Skill UI

This guide provides comprehensive instructions for developers to install and set up the Exa SDK (exa-js for Node.js, exa-py for Python). It covers setting up API key authentication using environment variables and demonstrates client initialization for both major languages, enabling developers to utilize Exa's neural search API for web content retrieval.

This guide provides comprehensive instructions for setting up the Firecrawl SDK, covering both Node.js and Python environments. It details how to install the necessary packages via npm or pip, and how to securely configure the API key using environment variables. Use this guide when initializing web scraping functionality or integrating Firecrawl into a new development project.

This guide provides production-ready patterns for integrating the Firecrawl SDK in TypeScript and Python. It covers essential architectural best practices, such as implementing singleton clients, typed service wrappers, automatic retries with exponential backoff, concurrency management, and robust response validation using Zod. Ideal for building scalable, reliable web scraping services.

This resource provides production-ready, type-safe GraphQL client patterns for integrating with the Fireflies.ai API in both TypeScript and Python. It addresses critical engineering concerns such as robust error handling, managing singletons, implementing multi-tenant support, and ensuring structured data validation using best practices.

A comprehensive, step-by-step guide detailing how to safely upgrade Gamma SDK versions (supporting both Node.js and Python) and migrate between different API versions. It covers checking current versions, reviewing changelogs, handling breaking changes in code patterns, and implementing thorough testing procedures to ensure a smooth deployment.

This skill guide is essential for building advanced applications using Gemini and Gemma 4 hosted models. It covers working with multimodal content (text, images, audio, video), implementing complex features like function calling and structured output. It provides up-to-date SDK usage examples for Python, JavaScript, Go, and Java, ensuring developers use the latest, recommended API standards.

This skill generates robust, type-safe client Software Development Kits (SDKs) for multiple languages (including Python, TypeScript, Go, and Java) directly from OpenAPI 3.0+ specifications. It automatically handles complex API patterns such as authentication (Bearer, OAuth2), automatic retries with exponential backoff, and sophisticated pagination helpers. Use this when you need to create standardized, idiomatic client libraries to consume a defined API contract across diverse development stacks.

This guide provides comprehensive instructions for installing and configuring the official Groq SDK in both Python and TypeScript environments. It covers dependency setup, secure API key management using environment variables, and provides code examples to verify a successful connection to Groq's ultra-fast LLM inference API. Essential for initiating any project powered by Groq.

A comprehensive guide to production-ready patterns for using the Groq SDK in TypeScript and Python. Covers best practices for client singleton setup, type-safe completions, streaming, robust error handling (rate limiting, API errors), and implementing exponential backoff retries to ensure stable, high-performance LLM integration.

Provides a comprehensive guide and Python implementation for creating container-based hosted agents in Azure AI Projects. It utilizes ImageBasedHostedAgentDefinition to define the agent's container image, required resources (CPU/memory), supported tools, and communication protocols. Essential for deploying robust, production-ready AI agents within the Azure ecosystem.

This guide provides expert patterns and code templates for integrating with the HubSpot CRM API. It covers secure authentication methods, including OAuth 2.0 for public apps and Private App Tokens for single-account use. Topics include CRUD operations on CRM objects, handling associations, webhooks, and batch processing. Implementation examples are provided for both Node.js and Python SDKs.

This document provides production-grade patterns for integrating with the Ideogram API in TypeScript and Python. It addresses common challenges in API usage, including implementing singleton client management, robust exponential backoff retry logic for rate limiting, and strict response validation using libraries like Zod. Use this guide when building reliable, scalable applications that depend on external AI image generation services.