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Manus+14 moreKubernetes Config Error Detective
Audits Kubernetes manifests, Helm values, deployment logs, and service configs to detect configuration errors and produce safe, reviewable fix plans.
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You say
My Kubernetes Service returns 503 and has no endpoints. Review my Deployment and Service manifests. Identify the likely root cause, propose a safe YAML patch, and include validation steps. Do not provide destructive commands or expose secrets.
Your agent does
=== SERVICE SELECTOR DETECTIVE ===
Service: web-service
Namespace: default
Service selector: app: web
Expected pod labels from Deployment template: app: frontend
Match status: Mismatch
Issue: The Service selects Pods with label app=web, but the Deployment creates Pods with label app=frontend. Because the selector does not match the Pod labels, the Service has no endpoints. Ingress or internal traffic to this Service may return 503 because there are no ready backend Pods.
Evidence: Service selector: app: web
Deployment pod template labels: app: frontend
Suggested fix: Align the Service selector with the Pod template labels, or align the Pod labels with the Service selector. The safest minimal change is usually to update the Service selector if the Deployment selector is already established.
Proposed YAML patch: [The skill returns a minimal Service selector patch for human review, using no destructive commands and no secret values.]
Risk: Medium. Changing Service selectors can route traffic to a different set of Pods. Confirm that app=frontend identifies only the intended Pods.
Validation:
- Confirm Pods have label app=frontend.
- Confirm Service endpoints appear after the patch.
- Confirm readiness probes are passing.
- Confirm Ingress traffic reaches the expected Pods.
- Confirm no unintended Pods match the selector.
Do not:
- Delete Pods as the first fix.
- Change Deployment selector casually if it is immutable or already used by rollout history.
What you get
About this skill
Kubernetes Config Error Detective helps DevOps teams, cloud engineers, platform teams, SREs, developers, startups, and AI coding agents diagnose Kubernetes deployment failures caused by YAML and configuration mistakes. It audits manifests, Helm values, rendered charts, Kustomize overlays, GitOps sync errors, deployment events, sanitized logs, Services, Ingresses, probes, resource settings, image references, ConfigMap and Secret references, RBAC, storage, scheduling, and networking configuration. The skill identifies likely root causes for issues such as CrashLoopBackOff, ImagePullBackOff, Pending Pods, no Service endpoints, 503 errors, readiness failures, missing configuration references, wrong selectors, wrong ports, bad probes, resource constraints, and Helm values mismatches. It produces evidence-based diagnosis reports, safe YAML patch suggestions, validation checklists, rollback notes, production readiness reviews, and PR review comments without executing cluster changes.
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