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Adapt Airflow workflow definitions for newer versions in Google Cloud's managed service. The skill guides checks for incompatible code and dependencies, explains required changes, and supports requested deployment and verification for Airflow 2.11.1 or Airflow 3.
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Helps create and extend Apache Airflow DAGs, workflows that define task order and dependencies, for Managed Service for Apache Airflow. Covers environment checks, version compatibility, coding practices, and local or deployed validation.
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Helps add or update translations for the Apache Airflow interface. Covers language setup, translation file generation, consistent terminology, preserved variables and plural forms, and checks for missing, unfinished, or unused entries.
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Build Apache Airflow data pipelines with clear task dependencies and schedules. This skill guides workflow design, retries, monitoring, local testing, and production deployment, with patterns for investigating failed runs.
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Guides preparation and authorized publication of Apache Airflow changes, including existing pull request checks, remote verification, reviews, tests, rebasing, and a prefilled submission page. Also explains how to track work deferred by temporary fixes.
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Helps contributors develop and fix Airflow’s Java SDK for JVM tasks. Explains public API boundaries, JAR packaging, Python coordination, communication schema updates, and the test commands used to validate changes.
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Guides contributors through adding a new programming language to Airflow, covering SDK structure, runtime coordination, communication rules, executable packaging, tests, and the documentation and configuration changes needed for a pull request.
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Draft commit messages, pull request titles and descriptions, and GitHub comments or reviews for Apache Airflow contributions. Follows project wording and AI attribution rules, and helps decide whether a change needs a release note.
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Update Flask-AppBuilder in Apache Airflow’s FAB provider, including pinned versions, dependency locks, and documentation. Check compatibility tests and review copied security code, with asset and database migration updates when the upstream release requires them.
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Build Apache Airflow workflows for data pipelines and scheduled jobs. The skill guides task dependencies, retries, waiting for conditions, testing, and deployment, with patterns for tasks that can be safely rerun.
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Investigate failed workflows and tasks in Managed Service for Apache Airflow. The skill compares remote logs, task states, and source code to explain failures, show supporting evidence, and recommend specific code or configuration fixes.
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Assess Airflow Improvement Proposals by comparing Confluence specifications with pull requests, commits, and codebase files. Produce evidence-backed progress reports showing delivered items, remaining work, and specification updates needed across one or more proposals.
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Plan data ingestion and transformation workflows with Apache Airflow, Spark, and dbt. Get guidance on processing changed records, validating data quality, and rerunning failed or historical jobs without creating duplicate results.
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Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.
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Turn Airflow Improvement Proposals into practical guides checked against current code, or proposed user stories for design review before implementation. Organize use cases, distinguish verified examples from speculative code, and capture specific design questions.
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Prepare Apache Airflow provider releases by classifying pending changes, inspecting pull request diffs for compatibility risks, and applying version bumps and changelog entries. Use existing breeze tooling to regenerate release documentation and consult the release manager on uncertain decisions.
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Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.
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Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an approach by whether run history and the same environment (URL/ARN) must be kept: a new-environment upgrade (blue-green), a rehearsed in-place upgrade validated on a test copy, or a direct in-place upgrade. Runs Ruff scanning and deprecation-warning log scans for 3.x moves, plus Docker validation, batched deployment, and switchover. Saves a resumable upgrade plan for multi-session work. Triggers on: upgrade MWAA, upgrade Airflow, migrate to Airflow 3, MWAA Airflow 3, Airflow 2 to 3, preserve Airflow history, keep same MWAA environment, blue-green cutover, validation environment before cutover. Not for authoring new DAGs (authoring-mwaa-workflow), debugging unrelated DAG failures (debugging-mwaa-workflow), or MWAA Serverless YAML workflows (provisioned Python DAGs only).
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Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
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Provides guidance for migrating Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers migration to Airflow 2.11.1 (MSAA Gen 2 and 3) and Airflow 3 (MSAA Gen 3), including environment inspection, GCS download/upload and scanning patterns for breaking changes. Use when migrating the DAG code to newer Airflow version. Don't use when checking DAG run failures unrelated to code migration.
中文简介正在准备中。当前显示原文说明。
Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Design and implement systems that collect, transform, validate, and store data for analysis. Covers scheduled and real-time processing, data warehouses, quality checks, monitoring, and cost tuning using tools such as Apache Spark, dbt, and Airflow.
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