# dag-factory

This skill walks your agent through writing Apache Airflow DAGs as dag-factory YAML configs instead of Python. Handy when you want a declarative pipeline fast.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 387 installs · ★ 456
- Author: astronomer
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/astronomer/agents/tree/HEAD/skills/dag-factory/SKILL.md
- Updated: 2026-10-14
- HTML: https://affpapa.org/en/nevibecode/skills/dag-factory

## What it does

The skill guides an agent through dag-factory, a library that turns YAML files into Apache Airflow DAGs. It lays out the order of work: check versions, create the `dags/load_dags.py` loader, then define the DAG in YAML. Tasks, task groups, dependencies, schedules, callbacks and dynamic task mapping are all covered.

You need Python 3.10+ and Airflow 2.4+. The skill targets dag-factory v1.0 and later, so an older project has to be upgraded first, following `reference/migration.md`. Installing `dag-factory` does not pull in Airflow providers — add those separately when your YAML references them.

There is a fork in the road too. For reusable Pydantic templates the skill points to blueprint; for full Python freedom it points to the authoring-dags skill. So dag-factory is the pick when you want low-code config rather than arbitrary code.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add astronomer/agents --skill dag-factory -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/dag-factory/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/dag-factory/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add astronomer/agents --skill dag-factory -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/dag-factory/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add astronomer/agents --skill dag-factory -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/dag-factory/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install astronomer/agents dag-factory
```

2. **Manually: put the files in place**
- File: `.github/skills/dag-factory/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add astronomer/agents --skill dag-factory -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/astronomer/agents --path skills/dag-factory
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/dag-factory/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add astronomer/agents --skill dag-factory -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/astronomer/agents/tree/HEAD/skills/dag-factory/SKILL.md

## When to use it
- define a DAG in YAML instead of a Python file
- share common settings across DAGs with a `default` block
- wire up task groups and their dependencies
- plug in a custom or provider operator
- validate a dag-factory config before running it

## Example prompts
- Create a dag-factory YAML DAG with three BashOperator tasks, where the second and third depend on the first
- Set up dag-factory in this project: add the package and create `dags/load_dags.py`
- Move start_date and retries into the `default` block so every DAG in the file picks them up

## Source text · SKILL.md · Apache-2.0

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BEGIN THIRD-PARTY FILE b469f4d2e2f62ba9
````text
---
name: dag-factory
description: Authors Apache Airflow DAGs declaratively from dag-factory YAML configs. Use when building DAGs declaratively from YAML via dag-factory; creating/editing dag-factory templates/YAML configs,reating/editing dag-factory YAML configs, defaults, dynamic tasks, datasets, or callbacks; or validating dag-factory configurations; upgrading or re-pinning dag-factory.
---
# DAG Factory

You are helping a user build Apache Airflow DAGs declaratively with **dag-factory**, a library that turns YAML configuration files into Airflow DAGs. Execute steps in order and prefer the simplest configuration that meets the user's needs.

> **Package**: `dag-factory` on PyPI
> **Repo**: https://github.com/astronomer/dag-factory
> **Docs**: https://astronomer.github.io/dag-factory/latest/
> **Targets**: dag-factory **v1.0+** only. For pre-1.0 projects, see [reference/migration.md](reference/migration.md) before applying any guidance from this skill.
> **Requires**: Python 3.10+, Airflow 2.4+ (Airflow 3 supported)

## Before Starting

Confirm with the user:
1. **Airflow version** ≥2.4
2. **Python version** ≥3.10
3. **dag-factory version**: this skill targets **v1.0+**. If the project is on <1.0, follow [reference/migration.md](reference/migration.md) to upgrade before continuing.
4. **Use case**: dag-factory is for declarative, low-code DAG authoring. If the user needs reusable, validated Pythonic templates with Pydantic, suggest **blueprint** instead. If they need full Python flexibility, suggest the **authoring-dags** skill.

---

## Determine What the User Needs

| User Request | Action |
|--------------|--------|
| "Create a YAML DAG" / "Convert this Python DAG to YAML" | Go to **Defining a DAG in YAML** |
| "Set up dag-factory in my project" | Go to **Project Setup** |
| "Share defaults across DAGs" / "Set start_date once" | Go to **Defaults** |
| "Use a custom operator" / "Use KPO / Slack / Snowflake" | Go to **Custom & Provider Operators** |
| "Dynamic / mapped tasks" / "expand / partial" | Go to **Dynamic Task Mapping** |
| "Schedule on dataset" / "Outlets and inlets" | Go to **Datasets** |
| "Add a callback" / "Slack on failure" | Go to **Callbacks** |
| "Use a timetable" / "datetime in YAML" / "timedelta in YAML" | Go to **Custom Python Objects (`__type__`)** |
| "Lint my YAML" / "Validate" | Go to **Validation Commands** |
| "Convert Airflow 2 YAML to Airflow 3" | Go to **Validation Commands** (`dagfactory convert`) |
| "Migrate from dag-factory <1.0" | See [reference/migration.md](reference/migration.md) |
| dag-factory errors / troubleshooting | Go to **Troubleshooting** |

---

## Project Setup

### 1. Install the Package

Add to `requirements.txt`:

```
dag-factory>=1.0.0
```
````
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---
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