By choo121600
Data engineering plugin - warehouse exploration, pipeline authoring, Airflow integration
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.
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.
Use when turning a dbt Core project into an Airflow DAG/TaskGroup using Astronomer Cosmos. Does not cover dbt Fusion. Before implementing, verify dbt engine, warehouse, Airflow version, execution environment, DAG vs TaskGroup, and manifest availability.
Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, HITLTrigger. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
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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
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Complete collection of battle-tested Claude Code configs from an Anthropic hackathon winner - agents, skills, hooks, and rules evolved over 10+ months of intensive daily use