to JSON or JSON Lines format within a single lightweight ETL workflow that handles format conversion and data transformation.

What is the best way to inspect tabular data for non-empty value coverage?▼

The best way to inspect tabular data for non-empty value coverage is to run a data inspection pipeline that quickly checks row counts, column names, and field completeness across datasets.

Do I need to install external Python libraries to process JSON Lines files?▼

No, you do not need external Python libraries to process JSON Lines files because the pipeline relies solely on the Python 3 standard library for deterministic csv and json handling.

Does this data cleaning approach work for generating analytics reports from multiple system exports?▼

Yes, this data cleaning approach works for generating analytics reports by standardizing multiple system exports, merging them by key, and producing a concise grouped summary report for stakeholders.

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