Gke Ai Troubleshooting Node Unresponsive Timeout logo

Gke Ai Troubleshooting Node Unresponsive Timeout

OrganizationPopular
google
gke-ai-troubleshooting-node-unresponsive-timeout

Diagnose and mitigate GKE TPU or GPU nodes stuck in NotReady / NodeStatusUnknown ("Kubelet stopped posting node status") due to host kernel panics, hardware lockups, or disabled node auto-repair. Use when nodes stop heartbeating beyond the node auto-repair threshold and pods remain stuck in Terminating. Don't use for healthy nodes, pod-only application crashes, or routine GKE upgrades.

Overview

Publishergoogle
Repositoryskills
Skill namegke-ai-troubleshooting-node-unresponsive-timeout
Stars
21.2K
Forks
1.8K
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 Ai Troubleshooting Node Unresponsive Timeout 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-ai-troubleshooting-node-unresponsive-timeout
  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-ai-troubleshooting-node-unresponsive-timeout .claude/skills/gke-ai-troubleshooting-node-unresponsive-timeout
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gke Ai Troubleshooting Node Unresponsive Timeout 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 Ai Troubleshooting Node Unresponsive Timeout 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 Ai Troubleshooting Node Unresponsive Timeout 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.

Troubleshoot unresponsive GKE TPU and GPU nodes (NodeStatusUnknown)

When the Compute Engine host of a TPU or GPU node has a fatal hardware error, kernel panic, or non-maskable interrupt (NMI) lockup, the guest OS stops responding. The kubelet can no longer send heartbeats, so the node Ready condition becomes Unknown with Reason: NodeStatusUnknown (Kubelet stopped posting node status.). If node auto-repair is disabled on the node pool, GKE doesn't repair the node. The node can stay NotReady, and pods on it can stay in Terminating, which blocks multi-host JobSet workloads from recovering.

Prerequisites

  • Tools: Install the Google Cloud SDK (gcloud) and kubectl.
  • Cloud Billing & Project Configuration: Verify an active billing account is linked (gcloud billing projects describe {project_id}), authenticate (gcloud auth login), set the target project (gcloud config set project {project_id}), and ensure container.googleapis.com, compute.googleapis.com, logging.googleapis.com, and monitoring.googleapis.com are enabled.
  • Required IAM Roles:
    • Kubernetes Engine Viewer (roles/container.viewer)
    • Compute Viewer (roles/compute.viewer)
    • Logs Viewer (roles/logging.viewer)
    • Monitoring Viewer (roles/monitoring.viewer)
    • For remediation ([High Risk] steps): Kubernetes Engine Cluster Admin (roles/container.clusterAdmin)
  • Documentation:

Read-only rule: Run read-only diagnostic commands only. Never drain, delete, or re-create nodes, or run any other command that changes the cluster. Give the user any fix to apply themselves.

When you recommend a fix, link the doc section that describes it.


Diagnostic workflow

Step 0: Collect context and set the investigation window [Low Risk]

Collect the target parameters. By default, query a 60-minute window `[T - 30m, T

  • 30m]around{issue_time}`:
  • {project_id}: Google Cloud project ID
  • {cluster_name}: GKE cluster name
  • {location}: Cluster region or zone
  • {nodepool_name}: Target TPU or GPU node pool name
  • {node_name}: Unresponsive GKE node name (and its Compute Engine {zone})
  • {issue_time}: Incident timestamp in RFC3339 UTC
  • {start_time}: {issue_time} - 30m
  • {end_time}: {issue_time} + 30m

Step 1: Verify the NodeStatusUnknown heartbeat timeout [Low Risk]

  1. Check Kubernetes node conditions: To inspect the node status and verify whether the Ready condition is Unknown with Reason: NodeStatusUnknown (Kubelet stopped posting node status.), follow the instructions in the section Check the node's status and conditions.
  2. Query Cloud Logging (read-only LQL): Query k8s_node and k8s_cluster logs across [{start_time}, {end_time}] to confirm when the control plane lost heartbeat contact with {node_name}:
(resource.type="k8s_node" OR resource.type="k8s_cluster")
resource.labels.cluster_name="{cluster_name}"
("{node_name}" AND ("NodeNotReady" OR "NodeStatusUnknown" OR "Kubelet stopped posting node status"))
timestamp >= "{start_time}" AND timestamp <= "{end_time}"
  1. Query Cloud Monitoring (read-only PromQL): Correlate the duration of the Unknown state using the GKE system metric kubernetes.io/node/status_condition (kubernetes_io:node_status_condition, GKE 1.32.1-gke.1357001+) documented in Monitor health metrics for TPU nodes and node pools, filtered by condition="Ready" and status="Unknown":
promql
kubernetes_io:node_status_condition{
  monitored_resource="k8s_node",
  cluster_name="{cluster_name}",
  node_name="{node_name}",
  condition="Ready",
  status="Unknown"
}
  • Decision logic:
    • If the node Ready condition is True and NodeStatusUnknown is absent, rule out an unresponsive node timeout and pivot to workload-level troubleshooting (for example, Troubleshoot OOM events) rather than repairing or draining the node.
    • If Ready is Unknown (NodeStatusUnknown), proceed to Step 2.

