Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsnpx agentmods add skills/h4vzz/awesome-ai-agent-skills/kubernetes-deploymentWrote 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/h4vzz/awesome-ai-agent-skills/kubernetes-deployment)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/kubernetes-deployment"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/kubernetes-deployment/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/h4vzz/awesome-ai-agent-skills/kubernetes-deployment"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/kubernetes-deployment.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 | $0.00026 | $0.02656 |
| Opus 5 | $0.00013 | $0.01328 |
| Sonnet 5 | $0.00005 | $0.00531 |
| Haiku 4.5 | $0.00003 | $0.00266 |
Grade A, and why
kubernetes-deployment 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
94% identical to kubernetes-deployment — 2 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 — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kubernetes Deployment
This skill enables the agent to deploy and manage applications on Kubernetes clusters. The agent can generate deployment manifests, services, ingress rules, Helm charts, and autoscaling configurations. It handles the full lifecycle from initial deployment through scaling, rolling updates, and troubleshooting, following production best practices for resource management, security, and reliability.
Workflow
-
Configure Cluster Access: The agent verifies that
kubectlis configured with the correct cluster context and namespace. It checks connectivity withkubectl cluster-infoand confirms that the user has sufficient RBAC permissions to create and manage resources in the target namespace. If a kubeconfig is not present, the agent guides the user through authentication (e.g.,aws eks update-kubeconfig,gcloud container clusters get-credentials). -
Define Deployment Manifests: The agent creates Kubernetes deployment manifests specifying the container image, replica count, resource requests and limits, environment variables, liveness and readiness probes, and pod anti-affinity rules. Labels and annotations are applied consistently for service discovery, monitoring, and operations. The agent uses specific image tags (never
latest) and setsimagePullPolicyappropriately. -
Configure Services and Ingress: The agent creates Service resources to expose deployments within the cluster (ClusterIP) or externally (LoadBalancer, NodePort). For HTTP workloads, the agent configures Ingress resources with TLS termination using cert-manager, path-based routing, and rate limiting annotations. The agent selects the appropriate service type based on the deployment environment and traffic requirements.
-
Apply Manifests and Verify Rollout: The agent applies manifests using
kubectl apply -fand monitors the rollout withkubectl rollout status. It verifies that all pods reach the Running state, health checks pass, and the service endpoints are registered. If a rollout stalls, the agent checks pod events withkubectl describe podand logs withkubectl logsto diagnose the issue, and can executekubectl rollout undoto revert to the previous version.
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 · 271 lines · 26 tokens per session scan A bd4b9ecfc8c5
kubernetes-deployment is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 2,656 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to kubernetes-deployment, differing in 2 lines, and is treated as a copy.
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helm
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