Loading...
Loading...
Found 19 Skills
Knative serverless platform for Kubernetes. Use when deploying serverless workloads, configuring autoscaling (scale-to-zero), event-driven architectures, traffic management (blue-green, canary), CloudEvents routing, Brokers/Triggers/Sources, or working with Knative Serving/Eventing/Functions. Covers installation, networking (Kourier/Istio/Contour), and troubleshooting.
Discover optimal autoscaling parameters for a Deco site by analyzing Prometheus metrics. Correlates CPU, concurrency, and latency to find the right scaling target and method.
Use when launching cloud VMs, Kubernetes pods, or Slurm jobs for GPU/TPU/CPU workloads, training or fine-tuning models on cloud GPUs, deploying inference servers (vllm, TGI, etc.) with autoscaling, writing or debugging SkyPilot task YAML files, using spot/preemptible instances for cost savings, comparing GPU prices across clouds, managing compute across 25+ clouds, Kubernetes, Slurm, and on-prem clusters with failover between them, troubleshooting resource availability or SkyPilot errors, or optimizing cost and GPU availability.
Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).
Use when operating production Kubernetes — Helm, autoscaling (HPA/VPA), resource management, StatefulSets, external-secrets, observability (Prometheus/Grafana/Loki), RBAC, Pod Security Standards, NetworkPolicies, admission control, backup (Velero), and cost control.
Operate GPU-backed Kubernetes clusters for AI inference and training with scheduling, autoscaling, node health, MIG partitioning, and cost controls.
Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.