Kubernetes Config Error Detective
Audits Kubernetes manifests, Helm values, deployment logs, and service configs to detect configuration errors and produce safe, reviewable fix plans.
- Audit Helm values and rendered manifests for configuration mismatches.
- Troubleshoot 503 errors by auditing Service selectors and Pod labels.
- Fix CrashLoopBackOff by analyzing probes, resources, and startup logs.
$50
· or 250 creditsSecure checkout via Stripe
Included in download
- Audit Helm values and rendered manifests for configuration mismatches.
- Troubleshoot 503 errors by auditing Service selectors and Pod labels.
- file_read, file_write automation included
- Ready for Compatible with ChatGPT Custom GPTs
Sample input
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.
Sample output
=== 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.
Kubernetes Config Error Detective
Audits Kubernetes manifests, Helm values, deployment logs, and service configs to detect configuration errors and produce safe, reviewable fix plans.
$50
· or 250 creditsSecure checkout via Stripe
Included in download
- Audit Helm values and rendered manifests for configuration mismatches.
- Troubleshoot 503 errors by auditing Service selectors and Pod labels.
- file_read, file_write automation included
- Ready for Compatible with ChatGPT Custom GPTs
- Instant install
Sample input
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.
Sample output
=== 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.
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.
Use Cases
- Audit Helm values and rendered manifests for configuration mismatches.
- Troubleshoot 503 errors by auditing Service selectors and Pod labels.
- Fix CrashLoopBackOff by analyzing probes, resources, and startup logs.
- Review Kubernetes Pull Requests for security and production readiness.
- Diagnose scheduling issues and resource quota constraints.
Known Limitations
This skill provides Kubernetes configuration triage, manifest audits, and safe fix recommendations, but it does not execute cluster changes, apply manifests, access live production clusters, reveal Secret values, approve production rollouts, or replace platform engineer review. Kubernetes behavior may depend on cluster version, admission controllers, CRDs, cloud provider, service mesh, ingress controller, policy engines, GitOps tools, RBAC, node pools, and runtime conditions not visible in static manifests. Validate all changes in staging or an approved environment before production.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/kubernetes-config-error-detective -o /tmp/kubernetes-config-error-detective.zip && unzip -o /tmp/kubernetes-config-error-detective.zip -d ~/.claude/skills && rm /tmp/kubernetes-config-error-detective.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Security Scanned
Passed automated security review
Permissions
File Scopes
This skill uses file access to read user-provided Kubernetes manifests, Helm charts, values files, rendered manifests, Kustomize bases and overlays, GitOps configurations, CI/CD logs, sanitized Kubernetes events, deployment logs, PR diffs, and platform documentation. It uses write access to create structured Markdown/text outputs such as Kubernetes configuration audits, failure diagnosis reports, safe YAML patch suggestions, validation checklists, PR review comments, production readiness reviews, incident summaries, runbooks, and SKILL.md files. Browser access is optional and should only be used for public documentation research when explicitly requested. The default safe setup does not require network access, shell access, environment variable access, Kubernetes write access, production cluster access, or Secret-value access.
Tags
Compatible with ChatGPT Custom GPTs, ChatGPT Agents, Claude-style workflows, Cursor, Claude Code, Codex CLI, OpenCode, Replit, platform engineering workflows, Kubernetes manifest review workflows, GitOps workflows, and other AI agent systems that support structured Markdown instruction files such as SKILL.md. It can also be used manually in any AI chat by pasting the instructions. For real clusters, use only authorized read-only diagnostics, sanitized logs, approved manifests, staging validation, and human review before applying changes.