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-handle-disruption-gpu-tpugit 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-handle-disruption-gpu-tpu)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-ai-troubleshooting-handle-disruption-gpu-tpu"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-ai-troubleshooting-handle-disruption-gpu-tpu/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-handle-disruption-gpu-tpu"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-ai-troubleshooting-handle-disruption-gpu-tpu.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 | $0.00117 | $0.01437 |
| Opus 5 | $0.00059 | $0.00718 |
| Sonnet 5 | $0.00023 | $0.00287 |
| Haiku 4.5 | $0.00012 | $0.00144 |
Grade A, and why
gke-ai-troubleshooting-handle-disruption-gpu-tpu 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-handle-disruption-gpu-tpu — 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Handle Disruption on GPUs and TPUs Troubleshooting
🔍 Diagnostic Workflow
Step 0: Context Acquisition
- Mandatory: When a user asks to debug or investigate an actual workload
disruption, node crash, or unexpected restart without providing complete
cluster details, you MUST immediately halt and request all missing mandatory
parameters (
project_id,location,cluster_name,timestamp) BEFORE delivering theories or general diagnostic commands. Only skip context acquisition if the user explicitly requests a generic reusable runbook or provides a complete static telemetry/log dump for offline analysis. - Optional:
node_name,workload_name,workload_namespace,nodepool_name.
Step 1: [Low Risk] Check for Upcoming Scheduled Maintenance
-
Action: Propose running
kubectlto check if nodes have the scheduled maintenance label indicating an upcoming disruption. -
Example Command:
kubectl get nodes -l cloud.google.com/scheduled-maintenance-time -L cloud.google.com/scheduled-maintenance-time -
Interpretation: The
SCHEDULED-MAINTENANCE-TIMEcolumn shows the Unix epoch time when the VM is scheduled for maintenance. If this label exists, a disruption is guaranteed to occur.
Step 2: [Low Risk] Investigation via Cloud Monitoring (PromQL)
-
Action: Call any available monitoring tool or provide PromQL for manual verification.
-
Mandatory Monitoring Rule: Whenever recommending follow-up monitoring or interruption tracking over time, you MUST explicitly present a PromQL query using the metric
kubernetes_io:node_interruption_countfiltered byinterruption_reason="HW/SW Maintenance". Do not suggest general Cloud Monitoring dashboards or Metrics Explorer without providing this specific PromQL metric expression. -
Example Query:
# Fetch host maintenance events for nodes sum by (interruption_type,interruption_reason)( sum_over_time( kubernetes_io:node_interruption_count{monitored_resource="k8s_node", interruption_reason="HW/SW Maintenance"}[${__interval}]))
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 · 111 lines · 117 tokens per session scan A 706f73fa33d1
gke-ai-troubleshooting-handle-disruption-gpu-tpu is a skill published in the GitHub repository gke-labs/kube-agents (54 stars, last pushed today), licensed Apache-2.0. It adds 117 tokens to every session and 1,437 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-handle-disruption-gpu-tpu, differing in 0 lines, and is treated as a copy.
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