
Troubleshooting Astro Deployments
- 445 installs
- 416 repo stars
- Updated August 4, 2026
- astronomer/agents
troubleshooting-astro-deployments is a Claude Code skill that guides developers through diagnosing and fixing failed Astronomer Astro Airflow deployments using the Astro CLI when production pipelines stall or crash.
About
troubleshooting-astro-deployments is a Claude Code skill for production incident response on Astronomer Astro. It walks developers through Astro CLI commands such as astro deployment list to locate deployments, inspect logs, analyze scheduler and worker failures, resolve DAG import errors, and adjust deployment environment variables. The skill complements managing-astro-deployments for lifecycle management and managing-astro-local-env for local reproduction. Developers reach for it when Airflow jobs fail after deploy, workers disappear from the cluster, or environment-specific runtime errors block scheduled DAG runs. It assumes familiarity with Astro-managed Airflow rather than self-hosted clusters.
- Deployment failure diagnosis
- Scheduler and worker debugging
- Log-driven root cause analysis
- DAG import error resolution
- Astro-specific recovery runbooks
Troubleshooting Astro Deployments by the numbers
- 445 all-time installs (skills.sh)
- Ranked #95 of 596 Debugging skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 445 |
|---|---|
| repo stars | ★ 416 |
| Last updated | August 4, 2026 |
| Repository | astronomer/agents ↗ |
How do you fix failed Astronomer Astro Airflow deployments?
Diagnose and resolve failed Astro Airflow deployments, scheduler faults, worker outages, DAG import errors, and environment-specific runtime failures.
Who is it for?
Data engineers and platform developers operating production Airflow pipelines on Astronomer Astro who need CLI-driven incident diagnosis.
Skip if: Teams building brand-new DAGs locally or managing initial deployment provisioning without an active production failure to investigate.
When should I use this skill?
A developer reports a failed Astro deployment, scheduler fault, worker outage, DAG import error, or needs production log analysis via Astro CLI.
What you get
Root-cause notes, corrected deployment configuration, resolved DAG import errors, and restored scheduler or worker health.
- Incident root-cause summary
- Corrected deployment configuration
By the numbers
- References 3 related Astronomer skills: troubleshooting, managing deployments, and local environment
Files
Astro Deployment Troubleshooting
This skill helps you diagnose and troubleshoot production Astronomer deployments using the Astro CLI.
For deployment management, see the managing-astro-deployments skill.
For local development, see the managing-astro-local-env skill.
---
Quick Health Check
Start with these commands to get an overview:
# 1. List deployments to find target
astro deployment list
# 2. Get deployment overview
astro deployment inspect <DEPLOYMENT_ID>
# 3. Check for errors
astro deployment logs <DEPLOYMENT_ID> --error -c 50---
Viewing Deployment Logs
Use -c to control log count (default: 500). Log flags cannot be combined — use one component or level flag per command.
Component-Specific Logs
View logs from specific Airflow components:
# Scheduler logs (DAG processing, task scheduling)
astro deployment logs <DEPLOYMENT_ID> --scheduler -c 50
# Worker logs (task execution)
astro deployment logs <DEPLOYMENT_ID> --workers -c 30
# Webserver logs (UI access, health checks)
astro deployment logs <DEPLOYMENT_ID> --webserver -c 30
# Triggerer logs (deferrable operators)
astro deployment logs <DEPLOYMENT_ID> --triggerer -c 30Log Level Filtering
Filter by severity:
# Error logs only (most useful for troubleshooting)
astro deployment logs <DEPLOYMENT_ID> --error -c 30
# Warning logs
astro deployment logs <DEPLOYMENT_ID> --warn -c 50
# Info-level logs
astro deployment logs <DEPLOYMENT_ID> --info -c 50Search Logs
Search for specific keywords:
# Search for specific error
astro deployment logs <DEPLOYMENT_ID> --keyword "ConnectionError"
# Search for specific DAG
astro deployment logs <DEPLOYMENT_ID> --keyword "my_dag_name" -c 100
# Find import errors
astro deployment logs <DEPLOYMENT_ID> --error --keyword "ImportError"
# Find task failures
astro deployment logs <DEPLOYMENT_ID> --error --keyword "Task failed"---
Complete Investigation Workflow
Step 1: Identify the Problem
# List deployments with status
astro deployment list
# Get deployment details
astro deployment inspect <DEPLOYMENT_ID>Look for:
- Status: HEALTHY vs UNHEALTHY
- Runtime version compatibility
- Resource limits (CPU, memory)
- Recent deployment timestamp
Step 2: Check Error Logs
# Start with errors
astro deployment logs <DEPLOYMENT_ID> --error -c 50Look for:
- Recurring error patterns
- Specific DAGs failing repeatedly
- Import errors or syntax errors
- Connection or credential errors
Step 3: Review Scheduler Logs
# Check DAG processing
astro deployment logs <DEPLOYMENT_ID> --scheduler -c 30Look for:
- DAG parse errors
- Scheduling delays
- Task queueing issues
Step 4: Check Worker Logs
# Check task execution
astro deployment logs <DEPLOYMENT_ID> --workers -c 30Look for:
- Task execution failures
- Resource exhaustion
- Timeout errors
Step 5: Verify Configuration
# Check environment variables
astro deployment variable list --deployment-id <DEPLOYMENT_ID>
# Verify deployment settings
astro deployment inspect <DEPLOYMENT_ID>Look for:
- Missing or incorrect environment variables
