Skill UI

A comprehensive checklist and script to validate the production readiness of decentralized applications (dApps) powered by Alchemy. It covers critical security best practices, API infrastructure validation, smart contract deployment checks, and performance optimization, ensuring a stable and secure mainnet launch.

This skill guides the secure deployment of Claude API integrations into production cloud environments. It provides comprehensive instructions for implementing services across multiple infrastructures, including Docker, GCP Cloud Run, and Kubernetes. The guide covers best practices such as secret management, health checks, and reliable rollback procedures to ensure high service reliability.

This skill provides comprehensive integration for TP-Link Tapo cameras (models like C200, C310, etc.). It supports streaming via RTSP and ONVIF protocols, allowing users to discover cameras on the network, capture motion-triggered video clips, and maintain stable live video feeds. Setup requires basic network credentials and Python environment setup.

A comprehensive guide to implementing production-grade reliability patterns for the Canva Connect API. Learn to use Circuit Breakers, graceful degradation, exponential backoff retries, and token refresh strategies to build fault-tolerant integrations that can handle network failures, rate limits, and service outages gracefully.

This workflow guides users through configuring CAST AI's autoscaler policies to achieve optimal cost management and resource utilization within a Kubernetes cluster. It covers enabling spot instances, setting node downscaler/evictor rules, defining cluster limits, and creating specialized node templates (e.g., for GPU or spot workers) using Terraform. Use this when implementing Phase 2 automation or optimizing cloud spend.

A comprehensive guide and set of scripts to maximize cost savings in Kubernetes environments using CAST AI. Learn to implement aggressive spot instance strategies, perform workload right-sizing, utilize cluster hibernation for non-production environments, and analyze monthly savings reports to significantly reduce cloud compute expenditure.

A comprehensive runbook designed to guide developers through responding to Anthropic API incidents, including outages, sustained errors (like 529), and rate limit issues. It covers systematic steps like checking the official status page, classifying incident severity, implementing model fallback logic (e.g., downgrading Opus to Sonnet), and conducting post-incident impact assessments to ensure service resilience.

Provides best practices for configuring Large Language Model (LLM) access, specifically for Anthropic Claude, across development, staging, and production environments. This guide details setting up separate API keys, selecting appropriate models (e.g., Haiku, Sonnet, Opus), and managing unique rate limits for robust, scalable, and secure LLM integration in enterprise systems.

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 skill outlines how to implement and manage Role-Based Access Control (RBAC) and namespace isolation for multi-team GPU workloads running on CoreWeave's Kubernetes platform. It provides guidance on setting strict resource quotas, preventing noisy-neighbor issues, and defining granular roles (e.g., ML Engineer, Inference Operator). This is essential for maintaining resource isolation, ensuring compliance (SOC 2, HIPAA), and managing access across large-scale cloud deployments.

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.