By astronomer
Manage the full Airflow data engineering lifecycle: author, test, debug, and deploy DAGs; profile and trace lineage across warehouses; migrate between Airflow versions; and troubleshoot production deployments via CLI.
Use when running a dbt Fusion project with Astronomer Cosmos. Covers Cosmos 1.11+ configuration for Fusion on Snowflake/Databricks with ExecutionMode.LOCAL. Before implementing, verify dbt engine is Fusion (not Core), warehouse is supported, and local execution is acceptable. Does not cover dbt Core.
Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.
Author Apache Airflow DAGs declaratively with dag-factory YAML configs. Use when creating dag-factory templates, composing DAGs from YAML for dag-factory, configuring defaults/dynamic tasks/datasets/callbacks for dag-factory, or validating dag-factory configurations.
Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline", "full root cause analysis", "why is this failing and how to prevent it". For simple debugging ("why did dag fail", "show logs"), the airflow entrypoint skill handles it directly. This skill provides structured investigation and prevention recommendations.
Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.
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AI agent tooling for data engineering workflows. Includes an MCP server for Airflow, a CLI tool (af) for interacting with Airflow from your terminal, and skills that extend AI coding agents with specialized capabilities for working with Airflow and data warehouses. Works with Claude Code, Cursor, and other agentic coding tools.
Built by Astronomer. Apache 2.0 licensed and compatible with open-source Apache Airflow.
npx skills add astronomer/agents --skill '*'
This installs all Astronomer skills into your project via skills.sh. You'll be prompted to select which agents to install to. To also select skills individually, omit the --skill flag.
[!IMPORTANT] Claude Code users: We recommend using the plugin instead (see Claude Code section below) for better integration with MCP servers and hooks.
Skills: Works with 25+ AI coding agents including Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, Cline, and more.
MCP Server: Works with any MCP-compatible client including Claude Desktop, VS Code, and others.
[!NOTE] Open-source Airflow users: The MCP server works with any Airflow 2.x/3.x REST API. Set
AIRFLOW_API_URLto your self-hosted instance. Skills are tool-agnostic and work with any Airflow deployment.
# Add the marketplace and install the plugin
claude plugin marketplace add astronomer/agents
claude plugin install astronomer-data@astronomer
# Upgrading from the old plugin name? Uninstall first:
# claude plugin uninstall data@astronomer && claude plugin marketplace update && claude plugin install astronomer-data@astronomer
The plugin includes the Airflow MCP server that runs via uvx from PyPI. Data warehouse queries are handled by the analyzing-data skill using a background Jupyter kernel.
Cursor supports both MCP servers and skills.
MCP Server - Click to install:
Skills - Install to your project:
npx skills add astronomer/agents --skill '*' -a cursor
This installs skills to .cursor/skills/ in your project.
Add to ~/.cursor/mcp.json:
{
"mcpServers": {
"airflow": {
"command": "uvx",
"args": ["astro-airflow-mcp", "--transport", "stdio"]
}
}
}
Create .cursor/hooks.json in your project:
{
"version": 1,
"hooks": {
"stop": [
{
"command": "uv run $CURSOR_PROJECT_DIR/.cursor/skills/analyzing-data/scripts/cli.py stop",
"timeout": 10
}
]
}
}
What these hooks do:
stop: Cleans up kernel when session endsFor any MCP-compatible client (Claude Desktop, VS Code, etc.):
# Airflow MCP
uvx astro-airflow-mcp --transport stdio
# With remote Airflow
AIRFLOW_API_URL=https://your-airflow.example.com \
AIRFLOW_USERNAME=admin \
AIRFLOW_PASSWORD=admin \
uvx astro-airflow-mcp --transport stdio
The astronomer-data plugin bundles an MCP server and skills into a single installable package.
npx claudepluginhub astronomer/agents --plugin astronomer-dataEditorial "Data Engineering" bundle for Claude Code from Antigravity Awesome Skills.
Claude Code skill pack for Databricks (24 skills)
Spec-Driven Development framework for Data Engineering — 58 agents, 24 KB domains, 5-phase SDD workflow, 31 commands
This plugin provides a specialized suite of skills for data engineers and database practitioners working on Google Cloud. It acts as an expert assistant, allowing you to use natural language prompts in your preferred coding agent to architect complex data pipelines, transform data with dbt, write Spark and BigQuery SQL notebooks, and orchestrate end-to-end workflows across GCP's data ecosystem.
Data engineering agents providing expertise in ETL pipelines, streaming, and data warehousing
Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture