data-pipeline

Name: data-pipeline
Availability: InStock
Author: agulli

Automate data pipeline definition, validation, and orchestration with schema contracts.

482|100|Updated Apr 9, 2026

Search Tags:#validation #schema #data #observability #idempotent #pipeline #etl

Ready to run

npx skills add https://github.com/agulli/atlas-agents --skill data-pipeline-agulli

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-pipeline
Source: https://github.com/agulli/atlas-agents/tree/main/ch09_agent_skills/skills/data-pipeline
Command: npx skills add https://github.com/agulli/atlas-agents --skill data-pipeline-agulli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data pipelines often fail at scale due to unclear contracts, missing validations, and weak observability. This Skill provides a structured approach to define input/output schemas, validate at the pipeline boundary, ensure idempotence, and monitor progress throughout the run.

Core Features & Use Cases

Contract-first design: Define input and output schemas, volume, and frequency before building transformations.
Boundary validation: Validate records at the boundary to prevent corrupt downstream data.
End-to-end reliability: Ensure idempotent runs, robust logging, and clear success/failure reporting across batches and schedules.
Use Case: Build an ETL job that ingests product events, validates schema, applies transformations, and loads into a warehouse with observable checkpoints.

Quick Start

Run a sample dataset through the pipeline to validate input/output contracts and idempotence.

Frequently Asked Questions about data-pipeline

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

FAQPage Schema

What is data pipeline observability and why do I need it for ETL workloads?▼

Data pipeline observability provides structured logging and clear success/failure reporting across batches and schedules. It ensures reliable ETL workloads by monitoring progress throughout the run and preventing corrupt downstream data through boundary validation.

How do I validate input and output schemas before building data pipeline transformations?▼

To validate schemas before building transformations, use a contract-first design approach to define input and output schemas, volume, and frequency. This boundary validation prevents corrupt downstream data by checking records at the pipeline boundary.

How can I make my ETL pipeline idempotent for batch processing?▼

Make an ETL pipeline idempotent by ensuring deterministic IDs and applying structured boundary validation. This approach guarantees end-to-end reliability, allowing robust logging and clear success or failure reporting across repeated batch processing runs.

Does this data pipeline approach support event ingestion and warehouse loading?▼

Yes, this approach supports event ingestion and warehouse loading. You can build an ETL job that ingests product events, validates schemas, applies transformations, and loads data into a warehouse with observable checkpoints.

What's the best way to prevent corrupt data from entering downstream transformations?▼

The best way to prevent corrupt downstream data is to validate records at the pipeline boundary. By applying contract-first design and defining input/output schemas upfront, you ensure only valid data enters your transformations.

When should I not use a contract-first approach for data pipelines?▼

A contract-first approach for data pipelines is not suitable for unstructured exploratory analysis where input schemas are unknown. It requires clearly defined input/output schemas, volume, and frequency before building transformations.