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Apache Airflow DAG Patterns

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.

techwavedev/agi-agent-kit0installs4starsDevOps & CI/CD

SKILL.md

Full skill instructions

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

  1. 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.
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AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.

# Check for prior infrastructure context before starting
python3 execution/​memory_manager.py auto --query "deployment configuration and patterns for Airflow Dag Patterns"

Storing Results

After completing work, store infrastructure decisions for future sessions:

python3 execution/​memory_manager.py store \
  --content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
  --type technical --project <project> \
  --tags airflow-dag-patterns devops

Multi-Agent Collaboration

Broadcast deployment changes so frontend and backend agents update their configurations accordingly.

python3 execution/​cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Deployed infrastructure changes — updated CI/​CD pipeline with new health check endpoints" \
  --project <project>

Playbook Integration

Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.

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