How do I install setting-up-astro-project?

Run `npx skills add https://github.com/astronomer/agents --skill setting-up-astro-project` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does setting-up-astro-project support?

setting-up-astro-project works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is setting-up-astro-project free to use?

Yes. setting-up-astro-project is free to install and use. It is available from the open Skills registry published by astronomer.

Productivity

setting-up-astro-project▌

Name: setting-up-astro-project
Availability: InStock
Author: astronomer

astronomer/agents · updated Apr 8, 2026

$npx skills add https://github.com/astronomer/agents --skill setting-up-astro-project

summary

Initialize and configure Astro/Airflow projects with dependencies, connections, and environment setup.

›Scaffolds complete project structure with astro dev init , including directories for DAGs, plugins, tests, and configuration files
›Manage Python and OS-level dependencies via requirements.txt and packages.txt , with custom Dockerfile support for complex setups
›Configure connections, variables, and pools declaratively in airflow_settings.yaml , with export/import commands for environ

skill.md

Astro Project Setup

This skill helps you initialize and configure Airflow projects using the Astro CLI.

To run the local environment, see the managing-astro-local-env skill. To write DAGs, see the authoring-dags skill. Open-source alternative: If the user isn't on Astro, guide them to Apache Airflow's Docker Compose quickstart for local dev and the Helm chart for production. For deployment strategies, use the deploying-airflow skill.

Initialize a New Project

astro dev init

Creates this structure:

project/
├── dags/                # DAG files
├── include/             # SQL, configs, supporting files
├── plugins/             # Custom Airflow plugins
├── tests/               # Unit tests
├── Dockerfile           # Image customization
├── packages.txt         # OS-level packages
├── requirements.txt     # Python packages
└── airflow_settings.yaml # Connections, variables, pools

Adding Dependencies

Python Packages (requirements.txt)

apache-airflow-providers-snowflake==5.3.0
pandas==2.1.0
requests>=2.28.0

OS Packages (packages.txt)

gcc
libpq-dev

Custom Dockerfile

For complex setups (private PyPI, custom scripts):

FROM quay.io/astronomer/astro-runtime:12.4.0

RUN pip install --extra-index-url https://pypi.example.com/simple my-package

After modifying dependencies: Run astro dev restart

Configuring Connections & Variables

airflow_settings.yaml

Loaded automatically on environment start:

airflow:
  connections:
    - conn_id: my_postgres
      conn_type: postgres
      host: host.docker.internal
      port: 5432
      login: user
      password: pass
      schema: mydb

  variables:
    - variable_name: env
      variable_value: dev

  pools:
    - pool_name: limited_pool
      pool_slot: 5

Export/Import

# Export from running environment
astro dev object export --connections --file connections.yaml

# Import to environment
astro dev object import --connections --file connections.yaml

Validate Before Running

Parse DAGs to catch errors without starting the full environment:

astro dev parse

Related Skills

managing-astro-local-env: Start, stop, and troubleshoot the local environment
authoring-dags: Write and validate DAGs (uses MCP tools)
testing-dags: Test DAGs (uses MCP tools)
deploying-airflow: Deploy DAGs to production (Astro, Docker Compose, Kubernetes)