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-tpu-metrics-monitoringgit 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-tpu-metrics-monitoring)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-tpu-metrics-monitoring"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-tpu-metrics-monitoring/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-tpu-metrics-monitoring"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-tpu-metrics-monitoring.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 | $0.00095 | $0.01813 |
| Opus 5 | $0.00048 | $0.00907 |
| Sonnet 5 | $0.00019 | $0.00363 |
| Haiku 4.5 | $0.00010 | $0.00181 |
Grade A, and why
gke-tpu-metrics-monitoring 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 9d 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
92% identical to gke-ai-troubleshooting-tpu-metrics-monitoring — 14 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GKE TPU Metrics Monitoring Guide
This skill enables the agent to monitor GKE TPU workloads, nodes, and node pools using GKE system metrics. It helps diagnose if workload interruptions or performance issues are caused by underlying infrastructure.
Step 0: Mandatory Context
Independently gather required context (such as cluster details or node pool names) using available GKE and Cloud tools, or use the provided {variable} placeholders:
{project_id}: The GCP Project ID.{cluster_name}: The GKE Cluster Name.{location}: The GKE Cluster Location (region or zone).{node_name}: (Optional) The name of the specific GKE node.{node_pool_name}: (Optional) The name of the GKE node pool.
Diagnostic Steps
Step 1: Verify TPU Runtime Metrics Configuration [Low Risk] [Auto]
Before analyzing runtime metrics, verify that the workload is configured to export them.
- Action: Verify that the Pod specification for the TPU workload includes:
containerPort: 8431- JAX version
0.4.14or later (if using JAX). - GKE version is
1.27.4-gke.900or later. - GKE System Metrics are enabled on the cluster.
Step 2: Monitor TPU Runtime Metrics [Low Risk] [Auto]
If configured correctly, the following metrics are available in Cloud Monitoring (monitored resources k8s_node and k8s_container):
- Container Metrics:
kubernetes.io/container/accelerator/duty_cycle: Percentage of time over the past sampling period (60 seconds) during which the TensorCores were actively processing on a TPU chip.kubernetes.io/container/accelerator/memory_used: Amount of accelerator memory allocated in bytes.kubernetes.io/container/accelerator/memory_total: Total accelerator memory in bytes.
- Node Metrics:
kubernetes.io/node/accelerator/duty_cyclekubernetes.io/node/accelerator/memory_usedkubernetes.io/node/accelerator/memory_total
Step 3: Check Node Status Condition [Low Risk] [Auto]
Query the status condition of GKE nodes (GKE version 1.32.1-gke.1357001 or later).
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.
- 9d ago First seen · 142 lines · 95 tokens per session scan A e073840ef083
gke-tpu-metrics-monitoring is a skill published in the GitHub repository gke-labs/kube-agents (54 stars, last pushed today), licensed Apache-2.0. It adds 95 tokens to every session and 1,813 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to gke-ai-troubleshooting-tpu-metrics-monitoring, differing in 14 lines, and is treated as a copy.
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