Skill UI

A specialized workflow for deploying applications to Kubernetes, covering container orchestration, Helm charts, service mesh configuration, production-ready K8s patterns, security policies, and observability. Use for cluster deployments, chart creation, mesh setup, networking, and security hardening.

Implement Kubernetes security contexts, Pod Security Standards, network policies, and RBAC to harden clusters and workloads. Includes YAML examples for restricted pods, default-deny networking, and least-privilege access.

Deploy, scale, and manage Kubernetes workloads with YAML examples for Deployments, Services, Ingress, ConfigMaps, Secrets, HPA, storage, and kubectl debugging. Suitable for DevOps engineers and developers operating containerized applications.

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that provides advanced operational capabilities powered by an MCP server and built-in AI agent skills. It helps manage complex deployments across edge and cloud environments, offering AI-assisted troubleshooting, performance testing, and deep integration with major CNCF projects like Argo and Istio.

Kustomize enables declarative customization of Kubernetes manifests without complex templating. It supports managing configurations across environments, applying patches, and generating variants from base resources. Ideal for DevOps teams managing production and development clusters efficiently.

This skill covers production patterns for Linkerd, a lightweight security-first service mesh for Kubernetes. It includes setup instructions, namespace injection, service profiles for retries/timeouts, traffic splits for canary deployments, server authorization policies, and multi-cluster configurations. Ideal for DevOps engineers managing microservices security and traffic management.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

This skill covers configuring Grafana Loki for log aggregation and analysis within DevOps environments. It includes setting up Promtail for log shipping, deploying via Docker or Kubernetes, and writing LogQL queries for filtering. Ideal for replacing ELK stacks with a lighter-weight solution for monitoring application and system logs.

Deploy ML models on Kubernetes with KServe and NVIDIA Triton Inference Server. Covers canary deployments, autoscaling, GPU-aware scheduling, and traffic splitting for production inference workloads.

Provides a decision framework and patterns for designing applications across AWS, Azure, and GCP. Covers service comparison, migration strategies, cost optimization, and cloud-agnostic patterns like Kubernetes and Terraform. Helps architects select appropriate services and implement resilient infrastructure.

Design secure multi-tenant LLM hosting platforms on shared GPU inference infrastructure, covering tenant isolation, per-tenant quotas and rate limits, billing attribution, noisy-neighbor protection, and per-tenant policy controls for vLLM on Kubernetes.

Configure New Relic for full-stack observability: install infrastructure agents on Linux, Docker and Kubernetes, integrate APM agents for Node.js, Python, Java and Go, add custom events, metrics and spans, and write NRQL queries for dashboards and alerts.