mcpbeat Sign in

Managing Astro Deployments Agent Skill

Manage Astronomer production deployments with Astro CLI. Use when the user wants to authenticate, switch workspaces, create/update/delete deployments, or deploy code to production.

2k tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
416
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/astronomer/agents --skill managing-astro-deployments

The instruction itself

21 sections, as written by the author

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:

# 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:

# 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

# 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

# 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

# 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

# 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):

astro deploy <DEPLOYMENT_ID>

Use when:

  • Dependencies changed (requirements.txt, packages.txt, Dockerfile)
  • First deployment of new project
  • Significant infrastructure changes

Deploy only DAG files, skip Docker image rebuild:

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:

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:

astro deploy <DEPLOYMENT_ID> --force

Caution: Skips validation that could prevent broken deployments.


Deployment API Tokens

Manage API tokens for programmatic access to deployments:

# 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

# 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

# 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

# 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

# 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)

  • troubleshooting-astro-deployments: Investigate deployment issues, view logs, manage environment variables
  • managing-astro-local-env: Manage local Airflow development environment
  • setting-up-astro-project: Initialize and configure Astro projects

Other skills for the same job

different authors, same section of the catalogue
Azure Kubernetes Automatic Readiness
by microsoft
vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

13k tokens
Capacity
by microsoft
vendor ×3

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

6k tokens scripts
Customize
by microsoft
vendor ×3

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

8k tokens
Deploy Model
by microsoft
vendor ×3

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

26k tokens scripts
Preset
by microsoft
vendor ×3

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

9k tokens
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take astronomer/managing-astro-deployments from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.