Skill UI

This guide provides a systematic approach to deep debugging complex HubSpot API issues. It includes layer-by-layer network testing (DNS, TCP, TLS), correlation ID tracking, and timing analysis to isolate intermittent or hard-to-reproduce errors. Use this when standard troubleshooting fails or when compiling detailed evidence for technical support.

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.

Comprehensive solution for conducting load testing, performance benchmarking, and auto-scaling setup for various Adobe APIs (Firefly, PDF Services, Photoshop). This guide utilizes k6 for stress testing, simulating real-world user traffic, and includes Kubernetes HPA configuration to ensure the application maintains high availability and stability under peak loads.

This guide provides a comprehensive blueprint for deploying the Abridge clinical AI integration to highly secure, HIPAA-compliant cloud infrastructure. It covers best practices for healthcare deployments, including utilizing BAA-covered services (GCP Cloud Run), robust secret management via GCP Secret Manager, and implementing VPC-restricted networking for maximum data security.

A comprehensive toolkit for debugging complex Adobe API issues that resist standard troubleshooting. Covers advanced techniques such as IMS token introspection, Firefly async job failure analysis, mapping PDF Services error codes, and deep network-layer diagnostics (DNS, TLS). Essential for resolving difficult, production-level integration bugs.