84 件 ・ 関連度順
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Guide for contributing to the Airflow Java SDK (AIP-108). Use this skill whenever a contributor is working in the `java-sdk/` directory or on the Java coordinator in `task-sdk/src/airflow/sdk/coordinators/java/` — whether they want to add a feature, write tests, fix a bug, understand the architecture, or prepare a PR. Trigger on phrases like "Java SDK", "JavaCoordinator", "java-sdk", "annotation processor", "Builder.Task", "BundleBuilder", or anything about running JVM tasks in Airflow.
日本語の概要は準備中です。原文の説明を表示しています。
Add or update translations for the Apache Airflow UI. Guides through setting up locales, scaffolding translation files, translating with locale-specific guidelines, and validating results. Use when working with i18n tasks in airflow-core/src/airflow/ui/public/i18n/locales/.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Guide for implementing a brand-new language SDK for Airflow (AIP-108). Use this skill when a contributor wants to add support for a new programming language — designing the Python coordinator, implementing the wire protocol in the target language, writing the bundle format, and structuring the PR. Trigger on phrases like "new language SDK", "new SDK", "add support for [language]", "implement coordinator for", "SubprocessCoordinator", "BaseCoordinator", "new runtime", "AFBNDL01", "supervisor schema", or anything about bringing a new language into the Airflow executor ecosystem.
日本語の概要は準備中です。原文の説明を表示しています。
Provides guidance for troubleshooting Apache Airflow DAGs (failed DAG runs and task instances) in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Use when figuring out reasons for DAG run or task instance failures. Don't use when looking for overall recommendations for Managed Airflow environment performance.
日本語の概要は準備中です。原文の説明を表示しています。
Provides guidance for troubleshooting Apache Airflow DAGs (failed DAG runs and task instances) in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Use when figuring out reasons for DAG run or task instance failures. Don't use when looking for overall recommendations for Managed Airflow environment performance.
日本語の概要は準備中です。原文の説明を表示しています。
Apache Airflow DAGs, operators, SLA monitoring, and workflow orchestration. Activate on: Airflow, DAG, operator, sensor, scheduler, task dependency, SLA, backfill, XCom. NOT for: dbt transformations (use dbt-analytics-engineer), streaming pipelines (use streaming-pipeline-architect).
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Airflow — apache Airflow workflow orchestration management. Covers DAG management, task instance monitoring, executor health, pool status, variable management, connection audit, and scheduler diagnostics. Use when checking DAG run status, investigating task failures, managing Airflow resources, or auditing configurations.
日本語の概要は準備中です。原文の説明を表示しています。
Use when working with Apache Airflow Deep — apache Airflow deep management covering DAG inventory, task instance monitoring, scheduler health, executor status, pool and variable management, connection auditing, and SLA miss tracking. Use when investigating DAG failures, analyzing task duration trends, monitoring scheduler performance, or auditing Airflow configurations.
日本語の概要は準備中です。原文の説明を表示しています。
Apache Airflow DAGs, operators, SLA monitoring, and workflow orchestration. Activate on: Airflow, DAG, operator, sensor, scheduler, task dependency, SLA, backfill, XCom. NOT for: dbt transformations (use dbt-analytics-engineer), streaming pipelines (use streaming-pipeline-architect).
日本語の概要は準備中です。原文の説明を表示しています。
Draft Apache Airflow commit messages, PR titles and descriptions, or GitHub comments and reviews. Also use for release-note decisions when preparing a contribution.
日本語の概要は準備中です。原文の説明を表示しています。
Prepare or carry out authorized publication of Apache Airflow changes, including remote checks, pre-push verification, PR creation, and tracking deferred work. Also use when planning these steps or managing contribution issues.
日本語の概要は準備中です。原文の説明を表示しています。
Apply airflow dag patterns in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses airflow dag patterns or its strengths fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. Learn to write DAGs, use operators, set up connections, configure scheduling, and deploy with Docker Compose.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Track Airflow Improvement Proposal (AIP) implementation progress by comparing Confluence specs against codebase evidence. Use when asked to assess, report on, or compare AIP status.
日本語の概要は準備中です。原文の説明を表示しています。
Upgrade the pinned Flask-AppBuilder (FAB) dependency in the Apache Airflow FAB provider (`providers/fab/`). Bumps the exact `flask-appbuilder==` pin and its mirror constant, regenerates `uv.lock`, drives the `test_fab_alignment.py` drift tripwire, reviews the vendored security-manager `override.py` against the new upstream FAB, and conditionally re-vendors static assets / DB migrations. Use when asked to "upgrade FAB", "bump flask-appbuilder", or move the FAB provider to a newer Flask-AppBuilder release.
日本語の概要は準備中です。原文の説明を表示しています。