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Gke Node Notready

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  • Cloud and DevOps
  • Apache-2.0
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From the author

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.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured diagnostic workflow for investigating GKE nodes that report a NotReady or Unknown status. It guides the user through gathering evidence from node conditions, events, kubelet logs, and system metrics to identify root causes such as resource pressure, container runtime failures, or networking issues. The skill operates with a read-only diagnostic boundary, ensuring that it only proposes safe remediations for human review rather than performing automated mutations on the cluster.

What it does

  • Extracts cluster and node context from user input or environment settings.
  • Analyzes node conditions and events to identify the primary cause of NotReady status.
  • Scans kubelet and serial console logs for specific error signatures like PLEG issues or OOM events.
  • Maps diagnostic findings to common root causes including resource exhaustion and CNI failures.
  • Provides deep links to Cloud Logging and Monitoring for efficient evidence review.
  • Proposes actionable remediation steps for human application.

When to use it

  • When GKE nodes report a NotReady or Unknown status.
  • When the kubelet stops posting node status to the control plane.
  • When workloads are evicted or stuck in Pending state due to node health issues.
  • When investigating persistent node health faults rather than transient provisioning states.

Advantages

  • Enforces a safe, read-only diagnostic boundary.
  • Provides a systematic, evidence-based approach to troubleshooting.
  • Reduces manual effort by generating direct links to relevant logs and metrics.
  • Clearly defines when to escalate issues to support.

Usage examples

“My GKE node node-1 is showing NotReady status, can you help?”
The agent will identify the node, inspect its conditions and events, check kubelet logs for error signatures, and provide a summary of the root cause with recommended remediation steps.
“Why is my GKE node stuck in Unknown status?”
The agent will analyze the node's recent events and logs to determine if the kubelet is failing to report status, potentially identifying issues like network connectivity or runtime crashes.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google/gke-node-notready/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip gke-node-notready.zip -d .agents/skills

Documentation by Google

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.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-node-notready)" and append agent-skills/1.0 (skill:gke-node-notready) to User-Agent headers.

🔍 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.


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.


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 signatureLikely root causeGo to
runtime is down, Container runtime not ready, errors on /run/containerd/containerd.sock (connection refused / DeadlineExceeded)Container runtime (containerd) down or unresponsiveStep 4a
Got sys oom event from cadvisor / kernel OOM-killer in serial logsSystem (node-level) OOM killed critical processesStep 4b
PLEG is not healthyPLEG stalled, usually node overload (CPU/disk)Step 4c
TaskHung for containerd/kubelet, high disk latencyDisk throttling / I/O starvationStep 4b
failed to ensure lease, leases.coordination.k8s.io ... namespace kube-node-lease ... terminatingkube-node-lease termination → NotReady flappingStep 4f
Kubelet cannot reach API server, TLS/dial timeoutsKubelet ↔ control-plane connectivityStep 4d
NetworkPluginNotReady, cni plugin not initialized, NetworkUnavailableCNI plugin failureStep 4d
Node-critical DaemonSet Pods (CNI, kube-proxy, metadata) blocked from admissionAdmission webhook interferenceStep 4e
Only generic NodeNotReady, no other signatureCause unclear — widen to Step 4d, then escalateEscalation

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.


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.


References

This skill is derived from public Google Cloud documentation:

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Node.js

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Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/gke-node-notready · commit 2194d3b
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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