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.
npx skills add gke-labs/kube-agents --skill gke-ai-troubleshooting-tpu-vbar-oomgit clone --depth 1 https://github.com/gke-labs/kube-agentsWrote 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.
[](https://agentmods.dev/skills/gke-labs/kube-agents/gke-ai-troubleshooting-tpu-vbar-oom)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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.
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
gcloudor 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
- 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
- Let
- 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}"
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.
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.
- 12d ago First seen · 157 lines · 125 tokens per session scan A 92762cce7dd9
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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