Skill UI

This specialized, end-to-end workflow guides the deployment of applications onto Kubernetes clusters. It systematically covers all critical production phases, including Docker container preparation, generating K8s manifests, structuring applications with Helm charts, configuring advanced service meshes (like Istio), enforcing robust security policies (RBAC, NetworkPolicy), and implementing full observability and GitOps pipelines for reliable, scalable deployments.

Auto-activating DevOps skill that guides Kubernetes deployment creation with best practices, production-ready IaC configurations, and validation across Terraform, Helm, and monitoring workflows.

Automated guidance for Kubernetes ingress configuration within advanced DevOps workflows, covering best practices, production-ready setups, and validation for infrastructure-as-code delivery.

Consolidates Kubernetes deployment best practices across manifests, Helm charts, autoscaling, and troubleshooting to keep API workloads resilient, efficient, and observable. Ideal for SREs or platform teams standardizing resource limits, probes, topology spread, and diagnostics.

Provides automated guidance for Kubernetes RBAC analyzer tasks within Security Advanced workflows, covering best practices, production-ready configurations, compliance checks, and threat modeling steps whenever the analyzer is mentioned.

Automates guidance for managing Kubernetes secrets, covering best practices, Terraform/Helm patterns, and IaC validation ideal for advanced DevOps scenarios.

Automated guidance for Kubernetes service management in the DevOps Advanced domain, offering best-practice advice, code snippets, and config checks for services, Terraform, Helm, and monitoring workflows.

Guides Kubernetes operators through workload design, manifest generation, security hardening, networking, storage, GitOps, and multi-cluster operations while ensuring probes, resource limits, and least-privilege policies.

Helps Claude manage Kubernetes network policies and firewall rules by generating or updating YAML configurations based on infrastructure needs, ensuring secure pod-to-pod communication and compliance with least-privilege principles.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

Automates nginx ingress manager tasks within the DevOps Advanced domain, guiding Kubernetes, Terraform, Helm, and monitoring setups with production-ready configurations and best practices.

Generates Prometheus configuration files on demand, guiding DevOps engineers through best practices for Kubernetes, Terraform, CI/CD, and monitoring while validating outputs against common standards.