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data-pipeline

Automate ETL and data transformation across CSV, JSON, Parquet, and NDJSON formats.

5|Updated Apr 8, 2026
Search Tags:#json#transformation#etl#csv#parquet#data-pipeline#ndjson
One-click install
npx skills add https://github.com/JansenAnalytics/claudex --skill data-pipeline-jansenanalytics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-pipeline
Source: https://github.com/JansenAnalytics/claudex/tree/main/skills/data-pipeline
Command: npx skills add https://github.com/JansenAnalytics/claudex --skill data-pipeline-jansenanalytics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

ETL and data transformation across CSV, JSON, Parquet, and NDJSON formats require reliable tooling to clean, convert, and enrich data at scale.

Core Features & Use Cases

  • Convert between CSV, JSON, Parquet, and NDJSON formats.
  • Filter, map, join, and aggregate data across multiple files.
  • Build multi-step pipelines for data ingestion, cleaning, and transformation.

Quick Start

Run a simple data-pipeline: read sample.csv, transform it, and write results to sample.json.

Frequently Asked Questions about data-pipeline

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

FAQPage Schema
How do I convert CSV to JSON or NDJSON without installing external Python packages?▼

You can convert CSV to JSON or NDJSON using only the Python 3 standard library, specifically the csv and json modules, with streaming stdin/stdout so no external dependencies are required.

What is the best way to build a multi-step data pipeline for filtering and joining multiple files?▼

Building a multi-step data pipeline is best handled by chaining filter, map, join, and aggregate operations across multiple files, using streaming stdin/stdout to process CSV, JSON, Parquet, and NDJSON formats seamlessly.

Can I transform Parquet files using only Python standard library tooling?▼

Parquet transformation is supported alongside CSV, JSON, and NDJSON formats, utilizing Python 3 standard library tooling with streaming stdin/stdout to ensure zero external dependencies during your data engineering pipelines.

Does this ETL approach work for aggregating large multi-file datasets?▼

This ETL approach works for aggregating large multi-file datasets by applying filtering, mapping, joining, and aggregation operations across CSV, JSON, Parquet, and NDJSON formats with streaming data processing.

Are there limitations when streaming NDJSON transformations through stdin and stdout?▼

Streaming NDJSON transformations through stdin and stdout rely strictly on the Python 3 standard library without external dependencies, meaning complex binary format handling or highly specialized parsing may require additional tooling.

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