data-engineering
v1.3.2ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development
By Seth HobsonLicense: MIT39.9k GitHub starsUpdated 3 days ago
Directory evidence
- Runtimes
- Codex and Claude Code
- Parsed components
- 4 skill or MCP entries
- Source updated
- Sep 21, 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 Codex and Claude Code
codex plugin marketplace add wshobson/agents
codex plugin marketplace upgrade claude-code-workflows
codex plugin add data-engineering@claude-code-workflowsPaste and run these commands in a terminal with Codex. They add and refresh the claude-code-workflows catalog, then install this plugin.
Compatibility: the page URL and API slug “data-engineering” remain stable.
- Codex:
data-engineering@agent-plugin-marketplace→data-engineering@claude-code-workflows
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/wshobson/agentsClone 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
├── .codex-plugin/plugin.json├── .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 manifests2
{
"name": "data-engineering",
"version": "1.3.2",
"description": "ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development",
"skills": "./skills/",
"author": {
"name": "Seth Hobson",
"email": "[email protected]"
},
"license": "MIT",
"interface": {
"displayName": "Data Engineering",
"shortDescription": "ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development",
"category": "Coding"
}
}{
"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)