Skill UI

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.

This skill provides comprehensive guides and best practices for working with Neon Serverless Postgres. It covers initial setup, various connection methods (TCP, WebSockets), and advanced features like branching, autoscaling, and connection pooling. It offers detailed integration advice for Python, TypeScript, and other languages, making it essential for building scalable, modern database applications.

Neon is a robust serverless Postgres platform that separates compute and storage, offering advanced features like autoscaling, branching, and instant restore. This guide provides comprehensive resources on setting up, connecting to, and utilizing Neon databases. It is fully compatible with standard Postgres and integrates seamlessly with various languages, ORMs, and developer tools for robust data management.

Automate the deployment of CAST AI across various multi-cloud Kubernetes clusters, including EKS, GKE, and AKS. This module uses Terraform Infrastructure as Code (IaC) patterns to reliably configure necessary components like IAM roles, node autoscaling policies, and cluster resources, ensuring standardized onboarding across different cloud environments.

A comprehensive diagnostic guide for common issues encountered with CAST AI agents, Kubernetes autoscaling, API connectivity, and resource provisioning. Covers scenarios like agent crashes, 401 errors, and scaling failures, providing specific commands (kubectl, curl, helm) and detailed fixes for robust troubleshooting.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

A comprehensive guide providing production-grade YAML patterns and debugging commands for Kubernetes deployments. Covers best practices for Deployments, Services, Ingress, resource management (requests/limits), RBAC, probes, autoscaling (HPA), and detailed kubectl debugging cheatsheets.