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- Add date_added to all 950+ skills for complete tracking - Update version to 6.5.0 in package.json and README - Regenerate all indexes and catalog - Sync all generated files Features from merged PR #150: - Stars/Upvotes system for community-driven discovery - Auto-update mechanism via START_APP.bat - Interactive Prompt Builder - Date tracking badges - Smart auto-categorization All skills validated and indexed. Made-with: Cursor
45 lines
1.4 KiB
Markdown
45 lines
1.4 KiB
Markdown
---
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name: airflow-dag-patterns
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description: "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."
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risk: unknown
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source: community
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date_added: "2026-02-27"
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---
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# Apache Airflow DAG Patterns
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Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies.
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## Use this skill when
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- Creating data pipeline orchestration with Airflow
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- Designing DAG structures and dependencies
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- Implementing custom operators and sensors
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- Testing Airflow DAGs locally
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- Setting up Airflow in production
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- Debugging failed DAG runs
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## Do not use this skill when
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- You only need a simple cron job or shell script
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- Airflow is not part of the tooling stack
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- The task is unrelated to workflow orchestration
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## Instructions
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1. Identify data sources, schedules, and dependencies.
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2. Design idempotent tasks with clear ownership and retries.
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3. Implement DAGs with observability and alerting hooks.
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4. Validate in staging and document operational runbooks.
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Refer to `resources/implementation-playbook.md` for detailed patterns, checklists, and templates.
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## Safety
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- Avoid changing production DAG schedules without approval.
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- Test backfills and retries carefully to prevent data duplication.
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## Resources
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- `resources/implementation-playbook.md` for detailed patterns, checklists, and templates.
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