Skill UI

This skill provides comprehensive guides for deploying stateless, serverless API wrappers powered by Claude (Anthropic). It details how to integrate and stream responses from the Messages API across major cloud environments, including Vercel Edge Functions, Fly.io, and Google Cloud Run. It is essential for productionizing AI applications that require robust secret management, scalable infrastructure, and real-time streaming capabilities.

A comprehensive guide and code pattern for configuring ClickHouse instances across development, staging, and production environments. It emphasizes best practices for infrastructure-as-code (IaC), secure secrets management (integrating AWS Secrets Manager, Vault, etc.), and ensuring environment-specific settings are correctly applied for reliable data analytics pipelines.

This bundle is designed to collect comprehensive diagnostics for CoreWeave clusters, including GPU node status, Kubernetes Pod logs, event history, and API connectivity. It aggregates critical cluster-level resource allocation and failure data into a single, actionable archive. Use it when preparing a support ticket to expedite the diagnosis of complex infrastructure issues like stuck pods, OOM errors, or resource bottlenecks.

This guide provides step-by-step instructions to configure access to the CoreWeave Kubernetes Service (CKS). It covers downloading the kubeconfig file, setting API tokens, and verifying GPU node access using standard `kubectl` commands. This is essential for deploying AI/ML workloads on bare-metal GPU infrastructure.

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 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.

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.

A universal infrastructure for automatically evolving and optimizing AI agents across any domain. Using LLM-driven mutation and iterative solve-observe-evolve cycles, it enhances critical components like prompts, skills, and memory by benchmarking performance against measurable datasets, enabling the creation of self-improving AI systems.

This skill guides users through setting up robust, production-grade infrastructure on Fly.io. It covers creating and connecting to Fly Postgres clusters for reliable data storage, provisioning persistent NVMe Volumes for durable file storage, and configuring private internal networking (6PN) for secure service-to-service communication. It is essential for modern microservices requiring stable databases and persistent state.

This comprehensive bundle collects all necessary diagnostic evidence for Fly.io applications, including machine status, application logs, volume metadata, and network diagnostics. It is crucial for generating detailed support tickets and troubleshooting complex infrastructure issues such as stuck deployments, boot failures, or edge networking problems.

This guide demonstrates how to deploy a minimal "Hello World" application to the Fly.io platform. It covers two primary deployment methods: using the simple `flyctl` Command Line Interface (CLI) for quick setup, and utilizing the advanced Machines API for programmatic deployment. This skill is ideal for beginners starting a new Fly.io project, or for implementing robust, globally distributed edge compute infrastructure.

This comprehensive guide provides step-by-step instructions for migrating and upgrading services on the Fly.io platform. It details critical processes such as migrating applications from Apps v1 (Nomad) to v2 (Machines), upgrading the flyctl CLI, performing major Postgres database version upgrades, and executing region migrations. Essential for maintaining and modernizing cloud infrastructure.