How do I install the Airflow Dag Patterns skill?

Run npx skillmds add thedixitjain/airflow-dag-patterns in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Airflow Dag Patterns safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Airflow Dag Patterns?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Airflow Dag Patterns free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Airflow Dag Patterns?

thedixitjain (@thedixitjain) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Airflow Dag Patterns by thedixitjain

Name: Airflow Dag Patterns
Rating: 4.2 (5 reviews)
Author: thedixitjain

Airflow Dag Patterns

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

thedixitjain 9602218 2 files · 15.9 KB Updated Sep 8, 2026 2 repo stars

File contents

Apache Airflow DAG Patterns

Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies.

Use this skill when

Creating data pipeline orchestration with Airflow
Designing DAG structures and dependencies
Implementing custom operators and sensors
Testing Airflow DAGs locally
Setting up Airflow in production
Debugging failed DAG runs

Do not use this skill when

You only need a simple cron job or shell script
Airflow is not part of the tooling stack
The task is unrelated to workflow orchestration

Instructions

Identify data sources, schedules, and dependencies.
Design idempotent tasks with clear ownership and retries.
Implement DAGs with observability and alerting hooks.
Validate in staging and document operational runbooks.

Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.

Safety

Avoid changing production DAG schedules without approval.
Test backfills and retries carefully to prevent data duplication.

Resources

resources/implementation-playbook.md for detailed patterns, checklists, and templates.

Limitations

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Source: sickn33/agentic-awesome-skills → skills/airflow-dag-patterns/SKILL.md

Also appears in: sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills/skills/airflow-dag-patterns/SKILL.md, sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/airflow-dag-patterns/SKILL.md, sickn33/agentic-awesome-skills/plugins/agentic-bundle-data-engineering/skills/airflow-dag-patterns/SKILL.md, sickn33/agentic-awesome-skills/plugins/agentic-bundle-aas-data-engineering-platform/skills/airflow-dag-patterns/SKILL.md

---name: airflow-dag-patternsdescription: Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.---# Apache Airflow DAG PatternsProduction-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies.## Use this skill when- Creating data pipeline orchestration with Airflow- Designing DAG structures and dependencies- Implementing custom operators and sensors- Testing Airflow DAGs locally- Setting up Airflow in production- Debugging failed DAG runs## Do not use this skill when- You only need a simple cron job or shell script- Airflow is not part of the tooling stack- The task is unrelated to workflow orchestration## Instructions1. Identify data sources, schedules, and dependencies.2. Design idempotent tasks with clear ownership and retries.3. Implement DAGs with observability and alerting hooks.4. Validate in staging and document operational runbooks.Refer to `resources/implementation-playbook.md` for detailed patterns, checklists, and templates.## Safety- Avoid changing production DAG schedules without approval.- Test backfills and retries carefully to prevent data duplication.## Resources- `resources/implementation-playbook.md` for detailed patterns, checklists, and templates.## Limitations- Use this skill only when the task clearly matches the scope described above.- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.---**Source:** [`sickn33/agentic-awesome-skills`](https://github.com/sickn33/agentic-awesome-skills) → `skills/airflow-dag-patterns/SKILL.md`**Also appears in:** `sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills/skills/airflow-dag-patterns/SKILL.md`, `sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/airflow-dag-patterns/SKILL.md`, `sickn33/agentic-awesome-skills/plugins/agentic-bundle-data-engineering/skills/airflow-dag-patterns/SKILL.md`, `sickn33/agentic-awesome-skills/plugins/agentic-bundle-aas-data-engineering-platform/skills/airflow-dag-patterns/SKILL.md`

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