Skill UI

A powerful cognitive tool that moves beyond surface-level brainstorming. It forces users to systematically deconstruct complex problems down to their 'irreducible truths.' The skill identifies 8-12 hidden assumptions (Surface, Middle, Deep), classifies them (Fact, Convention, Belief, Interest-Driven), and ranks them by Fragility × Impact. This process allows users to stress-test their premises and rebuild conclusions based only on verified realities, making it invaluable for major life decisions or business hypothesis testing.

This guide details setting up continuous integration and continuous deployment (CI/CD) pipelines for web scraping projects. It integrates Bright Data proxy testing into GitHub Actions, allowing developers to run both mocked unit tests (for quick checks) and live integration tests (verifying actual proxy connectivity) automatically upon code push or pull request.

This guide details how to set up robust CI/CD pipelines for Canva Connect API integrations using GitHub Actions. It covers unit testing with MSW mocks and full integration testing, including secure secret management and automatic token refreshing workflows. Use this when automating Canva API tests in your build process to ensure reliable deployments.

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.

This comprehensive guide teaches developers how to interact with Claude AI using the official Anthropic SDK. It provides step-by-step instructions on basic message creation, implementing system prompts, and managing multi-turn conversation flows via the Messages API. Ideal for setting up a new Claude integration or testing core API functionality.

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.

This guide provides a comprehensive walkthrough for interacting with the Clari API to export critical business data. It covers listing available forecasts, initiating the export of forecast and quota snapshots, monitoring the job status, and ultimately downloading and parsing the structured data. This skill is ideal for testing API connectivity and integrating Clari's revenue intelligence into external data pipelines.

Automate the execution of integration tests against a live ClickHouse database instance within a Continuous Integration (CI) pipeline using Docker containers. This setup eliminates the need for mocks, ensuring thorough schema validation and query reliability in environments mirroring production deployments. Ideal for robust CI/CD setup and schema enforcement.

This guide provides a comprehensive walkthrough for starting with the ClickUp API v2. It details the platform's core data hierarchy (Workspace > Space > Folder > List > Task) and demonstrates the fundamental API calls required for integration setup. Users will learn how to list workspaces, retrieve spaces, and successfully create a new task using both cURL and TypeScript examples. It is ideal for developers beginning a new ClickUp integration or requiring basic API testing.

This guide sets up a comprehensive, fast, and reproducible local development workflow for integrating with the Cohere API v2. It utilizes mocking, vitest for unit testing, and hot reload capabilities (tsx watch) to ensure rapid iteration cycles without constant reliance on external APIs. Ideal for developers building AI applications with Cohere.

This guide demonstrates how to deploy GPU workloads on the CoreWeave platform using Kubernetes (kubectl). It covers deploying vLLM inference services for large language model (LLM) testing and running batch CUDA jobs for benchmarking. This is ideal for users running their first GPU workloads, testing inference pipelines, or verifying cluster access on bare-metal GPU nodes (A100, H100).

A comprehensive guide to setting up CI/CD pipelines for ElevenLabs integrations. It implements a robust two-tier testing strategy: running mocked unit tests on every push to save API quota, and executing gated, real integration tests only on main branches or manually. Essential for automated validation of TTS projects using GitHub Actions.