---name: gke-node-notreadymetadata: version: 1.0.0category: Containersdescription: >----

GKE Node NotReady Troubleshooting Skill

Use this skill to systematically diagnose why one or more GKE nodes report a NotReady (or Ready: Unknown) status and to propose safe remediations. A NotReady status means the node's kubelet is not reporting to the control plane correctly, so Kubernetes stops scheduling new Pods on the node, which can reduce application capacity and cause downtime.

This skill operates non-interactively and enforces a read-only diagnostics boundary: gather evidence first, then propose a fix (a kubectl/gcloud command or a GitOps manifest change) for a human to apply. Never mutate the cluster, drain, delete, or recreate nodes automatically.

[!IMPORTANT] First rule out an expected NotReady: a node that is newly provisioning, upgrading, being repaired, cordoned, or scaling down will transiently report NotReady. Only treat it as a fault if it persists beyond the expected window.

🔍 Diagnostic Workflow

Step 0: Context discovery & time window

1. Parameter extraction — obtain project_id, cluster_name, cluster_location, and node_name non-interactively from the user prompt, active SETTINGS.md, or environment defaults (kubectl config current-context, gcloud config get-value project).
2. Credentials & fallback — attempt gcloud container clusters get-credentials {cluster_name} --location {cluster_location} --project {project_id}. If the cluster is unreachable or commands fail (sandbox/dry-run/offline), present the exact diagnostic commands for a human to run and continue the analysis from the reported symptoms.
3. Time window — determine {issue_time} (explicit, relative, or now) and center a 1-hour window around it (start = issue_time - 30m, end = issue_time + 30m) for all log/metric queries.

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Step 1: Identify NotReady nodes and gather initial status

# List nodes and spot NotReady status, node IPs, and container-runtime version.
kubectl get nodes -o wide

# Inspect the affected node's Conditions and Events (the primary clues).
kubectl describe node "{node_name}"

Equivalent via Cloud Logging (preferred when kubectl access is limited or for historical events). Open it as a Logs Explorer deep link — URL-encode the query and append the project and Step 0 time window: https://console.cloud.google.com/logs/query;query={URL_ENCODED_QUERY};timeRange={start}%2F{end}?project={project_id} (encode / as %2F, or use ;duration=PT1H for a rolling hour):

resource.type="k8s_node"
log_id("events")
resource.labels.node_name="{node_name}"
resource.labels.cluster_name="{cluster_name}"
resource.labels.location="{cluster_location}"

Interpret the Conditions table:

• Ready: False / Ready: Unknown with reason KubeletNotReady / NodeStatusUnknown ("Kubelet stopped posting node status") → kubelet or runtime problem; continue to Step 2.
• MemoryPressure: True, DiskPressure: True, PIDPressure: True → resource exhaustion; go to Step 4b.
• NetworkUnavailable: True → networking/CNI problem; go to Step 4d.

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Step 2: Scan kubelet logs for error signatures

Open these kubelet logs as a Logs Explorer deep link using the same logs/query;query={URL_ENCODED_QUERY};timeRange=...?project=... pattern as Step 1.

resource.type="k8s_node"
resource.labels.node_name="{node_name}"
resource.labels.cluster_name="{cluster_name}"
resource.labels.location="{cluster_location}"
log_id("kubelet")
severity>=WARNING

Also review the node's serial-console logs (log_id("serialconsole.googleapis.com/serial_port_1_output") or the resource.type="gce_instance" serial logs) for kernel TaskHung, OOM-killer, or disk I/O errors that correlate with the kubelet failures.

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Step 3: Map the signature to a root cause (decision table)

Kubelet / event signature	Likely root cause	Go to
runtime is down, Container runtime not ready, errors on /run/containerd/containerd.sock (connection refused / DeadlineExceeded)	Container runtime (containerd) down or unresponsive	Step 4a
Got sys oom event from cadvisor / kernel OOM-killer in serial logs	System (node-level) OOM killed critical processes	Step 4b
PLEG is not healthy	PLEG stalled, usually node overload (CPU/disk)	Step 4c
TaskHung for containerd/kubelet, high disk latency	Disk throttling / I/O starvation	Step 4b
failed to ensure lease, leases.coordination.k8s.io ... namespace kube-node-lease ... terminating	kube-node-lease termination → NotReady flapping	Step 4f
Kubelet cannot reach API server, TLS/dial timeouts	Kubelet ↔ control-plane connectivity	Step 4d
NetworkPluginNotReady, cni plugin not initialized, NetworkUnavailable	CNI plugin failure	Step 4d
Node-critical DaemonSet Pods (CNI, kube-proxy, metadata) blocked from admission	Admission webhook interference	Step 4e
Only generic NodeNotReady, no other signature	Cause unclear — widen to Step 4d, then escalate	Escalation

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Step 4: Branch investigations

4a. Container runtime (containerd) down

Confirm the kubelet cannot talk to containerd (socket errors above). Check for containerd restarts/crashes in serial logs. Remediation (propose, don't run): recreate/repair the node (kubectl drain then let the node pool recreate it, or gcloud container clusters upgrade/node auto-repair); if it recurs across nodes, suspect a node image or custom DaemonSet interfering with containerd.