- Secrets configuration (AIRFLOW__SECRETS__BACKEND)
- Connection configuration
---
Common Investigation Patterns
Recurring DAG Failures
Follow the complete investigation workflow above, then narrow to the specific DAG:
astro deployment logs <DEPLOYMENT_ID> --keyword "my_dag_name" -c 100Resource Issues
# 1. Check deployment resource allocation
astro deployment inspect <DEPLOYMENT_ID>
# Look for: resource_quota_cpu, resource_quota_memory
# Worker queue: max_worker_count, worker_type
# 2. Check for worker scaling issues
astro deployment logs <DEPLOYMENT_ID> --workers -c 50
# 3. Look for out-of-memory errors
astro deployment logs <DEPLOYMENT_ID> --error --keyword "memory"Configuration Problems
# 1. Review environment variables
astro deployment variable list --deployment-id <DEPLOYMENT_ID>
# 2. Check for secrets backend configuration
# Look for: AIRFLOW__SECRETS__BACKEND, AIRFLOW__SECRETS__BACKEND_KWARGS
# 3. Verify deployment settings
astro deployment inspect <DEPLOYMENT_ID>
# 4. Check webserver logs for auth issues
astro deployment logs <DEPLOYMENT_ID> --webserver -c 30Import Errors
# 1. Find import errors
astro deployment logs <DEPLOYMENT_ID> --error --keyword "ImportError"
# 2. Check scheduler for parse failures
astro deployment logs <DEPLOYMENT_ID> --scheduler --keyword "Failed to import" -c 50
# 3. Verify dependencies were deployed
astro deployment inspect <DEPLOYMENT_ID>
# Check: current_tag, last deployment timestamp---
Environment Variables Management
List Variables
# List all variables for deployment
astro deployment variable list --deployment-id <DEPLOYMENT_ID>
# Find specific variable
astro deployment variable list --deployment-id <DEPLOYMENT_ID> --key AWS_REGION
# Export variables to file
astro deployment variable list --deployment-id <DEPLOYMENT_ID> --save --env .env.backupCreate Variables
# Create regular variable
astro deployment variable create --deployment-id <DEPLOYMENT_ID> \
--key API_ENDPOINT \
--value https://api.example.com
# Create secret (masked in UI and logs)
astro deployment variable create --deployment-id <DEPLOYMENT_ID> \
--key API_KEY \
--value secret123 \
--secretUpdate Variables
# Update existing variable
astro deployment variable update --deployment-id <DEPLOYMENT_ID> \
--key API_KEY \
--value newsecretDelete Variables
# Delete variable
astro deployment variable delete --deployment-id <DEPLOYMENT_ID> --key OLD_KEYNote: Variables are available to DAGs as environment variables. Changes require no redeployment.
---
Key Metrics from deployment inspect
Focus on these fields when troubleshooting:
- status: HEALTHY vs UNHEALTHY
- runtime_version: Airflow version compatibility
- scheduler_size/scheduler_count: Scheduler capacity
- executor: CELERY, KUBERNETES, or LOCAL
- worker_queues: Worker scaling limits and types
min_worker_count,max_worker_countworker_concurrencyworker_type(resource class)- resource_quota_cpu/memory: Overall resource limits
- dag_deploy_enabled: Whether DAG-only deploys work
- current_tag: Last deployment version
- is_high_availability: Redundancy enabled
---
Investigation Best Practices
1. Always start with error logs - Most obvious failures appear here 2. Check error logs for patterns - Same DAG failing repeatedly? Timing patterns? 3. Component-specific troubleshooting:
- Worker logs → task execution details
- Scheduler logs → DAG processing and scheduling
- Webserver logs → UI issues and health checks
- Triggerer logs → deferrable operator issues
4. Use `--keyword` for targeted searches - More efficient than reading all logs 5. The `inspect` command is your health dashboard - Check it first 6. Environment variables in `inspect` output - May reveal configuration issues 7. Log count default is 500 - Adjust with -c based on needs 8. Don't forget to check deployment time - Recent deploy might have introduced issue
---
Troubleshooting Quick Reference
| Symptom | Command |
|---|---|
| Deployment shows UNHEALTHY | astro deployment inspect <ID> + --error logs |
| DAG not appearing | --error logs for import errors, check --scheduler logs |
| Tasks failing | --workers logs + search for DAG with --keyword |
| Slow scheduling | --scheduler logs + check inspect for scheduler resources |
| UI not responding | --webserver logs |
| Connection issues | Check variables, search logs for connection name |
| Import errors | --error --keyword "ImportError" + --scheduler logs |
| Out of memory | inspect for resources + --workers --keyword "memory" |
---
Related Skills
- managing-astro-deployments: Create, update, delete deployments, deploy code
- managing-astro-local-env: Manage local Airflow development environment
- setting-up-astro-project: Initialize and configure Astro projects
Related skills
How it compares
Choose troubleshooting-astro-deployments over generic Airflow debugging when the failure occurs on Astronomer-managed Astro infrastructure and Astro CLI log access is required.
FAQ
What CLI does troubleshooting-astro-deployments use?
troubleshooting-astro-deployments uses the Astronomer Astro CLI. It starts with commands like astro deployment list to identify the target deployment before pulling production logs and analyzing scheduler, worker, or DAG import failures.
When should I use troubleshooting-astro-deployments vs managing-astro-deployments?
troubleshooting-astro-deployments is for investigating active production failures such as crashed workers or import errors. managing-astro-deployments handles routine deployment lifecycle tasks rather than deep incident diagnosis.