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setting-up-astro-project

setting-up-astro-project

github.com/astronomerData & databases4571.1k installsUpdated 3w ago
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About setting-up-astro-project

A skill for initializing and configuring Airflow projects with the Astro CLI. It covers project structure, dependencies, settings, and DAG parsing for developers starting Astro projects.

Key features

  1. 01Initialize projects with Astro CLI
  2. 02Add Python and OS packages
  3. 03Configure connections and variables
  4. 04Export and import Airflow objects
  5. 05Parse DAGs before running

Use cases

  1. 01Starting a new Astro Airflow project
  2. 02Adding project dependencies
  3. 03Loading connection settings locally
  4. 04Checking DAG syntax before execution

Install

Copy the command and run it in a terminal.

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

After installing, type/setting-up-astro-projectin Claude Code to confirm it loaded.

Details

From the project README

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

Don't pass --airflow-version or --runtime-version unless the user explicitly asks for a specific pin. Plain astro dev init resolves to the latest Astro Runtime — that's the right default. Specifying a version risks pinning to a stale value from training data. If the user wants to know what was installed, read the generated Dockerfile afterward instead of guessing.

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)

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