Add 10 in-depth SEO-focused DevOps, security, and AI infra skills

infrastructure/local-ai/gpu-kubernetes-operations/SKILL.md

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+---
+name: gpu-kubernetes-operations
+description: Operate GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.
+license: MIT
+metadata:
+  author: devops-skills
+  version: "1.0"
+---
+
+# GPU Kubernetes Operations
+
+Run resilient and cost-efficient GPU clusters for production AI workloads.
+
+## Key Capabilities
+
+- NVIDIA device plugin and GPU operator lifecycle
+- MIG partitioning for multi-workload efficiency
+- GPU-aware autoscaling (KEDA/cluster autoscaler)
+- Node health checks and proactive remediation
+
+## Cluster Baseline
+
+- Dedicated GPU node pools with taints and tolerations
+- Runtime class and driver/toolkit compatibility checks
+- Local SSD or high-throughput network storage for model weights
+- DCGM metrics exported to Prometheus
+
+## Scheduling Patterns
+
+- Use node affinity by GPU type (A10/L4/A100/H100).
+- Separate latency-critical inference from batch training.
+- Pin model replicas with anti-affinity for availability.
+- Reserve headroom for failover and rolling updates.
+
+## Autoscaling Strategy
+
+- Scale on queue depth + GPU utilization, not CPU alone.
+- Warm spare replicas for large model cold-start mitigation.
+- Cap burst scaling to avoid quota exhaustion.
+
+## Reliability Checks
+
+- ECC error and Xid monitoring
+- GPU memory pressure alerts
+- Driver mismatch detection during upgrades
+- Pod preemption impact analysis
+
+## Cost Optimization
+
+- Prefer MIG slices for smaller inference services.
+- Schedule batch jobs in off-peak windows.
+- Route low-priority traffic to cheaper model tiers.
+
+## Related Skills
+
+- [llm-inference-scaling](../llm-inference-scaling/) - Autoscale inference workloads
+- [model-serving-kubernetes](../../../devops/orchestration/model-serving-kubernetes/) - Production model serving patterns
+- [gpu-server-management](../../servers/gpu-server-management/) - Host-level GPU management fundamentals