Skill UI

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.

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 guide and set of scripts for performing load testing and capacity planning for Anthropic's Claude API. This resource helps developers benchmark performance, anticipate traffic spikes, manage rate limits (RPM/ITPM/OTPM), and implement robust horizontal scaling strategies for mission-critical AI services using Python.

This guide details how to configure webhook notifications and utilize audit logs from CAST AI. It allows users to set up real-time alerts for critical cluster events like node scaling, cost threshold breaches, and security findings. By integrating with Slack, PagerDuty, and custom HTTP endpoints, users can achieve comprehensive monitoring and automated response for their 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.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

This comprehensive skill provides a framework for load testing and capacity planning of Figma API integrations. Using tools like k6, it helps benchmark API throughput, identify rate limit ceilings (e.g., leaky bucket behavior), and analyze latency under high load. It also outlines advanced scaling strategies—such as request coalescing and staggering—to ensure reliable, high-volume application integration.

This guide details the operational usage limits for Finta's core features, such as investor pipeline records, deal room creation, and AI suggestions. Since Finta lacks a traditional REST API, limits are defined by plan tiers. It provides reference tables and TypeScript examples for implementing usage tracking, robust webhooks retry strategies, and batch processing to ensure smooth scaling and prevent mid-raise disruptions during integrations.

This comprehensive reference architecture details the technical blueprint for managing fundraising operations using Finta CRM. It outlines the integration of core services—including pipeline tracking, deal rooms, and payment gateways (Stripe)—to provide founders and fund managers with end-to-end visibility. The document covers service layer design, caching strategies, event pipelines, and scaling considerations for robust, automated fundraising management.

A comprehensive guide to managing the full application lifecycle on Fly.io. This workflow covers deploying applications, configuring the environment using fly.toml, managing sensitive secrets, scaling resources across multiple regions, and controlling the application's operational state (including suspension and destruction). Ideal for setting up production-grade, resilient cloud services.