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Diagnoses failed Maestro cluster deployments by analyzing Helm releases, pod status, and resource conflicts
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Based on SOC occupation classification
This skill should be used when tracing ManifestWork resources through the Maestro system to find relationships between user-created work names, resource IDs, and applied manifests, or to debug manifest application issues across the management cluster and database.
This skill should be used when tracing resource requests through the Maestro system to debug resource lifecycle issues, track request flow from client to agent and back, or troubleshoot failures in resource creation, update, or deletion operations across the server-agent-server pipeline. Supports both Kubernetes log files and ARO HCP Kusto CSV exports.
Sets up a long-running Maestro cluster environment using Azure ARO-HCP infrastructure with both service and management clusters
Runs end-to-end or upgrade tests on existing long-running Maestro clusters deployed in Azure AKS
| name | diagnose-maestro-deployment |
| description | Diagnoses failed Maestro cluster deployments by analyzing Helm releases, pod status, and resource conflicts |
| category | Troubleshooting |
| tags | ["azure","aks","maestro","troubleshooting","debugging","helm","kubernetes"] |
Automatically diagnoses failed Maestro cluster deployments by:
Prerequisites:
Usage:
# Diagnose using deployment output file
diagnose-maestro-deployment /path/to/deployment.output
# Diagnose using cluster names directly
diagnose-maestro-deployment --svc-rg <resource-group> --svc-cluster <cluster-name> --mgmt-rg <resource-group> --mgmt-cluster <cluster-name>
#!/bin/bash
# Execute the diagnostic script
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
exec "$SCRIPT_DIR/scripts/diagnose.sh" "$@"