Skill UI

Automate the entire deployment lifecycle for ML inference services on CoreWeave GPU cloud. This workflow integrates unit testing, container image building (to GHCR), and rolling deployment to CoreWeave Kubernetes clusters via GitHub Actions. Ideal for ensuring stable, high-performance deployment of GPU-dependent models. Key features include validating GPU manifest requests and managing deployments using standard Kubernetes APIs.

A comprehensive guide to diagnose and resolve common CoreWeave infrastructure errors. It covers issues related to GPU scheduling, Pod status (e.g., Pending), CUDA Out of Memory errors, NCCL timeouts, and Kubernetes resource allocation within AI/ML deployment pipelines.

A comprehensive guide and set of tools for establishing a local development workflow tailored for CoreWeave GPU deployments. This includes steps for building Docker containers, validating Kubernetes YAML manifests using dry-run, and managing the full deployment lifecycle for AI inference services before pushing to the cloud.

A comprehensive utility designed for managing CoreWeave cloud infrastructure migrations. It facilitates upgrading between GPU generations (e.g., A100 to H100), updating CUDA versions, and handling Kubernetes resource changes. The tool uses K8s APIs to audit deployments, detect deprecated instances, and execute controlled rollbacks.

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 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 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.

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.

A comprehensive guide and implementation for setting up Continuous Integration and Continuous Deployment (CI/CD) pipelines for dApps powered by Alchemy. It utilizes GitHub Actions to automate critical Web3 workflows, including running Hardhat tests against a mainnet fork, verifying smart contract integrity, and securely deploying to testnets like Sepolia. Features built-in key exposure scanning for robust security.