google/skills is a collection of Agent Skills that guide AI agents in using Google products and technologies, including Google Cloud services. Developers and agents use these reusable workflows for tasks such as cloud authentication, data analysis, application deployment, and AI platform management. The catalogue entries are skills, plugins, and an MCP integration from this collection.
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 google/skills --skill gke-inferencegit clone --depth 1 https://github.com/google/skillsWrote 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/google/skills/gke-inference)<a href="https://agentmods.dev/skills/google/skills/gke-inference"><img src="https://agentmods.dev/badge/skills/google/skills/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/google/skills/gke-inference"><img src="https://agentmods.dev/badge/skills/google/skills/gke-inference.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00119 | $0.02056 |
| Opus 5.5 | $0.00048 | $0.00822 |
| Sonnet 5.5 | $0.00024 | $0.00411 |
| Haiku 4.5 | $0.00012 | $0.00206 |
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 today.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gke-inference — 100% identical, 39 lines differ
How it starts
The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GKE AI/ML Inference
Routing Note: For migrating existing AI workloads to GKE, open
google-cloud-solution-guided-gke-ai-migration/SKILL.md. For GKE RAG with Cloud SQL or AlloyDB (pgvector), opengoogle-cloud-solution-rag-enterprise-search-gke-sqldb/SKILL.md.
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 *
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-inference)"
and append agent-skills/1.0 (skill:gke-inference) to User-Agent headers.
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
What ships with it
1 file 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.
- today Changed · +9 lines 2153b93588c8
- 20d ago Changed · +4 lines · +45 tokens per session 55d6a6b0ce73
- 28d ago Changed · +1 lines feddea7055e1
- 1mo ago First seen · 209 lines · 74 tokens per session scan A 050cda071d12
gke-inference is a skill published in the GitHub repository google/skills (21,425 stars, last pushed today), licensed Apache-2.0. It adds 119 tokens to every session and 2,056 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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