Skill UI

This workflow provides an end-to-end programmatic solution for managing the Canva design lifecycle. It allows users to create new designs via the API, redirect users to the editor for modifications, retrieve design metadata, and finally, export finished assets in various high-quality formats (PDF, PNG, JPG, etc.). Ideal for integrating design capabilities into external applications like CMS or marketing automation tools.

A collection of production-ready patterns for wrapping the Canva Connect REST API, supporting both TypeScript and Python implementations. These patterns ensure robust API integration by handling automatic OAuth token refresh, implementing retry logic with exponential backoff, ensuring type safety, and supporting multi-tenant architectures. Use this when building resilient, reusable API wrappers for Canva services.

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 skill guides the setup and configuration of the CAST AI Workload Autoscaler, enabling dynamic right-sizing of pod resource requests (CPU and Memory). It automatically adjusts resources based on actual usage, significantly reducing cloud over-provisioning costs. Use this guide to implement workload scaling policies, configure resource limits via Kubernetes annotations, and monitor scaling events in Kubernetes environments.

This script generates a comprehensive diagnostic bundle for CAST AI deployed on a Kubernetes cluster. It automatically collects crucial data including component logs, pod statuses, Helm releases, node inventories, and cluster events into a single archive. Use this tool when preparing a detailed support ticket or performing deep technical troubleshooting.

This skill provides comprehensive API calls using `curl` to interact with the CAST AI platform. It allows users to list connected Kubernetes clusters, retrieve detailed cost savings reports, and inspect the inventory of managed nodes. It is essential for verifying connectivity, auditing cloud spending, and monitoring resource allocation after onboarding.

A comprehensive guide to connecting any Kubernetes cluster (EKS, GKE, AKS) to CAST AI for cost optimization, autoscaling, and security monitoring. This skill covers the entire setup process, including generating API keys, installing the CAST AI agent via Helm charts, and integrating the provider using Terraform.

This guide sets up a robust local development loop for integrated Kubernetes infrastructure using CAST AI. It allows developers to test autoscaler policies, validate Terraform modules, and measure potential cloud cost savings in a dev environment before deploying to production. This is essential for building cost-aware and efficient CI/CD pipelines.

A comprehensive reference architecture guide for deploying and managing CAST AI across multiple Kubernetes clusters (Dev, Staging, Prod). It details best practices for infrastructure provisioning using Terraform, focusing heavily on cost optimization techniques such as autoscaling, spot instance management, and environment-specific resource policies. Ideal for setting up team standards and ensuring reliable, cost-effective deployments.

A set of production-ready wrappers and patterns for interacting with the CAST AI REST API. This package provides robust client implementations in TypeScript and Python, handling critical features such as typed responses, singleton management, automatic retry logic with backoff, and managing cloud resources (e.g., Kubernetes clusters, node optimization). Use it when building reusable, reliable clients for cost optimization and infrastructure management.

A comprehensive guide for diagnosing and fixing common errors encountered when integrating with the Anthropic API (Claude). It covers critical issues such as authentication failures (401), rate limiting (429), server overload (529), invalid requests, and context window limits. Includes practical code solutions and best practices for robust API integration.

This guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.