setting-up-astro-project
Astro/Airflow 프로젝트를 초기화하고 구성하여 종속성, 연결 및 환경 설정을 처리합니다. astro dev init으로 DAG, 플러그인, 테스트, 설정 파일 디렉토리를 포함한 완전한 프로젝트 구조를 스캐폴딩합니다. requirements.txt와 packages.txt를 통해 Python 및 OS 수준 종속성을 관리하고, 복잡한 설정을 위한 사용자 정의 Dockerfile을 지원합니다. airflow_settings.yaml에서 연결, 변수, 풀을 선언적으로 구성하며, 환경을 위한 내보내기/가져오기 명령을 제공합니다...
npx skills add https://github.com/astronomer/agents --skill setting-up-astro-projectAstro 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-airflowskill.
Initialize a New Project
astro dev init
Don't pass
--airflow-versionor--runtime-versionunless the user explicitly asks for a specific pin. Plainastro dev initresolves 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 generatedDockerfileafterward 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)