Skill UI

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.

This skill provides comprehensive management for creating, editing, and automating presentations using LibreOffice Impress. Users can generate ODP presentations from scratch or templates, automating slide content and batch operations. It supports robust format conversion between ODP, PPTX, PDF, and other formats, offering deep integration via command line or Python scripting (UNO) for complex workflow automation.

This guide provides step-by-step instructions for setting up and authenticating with the Instantly.ai API v2. It covers generating necessary API keys, setting up environment variables, and provides ready-to-use client wrappers in TypeScript and Python. Use this when building custom integrations or automating workflows with the Instantly outreach platform.

A comprehensive set of production-ready client patterns for integrating with the Instantly.ai API v2 using TypeScript and Python. It provides robust solutions for managing API interactions, including type safety, sophisticated retry logic, pagination handling, and multi-tenant support, essential when building reliable, reusable wrappers without an official SDK.

This skill demonstrates how to use the Juicebox SDK to perform advanced people searches and profile enrichment. Users can search for individuals using natural language queries (e.g., "senior ML engineer at FAANG with PhD") and retrieve detailed contact information, skills, and education data for professional outreach and market research. Supports both TypeScript and Python.

Automated backend guidance for Kafka producer and consumer workflows, offering best practices, code samples, and configuration help for Node.js, Python, Go, databases, caching, messaging, and microservices scenarios.

This tool provides comprehensive Python solutions for processing batches of text-to-video requests using the Kling AI API. It handles controlled parallelism, rate-limiting, and detailed status tracking. Use this when building robust content pipelines that require generating multiple videos efficiently from a list of prompts, supporting synchronous, asynchronous, and webhook callback methods.

Implements robust content policy compliance for generative AI prompts and outputs, such as those from Kling AI. This skill provides pre-submission filtering functionality, preventing the generation of restricted content (e.g., violence, explicit material, hate speech) and saving API credits. It includes Python code examples for prompt screening and handling server-side rejection messages.

A comprehensive guide for developers on utilizing the Kling AI Text-to-Video REST API. This skill provides minimal working examples in Python and Node.js, demonstrating the complete workflow: submitting a text prompt, polling the task status, and retrieving the final generated video URL. Ideal for integrating advanced AI video generation capabilities into development projects.

A comprehensive guide detailing how to set up secure API authentication for Kling AI using JSON Web Tokens (JWT). This process involves generating tokens from your Access Key (AK) and Secret Key (SK) credentials, providing code examples for Python and Node.js, and implementing best practices for token management and refreshing to ensure continuous API access.

A production-ready Python SDK providing comprehensive patterns for integrating with the Kling AI API. This wrapper handles crucial operational complexities such as auto-refreshing JWT tokens, structured request/response models, exponential backoff polling for long-running tasks, and detailed error handling. Use this library when building stable, high-reliability applications that require advanced text-to-video or image-to-video synthesis capabilities.

This skill provides a comprehensive guide for deploying LangChain chains and agents into production-grade APIs. It supports multiple development stacks, including Python (LangServe/FastAPI) and Node.js (Express). Topics covered include containerization using Docker, setting up robust health checks, and deploying to major cloud platforms like Google Cloud Run and AWS Lambda. Learn how to build scalable, observable, and enterprise-grade API endpoints for advanced LLM applications.