Step 2: Inspect serial port output for a kernel panic [Low Risk]

The guest OS can't send logs after a fatal kernel freeze, so check the serial port output of the node's VM:

  • Cloud Logging: If serial port logging is enabled (i.e. VM metadata serial-port-logging-enable is set to true), GKE system logs in Cloud Logging include the node's serial port output. See the section System logs. Use Cloud Logging when the VM is stopped or has already been replaced by auto-repair, or when you need more than the most recent output.
  • Running VM: Follow Viewing serial port output to retrieve the serial port 1 output (gcloud compute instances get-serial-port-output with --port=1) for {node_name} in {zone}. This method returns only the most recent 1 MB of output per port.

Consult Troubleshoot Linux VM boot issues due to kernel panic to identify documented kernel panic and hardware crash patterns (such as Fatal Machine check, hung_task: blocked tasks, or NMI: Not continuing) in the serial port output.


Step 3: Check node auto-repair status [Low Risk]

Find out why GKE hasn't repaired the unresponsive node:


Step 4: Check Compute Engine system events [Low Risk]

To list the system events for {node_name} around {issue_time}, follow the instructions in the section Querying Cloud Audit Logs. Compare the method field with the table in the "Reviewing Cloud Audit Logs" section of the same document, and look for:

  • compute.instances.hostError: a hardware or software issue on the physical host caused the VM to crash.
  • compute.instances.preempted: Compute Engine preempted a Spot VM or preemptible VM. For preempted nodes, also see Confirm node preemption.
  • compute.instances.automaticRestart: Compute Engine restarted the VM after a hostError or terminateOnHostMaintenance event.
  • compute.instances.guestTerminate: the VM's operating system initiated the shutdown.

Step 5: Resolution [High Risk]

Guardrails:

  • Never force-delete stuck Terminating pods on an unresponsive node. Force deletion doesn't wait for the kubelet to confirm that the pod has stopped, so a replacement pod can start while the old one is still running. See Force Delete StatefulSet Pods.
  • Never delete GKE-managed Compute Engine VM instances directly (gcloud compute instances delete). Instead, check how long the node has reported NodeStatusUnknown (Step 1), check the serial console output (Step 2), and rely on node auto-repair, as described in the following steps.
  1. Enable node auto-repair (Standard clusters only):
  2. Let GKE repair the node:
    • Explain that GKE repairs a node that reports NotReady or no status for the documented time threshold by draining and re-creating it, and link the "Repair criteria" and "Node repair process" sections of Auto-repair nodes. GKE waits one hour for the drain to complete. If the drain doesn't complete, GKE shuts the node down and creates a new node. Tell the user to expect this one-hour drain wait before GKE re-creates the node.
    • For multi-host TPU slice node pools, note per Node auto repair in TPU slice nodes that the entire node pool is re-created.
  3. Verify node recovery:
    • After the repair completes, follow the instructions in the section Verify that the node has recovered to verify that the repaired node returns to Ready status, then confirm that the JobSet pods are running again.

Frequently asked questions

What does the Gke Ai Troubleshooting Node Unresponsive Timeout AI skill do?

Diagnose and mitigate GKE TPU or GPU nodes stuck in NotReady / NodeStatusUnknown ("Kubelet stopped posting node status") due to host kernel panics, hardware lockups, or disabled node auto-repair. Use when nodes stop heartbeating beyond the node auto-repair threshold and pods remain stuck in Terminating. Don't use for healthy nodes, pod-only application crashes, or routine GKE upgrades.

Why use Gke Ai Troubleshooting Node Unresponsive Timeout on TypingMind?

Because you install it once and use it with any model. Gke Ai Troubleshooting Node Unresponsive Timeout 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 Ai Troubleshooting Node Unresponsive Timeout in TypingMind?

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

Which AI models can use Gke Ai Troubleshooting Node Unresponsive Timeout?

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 Ai Troubleshooting Node Unresponsive Timeout?

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

Is the Gke Ai Troubleshooting Node Unresponsive Timeout 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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