Skill UI

This workflow provides comprehensive guidance for integrating the Wispr Flow voice-to-text API. It supports both low-latency WebSocket streaming and simpler REST API calls. It is essential for developers building sophisticated, voice-powered applications, including real-time dictation, transcribing content, and integrating voice inputs into core development tools.

This technical guide provides comprehensive integration details for the Wispr Flow voice-to-text API. It equips developers with the knowledge to build sophisticated, voice-powered applications. Learn how to utilize WebSocket streaming for low latency or REST endpoints for simple transcription, ensuring accurate context awareness for code and development jargon across multiple platforms.

This guide demonstrates how to integrate Wispr Flow for robust voice-to-text transcription. It covers two primary methods: asynchronous REST calls for transcribing audio files, and real-time WebSocket streaming for live microphone input. Wispr specializes in developer-context-aware dictation, making it ideal for building complex voice-powered applications, especially those involving code or technical jargon.

This guide provides comprehensive instructions for integrating the Wispr Flow API for advanced voice-to-text transcription. It details both WebSocket streaming (recommended for low latency) and REST API endpoints, covering authentication setup, API key configuration, and accessing short-lived client access tokens. Ideal for building real-time, voice-powered applications, such as code editors or dictation tools.

This guide provides essential security and technical guidance for integrating Wispr Flow's voice-to-text API. It covers best practices for authentication (API keys, access tokens), utilizing streaming methods like WebSockets, handling common API errors, and optimizing context awareness for high-accuracy transcription in various applications.

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.

This specialized agent allows applications to programmatically join, monitor, and leave live video conferences. It reads real-time events visible to the current identity, such as participants joining/leaving, speaking, chatting, and screen sharing. It is ideal for querying what happens during an active meeting or identifying the correct active meeting IDs. Note: It does not handle post-meeting tasks like transcriptions or recordings.

This skill allows users to mine their extensive coding history across various AI agents (Claude, Codex, Gemini, etc.) into a persistent, structured corpus. It surfaces reproducible workflows as evidence and interactively helps the user forge these past practices into reusable, installed skills across their agent ecosystem. It is designed to synthesize judgment from raw transcripts into actionable, portable skills.

This skill fetches raw text transcripts from any YouTube video. It prioritizes using the DeepAPI service for reliable, server-side scraping, which bypasses local bot detection issues. If DeepAPI fails or the API key is unavailable, it gracefully falls back to using the local `yt-dlp` tool. The output is always saved as a clean, unformatted .txt file.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

This skill transforms completed debugging sessions or repeated corrections into evidence-backed procedures. It captures reusable lessons upon explicit request, ensuring privacy by not automatically saving memories. Users must provide session summaries or transcripts to identify failures and verify fixes. The output is a reviewed procedure suitable for documentation, excluding secrets and personal details. It helps maintain knowledge without modifying agent configuration automatically.

Text-to-speech and speech-to-text using fal.ai audio models. Handles authorized transcription and synthesis jobs, including input resolution, output format, and status polling.