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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

Install / Use

npx skills add google/skills --skill gke-ai-troubleshooting-node-unresponsive-timeout

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

97/100

Supported Platforms

Universal

Tags

Our assessment of gke-ai-troubleshooting-node-unresponsive-timeout

gke-ai-troubleshooting-node-unresponsive-timeout scores 97/100 on our quality scale, 161st of 4,570 Development & Engineering skills we index (top 4%).

Its SKILL.md is 12 KB long, well organised into 9 sections with 2 code examples: a thorough specification that gives an agent plenty to work with.

With 20,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
30/30
Structure
18/20
Description
15/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 14 days ago, so gke-ai-troubleshooting-node-unresponsive-timeout is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

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All 4 of these similar skills score higher than gke-ai-troubleshooting-node-unresponsive-timeout; compare them before choosing.

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Frequently asked questions

How do I install gke-ai-troubleshooting-node-unresponsive-timeout?
Run npx skills add google/skills --skill gke-ai-troubleshooting-node-unresponsive-timeout. The install tabs above show the steps for each supported agent.
Which AI agents does gke-ai-troubleshooting-node-unresponsive-timeout work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is gke-ai-troubleshooting-node-unresponsive-timeout safe to use?
It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is gke-ai-troubleshooting-node-unresponsive-timeout still maintained?
The repository was last updated 14 days ago, so gke-ai-troubleshooting-node-unresponsive-timeout is actively maintained.

name: gke-ai-troubleshooting-node-unresponsive-timeout description: >- 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. metadata: version: "1.0.0" category: Containers

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":
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

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars20.3k
CategoryDevelopment
Updated14d ago
Forks1.7k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions