Skill UI

A Python implementation utilizing the Azure Speech-to-Text REST API for transcribing short audio files (up to 60 seconds). This method avoids the need for the dedicated Speech SDK, allowing developers to perform speech recognition simply by making HTTP POST requests. It supports both simple and detailed response formats and recommends chunked transfer for lower latency.

A comprehensive guide to diagnose and resolve common errors in Lindy AI agent workflows. Covers troubleshooting issues related to trigger failures (webhooks, email, schedule), action execution errors (Slack, Gmail, HTTP requests, code), and advanced agent step logic problems, ensuring reliable automation.

Manage BrowserWing’s intelligent browser automation platform: install Chrome, configure LLM providers, generate AI-explored scripts, and operate the script lifecycle through HTTP APIs for autonomous exploration, execution, and troubleshooting.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

This comprehensive guide details how to construct a complete, robust Model Context Protocol (MCP) server using TypeScript and the specialized v2 SDK. It covers all aspects of Node.js project setup, selecting appropriate transport layers (HTTP or stdio), implementing complex, schema-validated tools, and integrating resources, resulting in a highly type-safe AI backend service.

This guide provides comprehensive techniques for optimizing ElevenLabs Text-to-Speech (TTS) performance. Learn how to reduce latency and maximize throughput by strategically selecting models (e.g., Flash vs. v3), implementing streaming endpoints (HTTP or WebSocket), and choosing optimal audio formats. It is essential for real-time conversational AI, IVR systems, and high-volume content generation.

The `hf-mem` skill estimates the necessary VRAM or system memory required to load Safetensors or GGUF model weights for inference directly from the Hugging Face Hub. It uses efficient HTTP Range requests, avoiding local downloads. Users can determine if a model fits on their GPU or instance, including optional estimations for the KV cache and setting limits like maximum sequence length.

This skill deploys the OpenMed Model Context Protocol (MCP) server, exposing critical clinical NLP tools such as NER, PII extraction, and de-identification. It enables AI coding agents and chat clients to interact with OpenMed's capabilities via simple tool calls, running locally on-device to ensure user data privacy and security. Supports both stdio and Streamable HTTP deployment modes.