By astronomer
Manage the full Airflow data engineering lifecycle: author, test, debug, and deploy DAGs; profile and query data warehouses; trace lineage; migrate between Airflow versions; and manage local and production deployments via the Astro CLI.
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.
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.
Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.
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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
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