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/setting-up-astro-project

Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.

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Install
$ npx -y skills add astronomer/agents --skill setting-up-astro-project --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/setting-up-astro-project

Context preview

The summary Claude sees to decide when to auto-load this skill.

Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.

SKILL.md

setting-up-astro-project.SKILL.md
name: setting-up-astro-project
description: Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.

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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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

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