KubeShark: Failure-Mode Workflow for Kubernetes
SkillCloud & infraPrevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the KubeShark: Failure-Mode Workflow for Kubernetes skill
What this skill tells your AI
The instructions your AI receives, as published by lukasniessen/kubernetes-skill in SKILL.md and read by ahel’s review.
Run this workflow top to bottom.
1) Capture execution context
Record before writing manifests:
- cluster version (e.g. 1.30, 1.31) and distribution (EKS, GKE, AKS, k3s, vanilla)
- target namespace and environment criticality (dev/staging/prod)
- workload type (Deployment, StatefulSet, Job, CronJob, DaemonSet)
- deployment method (raw YAML, Helm, Kustomize, operator-managed)
- policy enforcement (Pod Security Admission level, Kyverno, OPA/Gatekeeper)
- cloud provider and CNI (affects networking, storage classes, load balancers)
- platform controllers/add-ons (GitOps, observability, ingress, service mesh, autoscaling)
If unknown, state assumptions explicitly.
2) Diagnose likely failure mode(s)
Select one or more based on user intent and risk:
- insecure workload defaults: missing security contexts, PSS violations, host access
- resource starvation: missing requests/limits, no PDB, scheduling chaos
- network exposure: flat networking, missing policies, wrong Service types, DNS issues
- privilege sprawl: overly permissive RBAC, leaked secrets, excess ServiceAccount rights
- fragile rollouts: misconfigured probes, mutable tags, unsafe update strategies
- API drift: wrong apiVersion, deprecated APIs, schema violations, tool-specific errors
3) Load only the relevant reference file(s)
Primary failure-mode references:
references/insecure-workload-defaults.mdreferences/resource-starvation.mdreferences/network-exposure.mdreferences/privilege-sprawl.mdreferences/fragile-rollouts.mdreferences/api-drift.md
Supplemental references (only when needed):
references/deployment-patterns.mdreferences/stateful-patterns.mdreferences/job-patterns.mdreferences/daemonset-operator-patterns.mdreferences/security-hardening.mdreferences/observability.mdreferences/multi-tenancy.mdreferences/storage-and-state.mdreferences/helm-patterns.mdreferences/kustomize-patterns.mdreferences/validation-and-policy.mdreferences/examples-good.mdreferences/examples-bad.mdreferences/do-dont-patterns.md
Conditional Reference Retrieval (CRR) references (load only when the signal is detected):
references/conditional/eks-patterns.mdfor EKS, AWS, IRSA, EKS Pod Identity, AWS Load Balancer Controller, EBS/EFS CSI, Karpenterreferences/conditional/gke-patterns.mdfor GKE, Autopilot, Workload Identity Federation for GKE, Dataplane V2, GCE Ingress, Config Syncreferences/conditional/aks-patterns.mdfor AKS, Microsoft Entra Workload ID, Azure CNI, AGIC, Azure Disk/File/Blob CSIreferences/conditional/openshift-patterns.mdfor OpenShift, OKD, ROSA, ARO, Routes, SCCs, OLM,ocreferences/conditional/gitops-controllers.mdfor Argo CD, ApplicationSet, Flux, GitOps reconciliation, sync wavesreferences/conditional/observability-stacks.mdfor Prometheus Operator, ServiceMonitor, PodMonitor, OpenTelemetry, Loki, Grafana
Do not load multiple CRR files unless the task spans multiple detected platforms/tools.
4) Propose fix path with explicit risk controls
For each fix, include:
- why this addresses the failure mode
- what could still go wrong at deploy time or runtime
- guardrails (validation commands, policy checks, rollback path)
5) Generate implementation artifacts
When applicable, output:
- Kubernetes manifests (YAML with security contexts, resource limits, labels)
- Helm values/templates or Kustomize overlays
- NetworkPolicies, RBAC resources, PodDisruptionBudgets
- Policy rules (Kyverno/OPA) and admission controls
6) Validate before finalize
Always provide validation steps tailored to deployment method and risk tier:
kubectl apply --dry-run=serverorkubectl diffkubeconformfor schema validation against target cluster version- cross-resource consistency check (label/selector/port alignment)
- policy scan (PSS profile check, Kyverno/OPA audit) Never recommend direct production apply without reviewed diff and approval.
7) Output contract
Return:
- assumptions and cluster version floor
- selected failure mode(s)
- chosen remediation and tradeoffs
- validation/test plan
- rollback/recovery notes (rollout undo, revision history, data safety)
Signals
- GitHub stars
- 409
- Forks
- 75
- Last commit
- Sep 2026
ahel review
S4info
community integration — published by lukasniessen, not kubernetes
Automated review, not a security audit. Ruleset v1.
Advanced
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kubernetes-skill- Source
- github.com/lukasniessen/kubernetes-skill