airflow-dag
This skill generates Apache Airflow DAGs tailored for construction data workflows, handling everything from BIM model processing to validation and cost reporting. It provides a builder interface for composing tasks—bash commands, Python functions, file sensors, and branching logic—then outputs production-ready DAG code. Pre-built templates accelerate common patterns like BIM validation pipelines.
airflow-dag creates Apache Airflow DAGs to orchestrate construction ETL pipelines from BIM extraction through reporting.
AI-generated summary based on this skill's SKILL.md
Install
datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/airflow-dag · repository language: Python
git clone https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction
cp -r DDC_Skills_for_AI_Agents_in_Construction/2_DDC_Book/4.2-ETL-Automation/airflow-dag ~/.claude/skills/airflow-dagnpx skillfed install datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/airflow-dagFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I create Apache Airflow DAG construction workflows?
airflow-dag generates Apache Airflow DAGs tailored for construction data workflows. It provides a builder interface where you compose tasks—bash commands, Python functions, file sensors, and branching logic—then outputs production-ready DAG code. The skill handles everything from BIM model processing to validation and cost reporting, making it easy to orchestrate complex construction ETL pipelines.
What templates does airflow-dag offer for construction pipelines?
airflow-dag includes pre-built templates that accelerate common construction patterns. These templates support BIM validation pipelines, cost estimation workflows, and batch conversion processes. The templates provide a foundation you can customize with your own tasks, sensors, and branching logic, reducing the time needed to build production-ready construction data pipelines.
Can airflow-dag automate batch conversion of CAD files?
Yes, airflow-dag supports automating batch conversion of CAD files using Airflow operators. The skill lets you configure data pipeline tasks with sensors, branching, and error handling to manage file conversions at scale. You can generate Python code for complex task dependencies and scheduling to orchestrate your construction data workflows efficiently.
What operators and features does airflow-dag support?
airflow-dag supports a range of Airflow operators including bash, Python, and file sensors. You can configure task dependencies, implement branching logic, and add error handling to your construction workflows. The skill generates the complete Python code needed to define these operators and their relationships, enabling sophisticated orchestration of construction ETL pipelines.
How does airflow-dag help with BIM validation and cost estimation?
airflow-dag provides pre-built templates specifically designed for BIM validation and cost estimation workflows. These templates streamline the process of building pipelines that extract, validate, and process BIM data, then generate cost reports. You can use the builder interface to customize these templates for your construction projects' specific requirements.
Is airflow-dag open source and what license does it use?
Yes, airflow-dag is released under the MIT license, making it free to use, modify, and distribute. The MIT license allows you to integrate airflow-dag into your construction automation projects with minimal restrictions, whether for commercial or personal use.
SKILL.md
rendered from the published skill — quoted content, verbatim
Apache Airflow DAG for Construction
Overview
Apache Airflow orchestrates complex data pipelines. This skill creates DAGs for construction ETL processes - from BIM extraction to cost reports.
Python Implementation
```python from datetime import datetime, timedelta from typing import Dict, Any, List, Optional, Callable from dataclasses import dataclass from enum import Enum import json
class TaskStatus(Enum): """Task execution status.""" PENDING = "pending" RUNNING = "running" SUCCESS = "success" FAILED = "failed" SKIPPED = "skipped"
@dataclass class DAGTask: """Single task in DAG.""" task_id: str operator: str params: Dict[str, Any] upstream: List[str] downstream: List[str]
@dataclass class DAGConfig: """DAG configuration.""" dag_id: str schedule: str start_date:
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2_DDC_Book/4.2-ETL-Automation/airflow-dag/SKILL.md
2_DDC_Book/4.2-ETL-Automation/airflow-dag/claw.json
2_DDC_Book/4.2-ETL-Automation/airflow-dag/instructions.md