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-app-onboardinggit 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-app-onboarding)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-app-onboarding"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-app-onboarding/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-app-onboarding"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-app-onboarding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00070 | $0.01213 |
| Opus 5 | $0.00035 | $0.00607 |
| Sonnet 5 | $0.00014 | $0.00243 |
| Haiku 4.5 | $0.00007 | $0.00121 |
Grade A, and why
gke-app-onboarding 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.
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GKE App Onboarding
This reference provides workflows for containerizing and deploying applications to GKE for the first time.
MCP Tools:
apply_k8s_manifest,get_k8s_resource,get_k8s_rollout_status,get_k8s_logs,describe_k8s_resource
Workflow
1. App Assessment
Before containerizing, assess the application:
- Language & Framework: Identify the tech stack
- Dependencies: List required libraries and external services
- Configuration: How is the app configured? (env vars, config files, secrets)
- Statefulness: Does it need persistent storage? (databases, file storage)
- Networking: Port mapping and protocol (HTTP, gRPC, TCP)
- Health endpoints: Does the app expose health check endpoints?
2. Containerization
Create a container image:
Dockerfile (recommended for most apps):
# Multi-stage build for smaller, more secure images
FROM golang:1.22 AS builder
WORKDIR /app
COPY . .
RUN CGO_ENABLED=0 go build -o server .
FROM gcr.io/distroless/static:nonroot
COPY --from=builder /app/server /server
USER nonroot:nonroot
EXPOSE 8080
ENTRYPOINT ["/server"]
Best practices:
- Use multi-stage builds to keep production images small
- Use distroless or minimal base images to reduce attack surface
- Run as non-root user
- Log to
stdoutandstderrfor Cloud Logging collection
For applications where writing a Dockerfile is not preferred, you can use Cloud Native Buildpacks to automatically detect the language and build a container image:
pack build <image> --builder gcr.io/buildpacks/builder:latest
3. Image Management
Build and store the container image:
# Configure Docker for Artifact Registry
gcloud auth configure-docker <REGION>-docker.pkg.dev --quiet
# Build and push
docker build -t <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG> .
docker push <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG>
What ships with it
4 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 · 186 lines · 70 tokens per session scan A d9133859907d
gke-app-onboarding is a skill published in the GitHub repository gke-labs/kube-agents (54 stars, last pushed today), licensed Apache-2.0. It adds 70 tokens to every session and 1,213 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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