gke-ai-troubleshooting-tpu-vbar-oom

gke-ai-troubleshooting-tpu-vbar-oom is a skill for Claude Code, Codex from gke-labs/kube-agents. It costs 125 tokens per session (1,571 once invoked), scanned A, a copy of gke-ai-troubleshooting-tpu-vbar-oom, Apache-2.0.

A troubleshooting workflow for vbarcontrolagent crashes, out-of-memory errors, and TPU startup failures on TPU v6e nodes in Google Kubernetes Engine (GKE). It focuses on failures caused by device resets and frequent metrics polling.

In plain words
What is it for?
Use it to diagnose vbarcontrolagent segmentation faults, memory-limit failures, and TPU device initialization problems using Google Cloud logs and cluster details.
Why use it?
It organizes the investigation of a specific class of TPU node failures and defines the information and time window needed to examine logs and telemetry.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to diagnose vbarcontrolagent segmentation faults, memory-limit failures, and TPU device initialization problems using Google Cloud logs and cluster details.

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Install with agentmods
npx agentmods add skills/gke-labs/kube-agents/gke-ai-troubleshooting-tpu-vbar-oom
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add gke-labs/kube-agents --skill gke-ai-troubleshooting-tpu-vbar-oom
Clone the repo
git clone --depth 1 https://github.com/gke-labs/kube-agents

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for gke-ai-troubleshooting-tpu-vbar-oom

README.md
[![agentmods](https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-ai-troubleshooting-tpu-vbar-oom/github.svg)](https://agentmods.dev/skills/gke-labs/kube-agents/gke-ai-troubleshooting-tpu-vbar-oom)
Your own site
<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-ai-troubleshooting-tpu-vbar-oom"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-ai-troubleshooting-tpu-vbar-oom/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for gke-ai-troubleshooting-tpu-vbar-oom

Your own site · 80×15
<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-ai-troubleshooting-tpu-vbar-oom"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-ai-troubleshooting-tpu-vbar-oom.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,571 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00125 $0.01571
Opus 5 $0.00063 $0.00785
Sonnet 5 $0.00025 $0.00314
Haiku 4.5 $0.00013 $0.00157

Measured 12d ago against content hash 92762cce7dd9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

gke-ai-troubleshooting-tpu-vbar-oom scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/validate_queries.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to gke-ai-troubleshooting-tpu-vbar-oom — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

agents/platform/skills/gke-ai-troubleshooting-tpu-vbar-oom/SKILL.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TPU Connection Failure and VBAR OOM Troubleshooting

Use this skill to systematically diagnose and prevent vbar_control_agent segfaults and Out-Of-Memory (OOM) errors on TPU v6e nodes.

⚠️ Prerequisites

  • Cloud Logging must be enabled for the project.
  • Access to the project and cluster via gcloud or equivalent tool.

🔍 Diagnostic Workflow

Step 0: Context Acquisition & Time Window Definition

Independently gather required context using available GCP/GKE tools or use the provided {variable} placeholders:

  • {project_id}: The GCP Project ID (e.g., customer-ai-project-123).
  • {cluster_name}: The GKE Cluster Name (e.g., tpu-cluster-prod).
  • {node_name}: The Node Name or Instance ID (e.g., tpu-node-1).
  • {workload_name}: The Workload Name / JobSet Name (e.g., my-training-job-456).
  • {namespace}: The Workload Namespace.
  • {issue_time}: The timestamp of the issue (e.g., 2026-04-14T20:00:00Z).
Time Handling & Execution Rules
  1. Window Calculation: If an issue timestamp {issue_time} is provided, calculate the query time window as [{issue_time} - 30m] to [{issue_time} + 30m].
    • Let {start_time} = {issue_time} - 30m
    • Let {end_time} = {issue_time} + 30m
  2. Informational vs. Live Execution: If the user request is informational or query-formulation (e.g. "How can I check...", "How do I determine..."), or if live GCP project resources are not actively targetable, directly output the calculated time window, log names, and Cloud Logging filter templates without attempting live log execution commands.

Step 1: Check for vbar_control_agent OOMs

Look for specific out of memory messages from vbar_control_agent in serial console logs (serialconsole.googleapis.com%2fserial_port_1_output).

  • Tool to use: query_logs (for live diagnostics)
  • Filter Templates:

Serial Console Logs (OOMs):

logName="projects/{project_id}/logs/serialconsole.googleapis.com%2fserial_port_1_output"
AND labels."compute.googleapis.com/resource_name"="{node_name}"
AND SEARCH(text_payload, "Memory cgroup out of memory: Killed process .* (vbar_control_ag)")
AND timestamp >= "{start_time}"
AND timestamp <= "{end_time}"

Read the full file on GitHub · 157 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 157 lines · 125 tokens per session scan A 92762cce7dd9

Subscribe to this mod's changes

gke-ai-troubleshooting-tpu-vbar-oom is a skill published in the GitHub repository gke-labs/kube-agents (54 stars, last pushed today), licensed Apache-2.0. It adds 125 tokens to every session and 1,571 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gke-ai-troubleshooting-tpu-vbar-oom, differing in 0 lines, and is treated as a copy.

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