agents

AI agent tooling for data engineering workflows. Extends code agents and IDEs like Claude Code and Cursor with specialized capabilities for working with Airflow and data warehouses.

Built by Astronomer.

Table of Contents

• Installation
  • Quick Start
  • Compatibility
  • Claude Code
  • Cursor
  • Other MCP Clients
• Features
  • MCP Server
  • Skills
  • User Journeys
• Configuration
  • Warehouse Connections
  • Airflow
• Usage
  • Getting Started
• Development
  • Local Development Setup
  • Adding Skills
• Troubleshooting
  • Common Issues
• Contributing
• License

Installation

Quick Start

npx skills add astronomer/agents

This installs Astronomer skills into your project via skills.sh. Works with Claude Code, Cursor, and other AI coding tools.

Compatibility

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.

Claude Code

# Add the marketplace and install the plugin
claude plugin marketplace add astronomer/agents
claude plugin install 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

Cursor supports both MCP servers and skills.

MCP Server - Click to install:

Skills - Install to your project:

npx skills add astronomer/agents

This installs skills to .cursor/skills/ in your project.

Manual MCP configuration

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "airflow": {
      "command": "uvx",
      "args": ["astro-airflow-mcp", "--transport", "stdio"]
    }
  }
}

Enable hooks (skill suggestions, session management)

Create .cursor/hooks.json in your project:

{
  "version": 1,
  "hooks": {
    "beforeSubmitPrompt": [
      {
        "command": "$CURSOR_PROJECT_DIR/.cursor/skills/airflow/hooks/airflow-skill-suggester.sh",
        "timeout": 5
      }
    ],
    "stop": [
      {
        "command": "uv run $CURSOR_PROJECT_DIR/.cursor/skills/analyzing-data/scripts/cli.py stop",
        "timeout": 10
      }
    ]
  }
}

What these hooks do:

• beforeSubmitPrompt: Suggests data skills when you mention Airflow keywords
• stop: Cleans up kernel when session ends

Other MCP Clients

For 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

Features

The data plugin bundles an MCP server and skills into a single installable package.

MCP Server

Server	Description
Airflow	Full Airflow REST API integration via astro-airflow-mcp: DAG management, triggering, task logs, system health

Skills

Data Discovery & Analysis

Skill	Description
init	Initialize schema discovery - generates .astro/warehouse.md for instant lookups
analyzing-data	SQL-based analysis to answer business questions (uses background Jupyter kernel)
checking-freshness	Check how current your data is
profiling-tables	Comprehensive table profiling and quality assessment

Data Lineage

Skill	Description
tracing-downstream-lineage	Analyze what breaks if you change something
tracing-upstream-lineage	Trace where data comes from

DAG Development

Skill	Description
airflow	Main entrypoint - routes to specialized Airflow skills
setting-up-astro-project	Initialize and configure new Astro/Airflow projects
managing-astro-local-env	Manage local Airflow environment (start, stop, logs, troubleshoot)
authoring-dags	Create and validate Airflow DAGs with best practices
testing-dags	Test and debug Airflow DAGs locally
debugging-dags	Deep failure diagnosis and root cause analysis

Migration

Skill	Description
migrating-airflow-2-to-3	Migrate DAGs from Airflow 2.x to 3.x

User Journeys

Data Analysis Flow

/data:init → /data:analyzing-data → /data:profiling-tables
                    ↓
            /data:checking-freshness

1. Initialize (/data:init) - One-time setup to generate warehouse.md with schema metadata
2. Analyze (/data:analyzing-data) - Answer business questions with SQL
3. Profile (/data:profiling-tables) - Deep dive into specific tables for statistics and quality
4. Check freshness (/data:checking-freshness) - Verify data is up to date before using

DAG Development Flow

/data:setting-up-astro-project → /data:authoring-dags → /data:testing-dags
           ↓                                                    ↓
/data:managing-astro-local-env                      /data:debugging-dags

1. Setup (/data:setting-up-astro-project) - Initialize project structure and dependencies
2. Environment (/data:managing-astro-local-env) - Start/stop local Airflow for development
3. Author (/data:authoring-dags) - Write DAG code following best practices
4. Test (/data:testing-dags) - Run DAGs and fix issues iteratively
5. Debug (/data:debugging-dags) - Deep investigation for complex failures

Configuration

Warehouse Connections

Configure data warehouse connections at ~/.astro/ai/config/warehouse.yml:

my_warehouse:
  type: snowflake
  account: ${SNOWFLAKE_ACCOUNT}
  user: ${SNOWFLAKE_USER}
  auth_type: private_key
  private_key_path: ~/.ssh/snowflake_key.p8
  private_key_passphrase: ${SNOWFLAKE_PRIVATE_KEY_PASSPHRASE}
  warehouse: COMPUTE_WH
  role: ANALYST
  databases:
    - ANALYTICS
    - RAW

Store credentials in ~/.astro/ai/config/.env:

SNOWFLAKE_ACCOUNT=xyz12345
SNOWFLAKE_USER=myuser
SNOWFLAKE_PRIVATE_KEY_PASSPHRASE=your-passphrase-here  # Only required if using an encrypted private key

Supported warehouses: Snowflake.

Airflow

The Airflow MCP auto-discovers your project when you run Claude Code from an Airflow project directory (contains airflow.cfg or dags/ folder).

For remote instances, set environment variables:

Variable	Description
AIRFLOW_API_URL	Airflow webserver URL
AIRFLOW_USERNAME	Username
AIRFLOW_PASSWORD	Password
AIRFLOW_AUTH_TOKEN	Bearer token (alternative to username/password)

Usage

Skills are invoked automatically based on what you ask. You can also invoke them directly with /data:<skill-name>.

Getting Started

1. Initialize your warehouse (recommended first step):

   /data:init
   

   This generates .astro/warehouse.md with schema metadata for faster queries.

2. Ask questions naturally:

   • "What tables contain customer data?"
   • "Show me revenue trends by product"
   • "Create a DAG that loads data from S3 to Snowflake daily"
   • "Why did my etl_pipeline DAG fail yesterday?"

Development

See CLAUDE.md for plugin development guidelines.

Local Development Setup

# Clone the repo
git clone https://github.com/astronomer/agents.git
cd agents

# Test with local plugin
claude --plugin-dir .

# Or install from local marketplace
claude plugin marketplace add .
claude plugin install data@astronomer

Adding Skills

Create a new skill in skills/<name>/SKILL.md with YAML frontmatter:

---
name: my-skill
description: When to invoke this skill
---

# Skill instructions here...

After adding skills, reinstall the plugin:

claude plugin uninstall data@astronomer && claude plugin install data@astronomer

Troubleshooting

Common Issues

Issue	Solution
Skills not appearing	Reinstall plugin: claude plugin uninstall data@astronomer && claude plugin install data@astronomer
Warehouse connection errors	Check credentials in ~/.astro/ai/config/.env and connection config in warehouse.yml
Airflow not detected	Ensure you're running from a directory with airflow.cfg or a dags/ folder

Contributing

Contributions welcome! See CLAUDE.md for development guidelines.

License

Apache 2.0

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Made with ❤️ by Astronomer