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google/gke-node-notready

google

gke-node-notready

Diagnoses GKE nodes reporting NotReady or Unknown status by inspecting node conditions, events, kubelet/containerd logs, and node metrics, then proposing safe remediations. Use when nodes show NotReady, when the kubelet stops posting node status, or when workloads are evicted or stuck Pending due to node health. Don't use for pod-level application failures (use gke-workload-troubleshooting), autoscaler scale-up/scale-down decisions (use gke-cluster-autoscaler), or non-GKE compute.

v1.0LATEST
NewUpdated Sep 4, 2026

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.

๐Ÿ” Diagnostic Workflow

Step 0: Context discovery & time window

  1. Parameter extraction โ€” 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 โ€” 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 โ€” 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.

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 โ€” 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") โ†’ kubelet or runtime problem; continue to Step 2.
  • MemoryPressure: True, DiskPressure: True, PIDPressure: True โ†’ resource exhaustion; go to Step 4b.
  • NetworkUnavailable: True โ†’ networking/CNI problem; go to Step 4d.

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.


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 โ†’ NotReady flapping Step 4f
Kubelet cannot reach API server, TLS/dial timeouts Kubelet โ†” 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 โ€” widen to Step 4d, then escalate Escalation

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 โ†’ increase disk size / use a faster PD type; reduce image/log churn.
  • System OOM: node memory exhausted โ†’ 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 โ†’ 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 โ†” control-plane: dial/TLS timeouts to the API server โ†’ 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 โ†’ 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.


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 โ€” using the deep-link pattern from Steps 1-2 โ€” so a human can open the evidence directly.
  • Propose the fix as a command or GitOps manifest change for a human to apply โ€” 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 โ€” 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.

References

This skill is derived from public Google Cloud documentation:

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Overall Score

88/100

Grade

A

Excellent

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

92

Quality

88

Clarity

88

Completeness

82

Summary

This skill diagnoses GKE nodes reporting NotReady or Unknown status by systematically gathering evidence through kubectl, Cloud Logging, and metrics queries. It maps diagnostic signatures to root causes (containerd failures, resource exhaustion, PLEG stalls, networking issues, admission webhooks, etc.) and proposes safe remediations without mutating the cluster. The skill explicitly enforces a read-only diagnostics boundary and escalates when evidence is insufficient.

Detected Capabilities

read kubectl outputread Cloud Logging queriesread Cloud Monitoring metricsquery node conditions and eventsgenerate diagnostic deep links (Cloud Logging, Cloud Monitoring)propose kubectl/gcloud commands for human executionpropose GitOps manifest changes

Trigger Keywords

Phrases that agents use to match this skill to user intent.

gke node notreadydiagnose node failureskubelet stopped postinggke troubleshootingnode health investigationcontainerd issues gkepleg is not healthygke node conditions

Risk Signals

INFO

Credential handling via 'gcloud container clusters get-credentials' โ€” requires GCP auth but is standard GCP CLI usage

Step 0, credentials & fallback section
INFO

Read-only access to sensitive system Pods and namespaces (kube-system, kube-node-lease) โ€” needed for diagnostics, no writes

Step 4d, 4e (kubectl get pods, get validatingwebhookconfigurations)
INFO

Cloud Logging and Cloud Monitoring query construction via URL encoding โ€” no external data transmission, only read queries

Steps 1-2, 4b (deep link patterns)

Referenced Domains

External domains referenced in skill content, detected by static analysis.

cloud.google.comconsole.cloud.google.comwww.apache.org

Use Cases

  • Investigate why one or more GKE nodes report NotReady status and identify the underlying cause
  • Distinguish between transient NotReady states (provisioning, upgrades, cordoning) and persistent failures
  • Gather diagnostic evidence from kubelet logs, node events, and metrics to root-cause node health issues
  • Identify whether failures stem from container runtime, resource pressure, PLEG staleness, networking, or admission webhooks
  • Escalate to GKE support with structured evidence when root cause cannot be determined from available logs

Quality Notes

  • Excellent scope documentation: skill explicitly excludes pod-level failures, autoscaler decisions, and non-GKE compute, reducing confusion about when to apply it
  • Strong safety boundary: 'never mutate, drain, delete, or recreate nodes automatically' is stated prominently and reinforced throughout; all proposed remediations are presented as commands for human approval
  • Comprehensive decision table (Step 3) maps kubelet/event signatures to root causes with clear navigation โ€” enables systematic root-cause mapping
  • Evidence-first approach: Steps 1-2 gather all diagnostic data before proposing fixes, reducing risk of incorrect remediation
  • Escalation criteria are well-defined (Step 5): explicitly states when logs are unavailable or root cause undetermined, preventing fabrication of diagnoses
  • Branch investigations (Step 4a-4f) are thorough and include specific metrics, log searches, and kubectl queries for each failure mode
  • Excellent reference documentation: links to official Google Cloud troubleshooting guides and Cloud Logging interface docs establish authoritative grounding
  • Time window scoping (Step 0, 3) is precise: ยฑ30 min around issue time enables focused log analysis and reduces noise
  • Cloud Logging deep-link pattern is reusable and well-documented, enabling human verification of findings
  • Parameter extraction (Step 0) covers multiple fallback sources (user prompt, SETTINGS.md, kubectl config, gcloud config), improving robustness when defaults are missing
  • Edge case handling: explicitly addresses offline/sandbox/dry-run scenarios by presenting diagnostic commands for human execution when cluster is unreachable
  • Step 4e (admission webhooks) is particularly strong โ€” identifies a subtle but important cause of NotReady that many operators miss
Model: claude-haiku-4-5-20251001Analyzed: Sep 4, 2026

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