Skill UI

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, multi-phase checklist for deploying and validating CAST AI cost optimization and autoscaling features on a production Kubernetes cluster. It guides users through prerequisites, initial monitoring setup (Phase 1), full autoscaling enablement (Phase 2), security hardening (RBAC, secrets), and critical validation commands for safe go-live deployment.

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.

A comprehensive guide for testing and validating Claude API integrations within Continuous Integration/Continuous Deployment (CI/CD) pipelines. Learn best practices for managing API keys securely, implementing effective mocking strategies for unit tests, and executing real integration tests while controlling costs using tools like GitHub Actions.

A comprehensive guide to implementing secure, scalable, and audited access control for Anthropic enterprise deployments. It details best practices for establishing Organization structures, isolating environments (Dev/Staging/Prod), managing API keys, and implementing Role-Based Access Control (RBAC) to prevent security breaches and control costs.

This guide sets up a fast, cost-effective local development loop for building applications powered by the Anthropic API (Claude). It includes instructions for hot reloading, cost-saving tips (e.g., using Haiku, token limits), and mock client implementation for robust unit testing. It supports both TypeScript/Node.js and Python environments, streamlining the AI development workflow.

A comprehensive checklist designed to ensure Claude-powered applications are robust, secure, and production-ready before going live. It guides developers through critical areas, including robust error handling, API security best practices, cost control mechanisms, real-time monitoring setup, and performance optimization, ensuring a stable user experience.

A comprehensive guide to production-ready patterns for integrating with Anthropic's Claude API using both TypeScript and Python SDKs. Covers robust client setup (retries, timeouts), advanced error handling, streaming implementations, prompt caching, and batch processing to ensure high performance and cost efficiency in real-world AI applications.

This guide details essential best practices for securing applications integrated with Anthropic's Claude API. It covers critical security measures including secure API key management (server-side only), robust input validation to prevent cost attacks, defense mechanisms against prompt injection, and implementing per-user rate limiting. Essential reading for maintaining data privacy and system integrity in production environments.

This guide provides strategies to minimize API overhead when integrating with Clari. Users can optimize export frequency (e.g., daily vs. quarterly), implement data caching, and restrict data exports to only the necessary types, thereby reducing operational costs and improving system performance.

Provides strategies to optimize costs when utilizing the Cohere API. This guide covers model tier selection (economy, standard, premium), implementing token budgets, reducing embedding storage costs (e.g., using int8), and establishing comprehensive usage trackers. It is essential for maintaining cost predictability in production AI deployments.

A comprehensive solution for setting up production-grade observability for Cohere API v2. It integrates Prometheus metrics, OpenTelemetry tracing, and robust alerting mechanisms. This skill tracks critical operational data including per-endpoint latency, token consumption (input/output), error rates, and usage costs, ensuring reliable monitoring for AI-powered applications.