Skill UI

This pattern provides comprehensive guidance for integrating the Wispr Flow voice-to-text API. It covers best practices for both low-latency WebSocket streaming and simpler REST API usage. Key topics include authentication, recommended audio formats, robust error handling, and context awareness (e.g., understanding code or CLI commands), making it ideal for building advanced voice-powered applications.

A comprehensive guide for migrating and integrating with the Wispr Flow voice-to-text API. This skill details best practices for both WebSocket (low-latency streaming) and REST API methods. It covers authentication, optimal audio formatting (16kHz mono PCM), context awareness for specialized jargon (e.g., code), and robust error handling across multiple platforms (Mac, iOS, browser).

A comprehensive guide for integrating the Wispr Flow voice-to-text API using webhooks. This skill covers best practices for real-time speech recognition, including low-latency WebSocket streaming and robust REST API calls. It is essential for developers building voice-powered applications, dictation tools, or any system requiring advanced audio processing and context-aware transcription.

Client library for Azure OpenAI and OpenAI services in .NET. Use for chat completions, streaming, embeddings, image generation, audio transcription, structured outputs, reasoning models, and assistants.

Build real-time voice AI applications with the Azure AI Voice Live SDK for .NET, featuring bidirectional WebSocket communication, audio streaming, session events, and function calling.

Build real-time voice AI applications on Azure with bidirectional WebSocket streaming using the azure-ai-voicelive Python SDK. Covers authentication, session configuration, PCM16 audio streaming, event handling and function tools.

Build real-time voice AI assistants in JavaScript/TypeScript with Azure Voice Live SDK, supporting bidirectional WebSocket communication, audio streaming, VAD, transcription, and function calling.

Python REST API examples for transcribing short audio files up to 60 seconds with Azure Speech to Text. Covers WAV/OGG formats, chunked streaming, response parsing, and key or bearer-token authentication without the Speech SDK.

This comprehensive guide covers all aspects of integrating the Google Gemini API into your applications using Python or Node.js. It details basic text generation, advanced multimodal inputs (text, image, audio), streaming responses, function calling/tool use, multi-turn chat implementation, and production best practices like model selection and robust error handling.

Build real-time bidirectional streaming applications using the Gemini Live API. Supports WebSocket-based audio/video/text streaming, voice activity detection, function calling, and session management. Includes SDKs for Python and JavaScript for low-latency AI interactions.

This comprehensive skill provides an end-to-end solution for video and audio content creation. Users can upload videos from various sources (YouTube, local files) and perform advanced operations such as real-time streaming, semantic search, and complex editing (trimming, overlays). Key features include AI-powered transcription, subtitle generation, and the ability to generate diverse media assets like images, music, sound effects, and voiceovers, making it ideal for content creators and media professionals.

Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support. Ideal for creating voice assistants, customer service agents, and voice-enabled applications that process streaming audio and integrate multiple AI providers effectively.