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Skill389 estrellas del repoactualizado 3d ago

setting-up-astro-project

This skill provides step-by-step guidance for initializing new Astro/Airflow projects using the Astro CLI, configuring project structure, managing Python and OS-level dependencies, and setting up connections and variables through airflow_settings.yaml. Use it when users need to scaffold a new project, add packages, or configure authentication for data sources before running the local development environment.

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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

```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)
add-adapter-methodSlash Command

Add a new method to both Airflow adapters

add-toolSlash Command

Add a new MCP tool to server.py

check-airflow-compatSlash Command

Verify code works with both Airflow 2.x and 3.x

airflow-adapterSkill

Airflow adapter pattern for v2/v3 API compatibility. Use when working with adapters, version detection, or adding new API methods that need to work across Airflow 2.x and 3.x.

airflow-hitlSkill

Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, HITLTrigger. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).

airflow-pluginsSkill

Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.

airflowSkill

Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.

analyzing-dataSkill

Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.