managing-astro-deployments
This skill enables management of Astronomer production deployments using the Astro CLI, including authentication, workspace switching, and creation, inspection, updating, and deletion of deployments. Use it when users need to authenticate with Astronomer, organize deployments across workspaces, configure deployment settings like executor type and resource allocation, or deploy code changes to production environments.
git clone --depth 1 https://github.com/astronomer/agents /tmp/managing-astro-deployments && cp -r /tmp/managing-astro-deployments/skills/managing-astro-deployments ~/.claude/skills/managing-astro-deploymentsskill.md
# Astro Deployment Management This skill helps you manage production Astronomer deployments using the Astro CLI. > **For local development**, see the **managing-astro-local-env** skill. > **For production troubleshooting**, see the **troubleshooting-astro-deployments** skill. --- ## Authentication All deployment operations require authentication: ```bash # Login to Astronomer (opens browser for OAuth) astro login ``` Authentication tokens are stored locally for subsequent commands. Run this before any deployment operations. --- ## Workspace Management Deployments are organized into workspaces: ```bash # List all accessible workspaces astro workspace list # Switch to a specific workspace astro workspace switch <WORKSPACE_ID> ``` Workspace context is maintained between sessions. Most deployment commands operate within the current workspace context. --- ## List and Inspect Deployments ```bash # List deployments in current workspace astro deployment list # List deployments across all workspaces astro deployment list --all # Inspect specific deployment (detailed info) astro deployment inspect <DEPLOYMENT_ID> # Inspect by name (alternative to ID) astro deployment inspect --deployment-name data-service-stg ``` ### What `inspect` Shows - Deployment status (HEALTHY, UNHEALTHY) - Runtime version and Airflow version - Executor type (CELERY, KUBERNETES, LOCAL) - Scheduler configuration (size, count) - Worker queue settings (min/max workers, concurrency, worker type) - Resource quotas (CPU, memory) - Environment variables - Last deployment timestamp and current tag - Webserver and API URLs - High availability status --- ## Create Deployments ```bash # Create with default settings astro deployment create # Create with specific executor astro deployment create --label production --executor celery astro deployment create --label staging --executor kubernetes # Executor options: # - celery: Best for most production workloads # - kubernetes: Best for dynamic scaling, isolated tasks # - local: Best for development only ``` --- ## Update Deployments ```bash # Enable DAG-only deploys (faster iteration) astro deployment update <DEPLOYMENT_ID> --dag-deploy-enabled # Update other settings (use --help for full options) astro deployment update <DEPLOYMENT_ID> --help ``` --- ## Delete Deployments ```bash # Delete a deployment (requires confirmation) astro deployment delete <DEPLOYMENT_ID> ``` **Destructive**: This cannot be undone. All DAGs, task history, and metadata will be lost. --- ## Deploy Code to Production ### Full Deploy Deploy both DAGs and Docker image (required when dependencies change): ```bash astro deploy <DEPLOYMENT_ID> ``` Use when: - Dependencies changed (`requirements.txt`, `packages.txt`, `Dockerfile`) - First deployment of new project - Significant infrastructure changes ### DAG-Only Deploy (Recommended for Iteration) Deploy only DAG files, skip Docker image rebuild: ```bash astro deploy <DEPLOYMENT_ID> --dags ``` Use when: - Only DAG files changed (Python files in `dags/` directory) - Quick iteration during development - Much faster than full deploy (seconds vs minutes) **Requires**: `--dag-deploy-enabled` flag set on deployment (see Update Deployments) ### Image-Only Deploy Deploy only Docker image, skip DAG sync: ```bash astro deploy <DEPLOYMENT_ID> --image-only ``` Use when: - Only dependencies changed - Dockerfile or requirements updated - No DAG changes ### Force Deploy Bypass safety checks and deploy: ```bash astro deploy <DEPLOYMENT_ID> --force ``` **Caution**: Skips validation that could prevent broken deployments. --- ## Deployment API Tokens Manage API tokens for programmatic access to deployments: ```bash # List tokens for a deployment astro deployment token list --deployment-id <DEPLOYMENT_ID> # Create a new token astro deployment token create \ --deployment-id <DEPLOYMENT_ID> \ --name "CI/CD Pipeline" \ --role DEPLOYMENT_ADMIN # Create token with expiration astro deployment token create \ --deployment-id <DEPLOYMENT_ID> \ --name "Temporary Access" \ --role DEPLOYMENT_ADMIN \ --expiry 30 # Days until expiration (0 = never expires) ``` **Roles**: - `DEPLOYMENT_ADMIN`: Full access to deployment **Note**: Token value is only shown at creation time. Store it securely. --- ## Common Workflows ### First-Time Production Deployment ```bash # 1. Login astro login # 2. Switch to production workspace astro workspace list astro workspace switch <PROD_WORKSPACE_ID> # 3. Create deployment astro deployment create --label production --executor celery # 4. Note the deployment ID, then deploy astro deploy <DEPLOYMENT_ID> ``` ### Iterative DAG Development ```bash # 1. Enable fast deploys (one-time setup) astro deployment update <DEPLOYMENT_ID> --dag-deploy-enabled # 2. Make DAG changes locally # 3. Deploy quickly astro deploy <DEPLOYMENT_ID> --dags ``` ### Promoting Code from Staging to Production ```bash # 1. Deploy to staging first astro workspace switch <STAGING_WORKSPACE_ID> astro deploy <STAGING_DEPLOYMENT_ID> # 2. Test in staging # 3. Deploy same code to production astro workspace switch <PROD_WORKSPACE_ID> astro deploy <PROD_DEPLOYMENT_ID> ``` --- ## Configuration Management ```bash # View CLI configuration astro config get # Set configuration value astro config set <KEY> <VALUE> # Check CLI version astro version # Upgrade CLI to latest version astro upgrade ``` --- ## Tips - Use `--dags` flag for fast iteration (seconds vs minutes) - Always test in staging workspace before production - Use `deployment inspect` to verify deployment health before deploying - Deployment IDs are permanent, names can change - Most commands work with deployment ID; `inspect` also accepts `--deployment-name` - Set `--dag-deploy-enabled` once per deployment for fast deploys - Keep workspace context visible with `astro workspace list` (shows asterisk for current) --- ## Related Skills - **troubleshooting-astr
Add a new method to both Airflow adapters
Add a new MCP tool to server.py
Verify code works with both Airflow 2.x and 3.x
Airflow adapter pattern for v2/v3 API compatibility. Use when working with adapters, version detection, or adding new API methods that need to work across Airflow 2.x and 3.x.
Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, HITLTrigger. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.