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Gke Node Notready

OrganizationPopular
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.

Overview

Publishergoogle
Repositoryskills
Skill namegke-node-notready
Stars
20.5K
Forks
1.7K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

Install the Gke Node Notready AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/google/skills/tree/main/skills/cloud/gke-node-notready
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/cloud/gke-node-notready .claude/skills/gke-node-notready
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gke Node Notready in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gke Node Notready on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gke Node Notready is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Step 1: Identify NotReady nodes and gather initial status

bash
# 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
bash
# 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
bash
# 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.

bash
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:

Frequently asked questions

What does the Gke Node Notready AI skill do?

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.

Why use Gke Node Notready on TypingMind?

Because you install it once and use it with any model. Gke Node Notready is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gke Node Notready in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/skills/tree/main/skills/cloud/gke-node-notready. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gke Node Notready?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gke Node Notready?

As many as you like. As long as a model supports skills, you can use Gke Node Notready with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gke Node Notready AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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