data-engineering
v1.3.2ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development
By Seth HobsonLicense: MIT0 GitHub starsUpdated 4 months ago
Directory evidence
- Runtimes
- Claude Code
- Parsed components
- 4 skill or MCP entries
- Source updated
- Jun 22, 2026
- Manifest status
- Canonical path parsed
The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →
Install data-engineering for Claude Code
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install data-engineering-4@agent-plugin-marketplacePaste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.
The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.
Get the source manually
git clone https://github.com/yo-steven/agents-exploration-20260523Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/data-engineering/.
Plugin files
├── .claude-plugin/plugin.json├── skills/airflow-dag-patterns/SKILL.md├── skills/data-quality-frameworks/SKILL.md├── skills/dbt-transformation-patterns/SKILL.md└── skills/spark-optimization/SKILL.md
Included Skills4
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
Plugin manifests1
{
"name": "data-engineering",
"version": "1.3.2",
"description": "ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development",
"author": {
"name": "Seth Hobson",
"email": "[email protected]"
},
"license": "MIT"
}For maintainers
If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.
[data-engineering on Agent Plugins Marketplace](https://pluginsmp.com/plugins/data-engineering-4)