airflow-1-basic-dag-structure

Sub-skill of airflow: 1. Basic DAG Structure.

5 stars

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

$curl -o ~/.claude/skills/1-basic-dag-structure/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/operations/automation/airflow/1-basic-dag-structure/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/1-basic-dag-structure/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How airflow-1-basic-dag-structure Compares

Feature / Agentairflow-1-basic-dag-structureStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/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="""

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