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name: deployment-helm-docker description: "Reason about Apache Airflow deployments with the official Helm chart and Docker images, including values, image customization, Dag delivery, logging, secrets, scaling, and safe rendering checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Airflow deployment with Helm and Docker

Use this sub-skill when the task is about deploying Airflow with the official Helm chart, extending or customizing official Docker images, reviewing chart values, planning Kubernetes operational choices, or debugging deployment failures.

Start here

1. Read references/deployment-workflows.md to choose the deployment path: local container image iteration, Helm on Kubernetes, Dag distribution, logs, secrets/config, or scaling.
2. Read references/helm-reference.md when reviewing or drafting chart values, rendering manifests safely, or checking component ownership.
3. Read references/docker-image-reference.md before advising on packages, providers, image tags, entrypoints, or custom image builds.
4. Read references/troubleshooting.md when pods fail to start, rendered manifests are wrong, Dags do not appear, imports fail in the scheduler, logs disappear, migrations hang, or autoscaling behaves oddly.
5. Use scripts/render_helm_values_summary.py for a no-cluster, no-Helm summary of a values file before suggesting deeper chart rendering.

Scope boundaries

This sub-skill covers:

• Official Airflow Docker image extension/customization, dependency installation timing, build arguments, entrypoint behavior, and image selection.
• Helm chart values for API server, scheduler, Dag processor, workers, triggerer, Redis, PgBouncer, metadata database, secrets, logging, Dag delivery, KEDA/HPA, and rendering checks.
• Safe validation commands such as local values summary, helm template, helm lint, helm diff, and chart test selection when the user is already changing chart code.
• Kubernetes deployment reasoning that does not require cluster access by default.

Do not use this sub-skill for:

• Day-to-day Airflow CLI, REST API, airflowctl, or metadata operations; route those to operations-cli-api.
• Task SDK authoring, Dag syntax, assets, or provider APIs except when deployment packaging affects imports.
• Broad provider implementation deep dives or generated client work.
• General contribution workflow, release workflow, or non-chart CI maintenance except chart-specific rendered manifest tests and generated-doc cautions.

High-signal reminders

• Prefer building an immutable image for production dependencies. Runtime pip install during pod startup is fragile, slow, and repeated on every container start.
• For Airflow 3 chart values, think in components: apiServer, scheduler, dagProcessor, triggerer, workers, and optional services such as Redis, PgBouncer, StatsD, and OpenTelemetry.
• Treat secrets as Secret references whenever possible. Avoid plain credentials in values files, and never echo secrets into handoffs or generated examples.
• Normal skill usage should not require a Kubernetes cluster. Start with values inspection and local rendering; use cluster commands only when the user explicitly asks for live troubleshooting.
• Use title-case Dag in prose. Preserve literal tokens such as dags.gitSync, airflow dags list, DAG, and dag_id exactly when they are code/config/CLI tokens.