Skill UI

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 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 logging wrapper designed to instrument and monitor calls to the Claude API. It automatically tracks critical metrics including token usage, API latency, estimated cost, and error details. Essential for implementing robust observability patterns and setting up proactive alerts in production environments.

This skill provides blueprints for deploying Clari data export pipelines into robust, production-grade cloud environments. It guides users on setting up reliable, scheduled, and serverless data syncs using major platforms like Airflow DAGs, AWS Lambda, and Google Cloud Functions. Ideal for automating revenue forecasting and business intelligence data extraction.

A comprehensive workflow for setting up local development environments for Clari API integrations. It allows users to mock forecast data, test complex export pipelines, and iterate on data transformation logic (such as forecasting and aggregation) without requiring live API calls. Ideal for building robust dashboards and validating data workflows.

This comprehensive guide teaches users how to set up a ClickHouse database environment. It covers creating a MergeTree table, inserting structured event data using TypeScript, and executing essential analytical queries (e.g., counting, grouping, unique user metrics). Ideal for beginners learning OLAP databases, testing connections, or mastering core data warehousing concepts.

This guide provides comprehensive steps for setting up and authenticating the official ClickHouse client for both Node.js and Python. It covers connecting to ClickHouse Cloud or a self-hosted instance, managing environment variables, and verifying the connection using sample code snippets. Ideal for new ClickHouse projects or integrating data analytics pipelines into existing applications.

Provides a comprehensive setup guide for running ClickHouse locally using Docker Compose. This includes defining the database schema, seeding mock data, and setting up robust integration tests (using vitest). Ideal for developers building OLAP systems or performing local data analysis to ensure schema and query reliability.

Facilitates the secure deployment of Adobe-powered applications across multiple modern cloud environments, including Vercel (serverless), Google Cloud Run (containers), and Adobe App Builder. This integration manages complex OAuth credential injection and sets up health monitoring endpoints, making it ideal for moving Adobe API integrations into production systems.

This comprehensive guide covers the full lifecycle of Apify Actors, enabling deployment from local environments to production platforms. Learn to trigger scraping tasks via APIs (e.g., Next.js routes), consume structured data, and handle results asynchronously using webhooks in frameworks like Express.js. Ideal for building robust, data-driven web services and automating data pipelines.

This guide provides comprehensive instructions for setting up and authenticating the Apify ecosystem. It covers installing and configuring core components including the apify-client library, the native Apify SDK, and the Apify CLI. Learn how to manage API tokens, use environment variables, and initialize the necessary tools for building web scrapers and automation workflows.