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-inferencegit 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-inference)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-inference"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-inference/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-inference"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-inference.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 | $0.00074 | $0.01813 |
| Opus 5 | $0.00037 | $0.00907 |
| Sonnet 5 | $0.00015 | $0.00363 |
| Haiku 4.5 | $0.00007 | $0.00181 |
Grade A, and why
gke-inference 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-inference — 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GKE AI/ML Inference
This reference covers deploying AI/ML inference workloads on GKE using Google's Inference Quickstart (GIQ) and best practices for LLM serving.
MCP Tools:
apply_k8s_manifest,get_k8s_resource,get_k8s_logs,get_k8s_rollout_status,describe_k8s_resource,list_k8s_events. CLI-only:gcloud container ai profiles *
When to Use
- Deploy an AI model (Llama, Gemma, Mistral, etc.) to GKE
- Generate optimized Kubernetes manifests for inference
- Select GPU/TPU accelerators for model serving
- Configure autoscaling for LLM inference
Prerequisites
- A golden path GKE Autopilot cluster (GPU workloads are supported via ComputeClasses and NAP)
gcloudCLI authenticated- Sufficient GPU/TPU quota in the target region
Workflow
1. Discovery: Find Models and Hardware
# List all supported models
gcloud container ai profiles models list --quiet
# Find valid accelerator/server combinations for a model
gcloud container ai profiles list --model=<MODEL_NAME> --quiet
# Example: what can run Gemma 2 9B?
gcloud container ai profiles list --model=gemma-2-9b-it --quiet
2. Generate Manifest
gcloud container ai profiles manifests create \
--model=<MODEL_NAME> \
--model-server=<SERVER> \
--accelerator-type=<ACCELERATOR> \
--target-ntpot-milliseconds=<NTPOT> --quiet > inference.yaml
Parameters:
--model: Model ID (e.g.,gemma-2-9b-it,llama-3-8b)--model-server: Inference server (vllm,tgi,triton,tensorrt-llm)--accelerator-type: GPU/TPU type (nvidia-l4,nvidia-tesla-a100,nvidia-h100-80gb)--target-ntpot-milliseconds: Target Normalized Time Per Output Token (optional, for latency optimization)
Example:
gcloud container ai profiles manifests create \
--model=gemma-2-9b-it \
--model-server=vllm \
--accelerator-type=nvidia-l4 \
--target-ntpot-milliseconds=50 --quiet > inference.yaml
3. Review and Deploy
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 · 209 lines · 74 tokens per session scan A 050cda071d12
gke-inference is a skill published in the GitHub repository gke-labs/kube-agents (53 stars, last pushed today), licensed Apache-2.0. It adds 74 tokens to every session and 1,813 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to gke-inference, differing in 0 lines, and is treated as a copy.
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gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
agent-platform-endpoint-management
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modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.