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create-gold

Generate PySpark Gold layer artifacts from approved design documents.

5|1|Updated Sep 23, 2025
Search Tags:#data quality#data engineering#airflow#pyspark#gold layer#medallion pipeline
One-click install
npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill create-gold
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: create-gold
Source: https://github.com/RDEWAI/Redefining-DataEngineering-With-AI/tree/main/chapter-6/developer-plugin/skills/create-gold
Command: npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill create-gold

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Building the Gold (consumable) layer of a medallion data pipeline traditionally requires manually aligning multiple design documents, writing consistent builder modules, contracts, data quality rules, tests, and DAG wiring, which is time-consuming and prone to alignment drift with upstream specifications.

Core Features & Use Cases

  • End-to-end artifact generation: Automatically produces PySpark builder modules, per-table contracts, data quality rule files, unit tests, and Airflow DAG wiring for all Gold consumer tables defined in upstream design documents.
  • Strict upstream alignment: All generated outputs strictly follow approved Low-Level Design (LLD), Data Model Specification (DMS), Silver-to-Gold mapping (STM), and Data Quality Specification (DQS) documents to ensure consistency with existing pipeline standards.
  • Use case: A healthcare data engineering team building a Patient 360 pipeline can use this skill to generate all Gold layer deliverables for a new user story or full layer build in minutes instead of days of manual work.

Quick Start

Use the create-gold skill to generate all Gold layer deliverables for story STORY-06-001 or run a full Gold layer build for the Patient 360 medallion pipeline.

Frequently Asked Questions about create-gold

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

FAQPage Schema
How do I automate Gold layer builds for a medallion data pipeline?▼

Automate Gold layer builds by generating production-ready PySpark builder modules, per-table contracts, data quality rule files, unit tests, and Airflow DAG wiring directly from approved upstream design documents. This eliminates manual coding errors and specification drift.

What is the Gold layer in a medallion data pipeline?▼

The Gold layer is the consumable tier of a medallion data pipeline, containing aggregated business-ready tables like patient_summary and patient_billing_summary. It is built from the Silver layer to serve analytics and reporting use cases.

How do I generate PySpark builder modules and Airflow DAGs for healthcare Patient 360 pipelines?▼

Generate PySpark builder modules and Airflow DAGs for Patient 360 pipelines by processing approved Low-Level Design, Data Model Specification, and Data Quality Specification documents to automatically output production-ready code and wiring.

Can I use this to generate data quality rules and unit tests for Gold consumer tables?▼

Yes, you can generate data quality rule files and unit tests for Gold consumer tables. The outputs strictly adhere to upstream Data Quality Specifications to ensure consistency with existing pipeline standards and validate table contracts.

What's the best way to align Gold layer code with upstream LLD and DMS specifications?▼

The best way to align Gold layer code with LLD and DMS specifications is to automate generation directly from those approved documents, ensuring strict adherence to Low-Level Design, Data Model Specification, and Silver-to-Gold mapping standards.

Do I need approved upstream design documents to build the Gold layer?▼

Yes, approved upstream design documents including LLD, DMS, STM, and DQS are required. The Gold layer generation strictly relies on these specifications to produce consistent builder modules, contracts, and Airflow DAG wiring without drift.

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