airflow-1-basic-dag-structure
Sub-skill of airflow: 1. Basic DAG Structure.
Best use case
airflow-1-basic-dag-structure is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of airflow: 1. Basic DAG Structure.
Teams using airflow-1-basic-dag-structure should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/1-basic-dag-structure/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How airflow-1-basic-dag-structure Compares
| Feature / Agent | airflow-1-basic-dag-structure | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of airflow: 1. Basic DAG Structure.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 1. Basic DAG Structure
## 1. Basic DAG Structure
```python
# dags/basic_dag.py
"""
Basic DAG demonstrating core Airflow concepts.
"""
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.operators.bash import BashOperator
from airflow.operators.empty import EmptyOperator
# Default arguments for all tasks
default_args = {
'owner': 'data-team',
'depends_on_past': False,
'email': ['alerts@example.com'],
'email_on_failure': True,
'email_on_retry': False,
'retries': 3,
'retry_delay': timedelta(minutes=5),
'retry_exponential_backoff': True,
'max_retry_delay': timedelta(minutes=30),
'execution_timeout': timedelta(hours=2),
}
# DAG definition
with DAG(
dag_id='basic_etl_pipeline',
default_args=default_args,
description='Basic ETL pipeline demonstrating core patterns',
schedule_interval='0 6 * * *', # Daily at 6 AM
start_date=datetime(2026, 1, 1),
catchup=False,
max_active_runs=1,
tags=['etl', 'production'],
doc_md="""Related Skills
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