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 BagelHole/DevOps-Security-Agent-Skills --skill gpu-kubernetes-operationsgit clone --depth 1 https://github.com/BagelHole/DevOps-Security-Agent-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/bagelhole/devops-security-agent-skills/gpu-kubernetes-operations)<a href="https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/gpu-kubernetes-operations"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/gpu-kubernetes-operations.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 86 Code deploys a privileged Kubernetes workload (privileged container, hostPath mount, or host namespaces). This grants root on the node and is a node/cluster takeover vector.Fix: Remove privileged, hostPath, and host-namespace settings from workloads. Use a least-privilege securityContext, drop capabilities, and avoid mounting the host filesystem.
- high Tool Misuse · line 99 Code deploys a privileged Kubernetes workload (privileged container, hostPath mount, or host namespaces). This grants root on the node and is a node/cluster takeover vector.Fix: Remove privileged, hostPath, and host-namespace settings from workloads. Use a least-privilege securityContext, drop capabilities, and avoid mounting the host filesystem.
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 | $0.00033 | $0.03117 |
| Opus 5 | $0.00016 | $0.01558 |
| Sonnet 5 | $0.00007 | $0.00623 |
| Haiku 4.5 | $0.00003 | $0.00312 |
Grade A, and why
gpu-kubernetes-operations 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 4d 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 — 453 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPU Kubernetes Operations
Run resilient and cost-efficient GPU clusters for production AI workloads.
When to Use This Skill
- Setting up GPU node pools in Kubernetes for AI inference or training
- Configuring NVIDIA device plugin and GPU operator
- Implementing MIG partitioning to share GPUs across workloads
- Building GPU-aware autoscaling policies
- Monitoring GPU health with DCGM and Prometheus
- Troubleshooting GPU scheduling, driver, or OOM issues
Prerequisites
- Kubernetes 1.28+ cluster with GPU-capable nodes
- NVIDIA GPUs (A10, L4, A100, H100, or similar)
- NVIDIA drivers installed on nodes (535+ recommended)
- Helm 3 for operator and plugin installation
- Prometheus stack for metrics collection
NVIDIA GPU Operator Installation
The GPU Operator automates driver, toolkit, device plugin, and DCGM deployment.
# Add NVIDIA Helm repo
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update
# Install GPU Operator
helm install gpu-operator nvidia/gpu-operator \
--namespace gpu-operator \
--create-namespace \
--set driver.enabled=true \
--set toolkit.enabled=true \
--set devicePlugin.enabled=true \
--set dcgmExporter.enabled=true \
--set migManager.enabled=true \
--set nodeStatusExporter.enabled=true \
--version v24.3.0
# Verify installation
kubectl get pods -n gpu-operator
kubectl get nodes -o json | jq '.items[].status.allocatable["nvidia.com/gpu"]'
NVIDIA Device Plugin (Standalone)
If not using the GPU Operator, deploy the device plugin directly.
# nvidia-device-plugin.yaml
apiVersion: apps/v1
kind: DaemonSet
metadata:
name: nvidia-device-plugin
namespace: kube-system
spec:
selector:
matchLabels:
name: nvidia-device-plugin
template:
metadata:
labels:
name: nvidia-device-plugin
spec:
tolerations:
- key: nvidia.com/gpu
operator: Exists
effect: NoSchedule
priorityClassName: system-node-critical
containers:
- name: nvidia-device-plugin
image: nvcr.io/nvidia/k8s-device-plugin:v0.15.0
securityContext:
privileged: true
env:
- name: FAIL_ON_INIT_ERROR
value: "false"
- name: DEVICE_SPLIT_COUNT
value: "1"
- name: DEVICE_LIST_STRATEGY
value: "envvar"
volumeMounts:
- name: device-plugin
mountPath: /var/lib/kubelet/device-plugins
volumes:
- name: device-plugin
hostPath:
path: /var/lib/kubelet/device-plugins
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.
- 4d ago First seen · 453 lines · 33 tokens per session scan A 18fc7be68d53
gpu-kubernetes-operations is a skill published in the GitHub repository BagelHole/DevOps-Security-Agent-Skills (1,058 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 3,117 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
agentic-eks-bootstrap
Bootstrap an AWS EKS cluster optimized for Agentic AI workloads — Karpenter v1.2+ GPU node pools, EKS Auto Mode, Kubernetes 1.32+ with DRA 1.35 GA, VPC CNI, GPU Operator, and baseline observability. Use when starting a new EKS cluster that will host vLLM, Inference Gateway, Langfuse, or Kagent.
gpu-resource-management
Design GPU orchestration on EKS using Karpenter v1.2+ NodePools, KEDA scale-to-zero, and DRA 1.35 GA for multi-instance GPU (MIG) partitioning. Right-size NodePool for p5/g6e/trn2 instance mix, spot/on-demand split, consolidation, and topology-aware scheduling.
inference-gateway-routing
Configure kgateway v2.0+ as L1 and Bifrost v1.x or LiteLLM v1.60+ as L2 for a 2-Tier Inference Gateway on EKS. Apply Cascade Routing (Haiku→Sonnet→Opus fallback), Semantic Router (intent-based model pick), and HTTPRoute with OTel trace propagation to Langfuse.
vllm-serving-setup
Design, deploy, and tune vLLM v0.18.2 inference serving on EKS with PagedAttention v2, Multi-LoRA, FP8 KV Cache, Chunked Prefill, and Continuous Batching. Produces Helm values.yaml, PodMonitor, HPA, and kubectl validation steps for production agentic workloads.
aws-bedrock-ai
WORKFLOW SKILL — Amazon Bedrock and AWS AI design: foundation model selection, knowledge bases (RAG), agents for bedrock, guardrails, provisioned throughput, batch inference, fine-tuning, KMS, VPC endpoints, regional GA, and per-provider licensing.
implementing-aws-config-rules-for-compliance
Implementing AWS Config rules for continuous compliance monitoring of AWS resources, deploying managed and custom rules aligned to CIS and PCI DSS frameworks, configuring automatic remediation with SSM Automation, and aggregating compliance data across accounts.