Skill UI

This guide details setting up and managing CoreWeave GPU cloud infrastructure across Development, Staging, and Production environments. It ensures strict resource separation using dedicated Kubernetes namespaces for GPU quotas and scaling policies. Use this when deploying complex ML/AI workloads requiring robust environment isolation and controlled promotion workflows.

This skill provides comprehensive observability for CoreWeave's GPU-intensive workloads running on Kubernetes. It monitors critical operational metrics, including GPU utilization, memory usage, inference latency (p99), and job completion rates. Use it for setting up DCGM metrics dashboards, configuring proactive alerts for underutilization or Out-Of-Memory (OOM) conditions, and ensuring high reliability for AI/ML deployments.

This reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

This SDK provides production-ready Python patterns for managing GPU workloads on CoreWeave. It includes helpers for defining specific GPU affinity, setting resource limits (A100, H100), and generating complete Kubernetes Deployment YAML. Use this library for building robust inference clients, managing GPU deployments programmatically, or automating complex AI workflows.

Provides comprehensive guidelines for securing CoreWeave cloud deployments on Kubernetes. Covers critical security areas including credential management, implementing Role-Based Access Control (RBAC), enforcing network isolation policies, and handling sensitive secrets for ML model access. Essential for hardening GPU workloads and protecting data in cloud environments.

This guide details how to manage various types of sensitive credentials within KubeSphere DevOps. It covers setting up SSH keys, basic authentication pairs, API tokens, and kubeconfigs. These secrets are stored as specialized Kubernetes Secrets and synchronized with CI/CD tools like Jenkins, enabling secure connectivity for Git repositories, external services, and cluster deployments.

Provides a comprehensive, production-ready deployment solution for OneNote integrations in containerized environments. This skill addresses critical issues like MSAL token persistence (supporting both file and Redis caches), implementing deep health/readiness checks that validate actual Graph API connectivity, and managing graceful shutdowns. Ideal for containerizing OneNote services for robust, scalable production use with Docker and Kubernetes.

This skill provides a comprehensive solution for deploying containerized applications to Oracle Cloud Infrastructure (OCI). It supports two primary deployment paths: OKE (Oracle Kubernetes Engine) for full Kubernetes orchestration, and Container Instances for a simpler, serverless alternative. Use this skill for automating the entire lifecycle, including pushing images to OCIR, provisioning OKE clusters via Python SDK, and executing Kubernetes deployments using kubectl.

This guide provides comprehensive best practices for configuring Palantir Foundry integrations across development, staging, and production environments. It details how to manage environment-specific credentials, hostnames, and scope policies using structured Python code and platform-specific deployment manifests (Docker Compose, Kubernetes, gcloud). Use this skill when implementing resilient, multi-tier SaaS deployments that require strict credential and configuration separation.

A comprehensive guide detailing how to establish robust monitoring for Qdrant. Covers setting up Prometheus metrics scraping, implementing Liveness and Readiness probes for Kubernetes, configuring alerts, and centralizing logs. It provides specific instructions for both self-hosted and Hybrid Cloud environments, ensuring full operational visibility and compliance.

This advanced skill automates the entire lifecycle of cloud infrastructure deployment. Instead of manual documentation review, it researches current best practices across multi-cloud platforms (AWS, GCP, Azure), synthesizes a coherent deployment strategy, and generates production-grade Infrastructure as Code (IaC). It outputs comprehensive artifacts, including Terraform modules, Kubernetes manifests, CI/CD pipelines, monitoring setups, and cost estimates, ensuring secure and optimized deployment.

This skill provides a comprehensive guide for installing and configuring the WizTelemetry Data Pipeline using Vector. It facilitates the collection, transformation, and routing of diverse observability data—including logs, audit records, events, and notifications—into an OpenSearch backend within a KubeSphere environment. Use this when establishing centralized monitoring and robust data collection systems for Kubernetes clusters.