4b. Resource pressure & OOM

# Node allocatable vs. usage.
kubectl describe node "{node_name}" | sed -n '/Allocated resources/,/Events/p'

Cloud Monitoring metrics to inspect (read-only): kubernetes.io/node/memory/used_bytes, kubernetes.io/node/cpu/core_usage_time, kubernetes.io/node/ephemeral_storage/used_bytes.

• DiskPressure / disk throttling: full boot disk or slow PD → increase disk size / use a faster PD type; reduce image/log churn.
• System OOM: node memory exhausted → set/raise Pod memory requests/limits, reduce over-commit, or use larger machine types. Distinguish system OOM (node-wide, kills kubelet/runtime) from cgroup OOM (single container).
• PIDPressure: too many processes → cap Pod PIDs / reduce workload density.

4c. PLEG is not healthy

PLEG is not healthy almost always means the node is overloaded (CPU saturation, disk latency, or too many Pods/containers per node) so the runtime can't relist in time. Correlate with 4b metrics. Remediation: reduce node density, add CPU/disk headroom, or spread workloads.

4d. Networking

# Are node-critical networking Pods healthy on this node?
kubectl get pods -n kube-system -o wide --field-selector spec.nodeName={node_name}

• Kubelet ↔ control-plane: dial/TLS timeouts to the API server → check firewall rules, Private Google Access, authorized networks, and route/NAT changes.
• CNI failure (NetworkPluginNotReady): the CNI DaemonSet (netd/calico/dataplane) is not running on the node → inspect those Pods' logs/events.

4e. Admission webhook interference

A misconfigured/failing validating or mutating webhook with a broad scope can block node-critical system Pods from being admitted, keeping the node NotReady.

kubectl get validatingwebhookconfigurations,mutatingwebhookconfigurations

Look for webhooks that intercept kube-system / node-critical objects with failurePolicy: Fail. Remediation (propose): scope the webhook out of kube-system/node-critical namespaces or set an appropriate namespaceSelector.

4f. kube-node-lease termination flapping

If the node flaps NotReady with leases.coordination.k8s.io ... namespace kube-node-lease ... is being terminated, the kube-node-lease namespace was deleted/terminating. Remediation (propose): do not delete the kube-node-lease namespace; if terminating, identify the finalizer/actor holding it and restore the namespace.

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Step 5: Remediation boundary & escalation

• Present the root cause + evidence (the exact conditions, events, log lines, or metrics observed). Provide Cloud Logging deep links (and Cloud Monitoring links for the Step 4b metrics) to the supporting entries — using the deep-link pattern from Steps 1-2 — so a human can open the evidence directly.
• Propose the fix as a command or GitOps manifest change for a human to apply — never apply, drain, or recreate nodes automatically. When to escalate (do this instead of proposing more self-service diagnostics):

Escalate when either:

• the relevant logs are unavailable — excluded by a logging filter, or older than the log bucket's retention (the _Default bucket defaults to 30 days, so incidents older than that are permanently deleted); or
• the kubelet/event signature is not in the Step 3 table and the root cause remains undetermined after the branch investigations.

In those cases, do all three:

1. State the limitation plainly (for example, "kubelet logs for that date are past the 30-day _Default retention window and are permanently deleted").
2. Summarize the findings you did gather (node conditions, events, metrics, and any Admin Activity audit logs still in the _Required bucket, default 400-day retention).
3. Route to GKE support / engineering escalation with those findings. Do not keep proposing further self-service investigation, and do not fabricate a diagnosis when the evidence is missing.

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References

This skill is derived from public Google Cloud documentation:

• Troubleshoot nodes with the NotReady status — node conditions and the kubelet / PLEG / system-OOM / containerd / kube-node-lease / CNI / admission-webhook signatures and their remediations.
• Troubleshoot node registration — Node Registration Checker for nodes that never finish registering.
• View GKE logs and Cloud Logging routing overview — log queries (resource.type="k8s_node", log_id("kubelet")) and log-bucket retention (_Default 30 days, _Required 400 days).
• Logs Explorer interface — building and sharing a query by URL (the logs/query;query=... deep-link format used above).