Skill UI

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.

Comprehensive patterns and best practices for deploying and managing microservices using the Linkerd service mesh on Kubernetes. Covers automatic mTLS, sophisticated traffic splitting for canary deployments, setting Service Profiles for enhanced resilience (retries/timeouts), and defining robust authorization policies.

A comprehensive guide for deploying complex integrations to production cloud environments (GCP, Kubernetes). This skill covers best practices including creating multi-stage Dockerfiles, implementing robust health and readiness checks, and utilizing cloud tools like gcloud and kubectl for seamless, secure deployment with secret management.

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.

A comprehensive skill set for building, deploying, and managing production-grade Machine Learning systems. Expertise spans modern frameworks (PyTorch, TensorFlow), model serving using Kubernetes, advanced feature engineering (Feast, Tecton), and robust MLOps practices (monitoring, A/B testing, CI/CD). Focuses on delivering scalable, reliable, and business-value-driven AI solutions.

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.

A comprehensive guide covering the complete setup of Prometheus, including metric collection strategies, advanced scrape configuration (static, file-based, Kubernetes), defining recording rules, and integrating with AlertManager and Grafana. Use this skill to establish robust monitoring for applications and infrastructure.

This comprehensive toolkit provides end-to-end DevOps automation capabilities. It scaffolds CI/CD pipelines (GitHub Actions, CircleCI), generates robust Infrastructure as Code (IaC) modules using Terraform for major clouds (AWS, GCP, Azure), and manages complex Kubernetes deployments (Blue/Green, Rolling). Ideal for automating infrastructure setup, application deployment, and drift management in multi-cloud environments.