Bootstrap an Astro data pipeline from scratch

---
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

```bash
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):

```dockerfile
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:

```yaml
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

```bash
# 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:

```bash
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)