Skill UI

This SDK provides a comprehensive Python client library for interacting with Azure AI Speech-to-Text services. It supports both real-time streaming transcription and robust batch processing of audio files. Users can leverage advanced features such as speaker diarization (identifying multiple speakers) and capturing precise timestamps, making it invaluable for automated meeting minute generation, podcast content processing, and live captioning solutions.

This SDK provides a comprehensive client library for Azure AI Document Translation service. It enables large-scale, batch translation of various document formats, including Word, PDF, Excel, and PowerPoint, while accurately preserving the original document structure and formatting. It supports features like glossary integration and async operations, making it ideal for enterprise content localization pipelines.

This SDK provides a comprehensive client library for Azure AI Document Translation service. It enables large-scale, batch translation of various document formats, including Word, PDF, Excel, and PowerPoint, while accurately preserving the original document structure and formatting. It supports features like glossary integration and async operations, making it ideal for enterprise content localization pipelines.

A comprehensive TypeScript SDK for integrating Azure AI Translator services. It provides robust clients for both real-time text translation (including language detection, transliteration) and large-scale document translation. Users can translate single documents or manage complex batch translation jobs using REST-style API calls, making multilingual support easy to integrate into applications.

This SDK provides a comprehensive Python client for Azure AI Vision 4.0, enabling advanced image understanding tasks. It supports generating descriptive captions, extracting content tags, detecting objects and people with bounding boxes, performing OCR (text recognition), and suggesting smart crop regions. It is ideal for computer vision applications and detailed image content analysis.

A comprehensive guide for installing and configuring the Deepgram SDK for both Node.js and Python environments. This skill covers best practices for secure API key management using environment variables, client initialization (including version differences), and connection verification. Use this when integrating Deepgram's advanced speech-to-text and text-to-speech capabilities into a new application.

Practical guide to integrating Claude models: covers API client usage, streaming responses, tool calls, RAG pipelines, document chunking, cost-aware model selection, and anti-pattern warnings for efficient LLM workflows.

This skill provides production-ready patterns for integrating with the Perplexity Sonar API in both TypeScript and Python. It covers best practices for creating robust wrappers around the standard OpenAI client, including dedicated handling for citations, search results, and related questions. It also includes essential utilities like implementing exponential backoff for reliable retries. Use this when setting up complex search-augmented generation workflows or standardizing team coding practices around Perplexity usage.

Guides connecting a Rube MCP client to Perplexityai via Composio, from discovering up-to-date tool schemas through RUBE_SEARCH_TOOLS to executing actions in RUBE_MULTI_EXECUTE_TOOL while monitoring connection status.

This resource provides production-ready, structured patterns for building robust voice agent applications using the Retell AI SDK. It covers best practices such as implementing singleton client management, defining typed agent configurations with integrated LLMs, adding automatic retry logic for call failures, and managing large-scale outbound campaigns. Use this when building stable, scalable, and reliable voice automation systems.

This infrastructure provides a robust solution for running powerful AI agents in remote, sandboxed environments. It overcomes the limitations of local execution by ensuring unlimited concurrency, consistent environments, and collaborative features. Use this to build complex background coding agents, multi-client interfaces (e.g., Slack, Web), or scale agent workloads far beyond local machine constraints.

Transformers.js allows developers to run state-of-the-art machine learning models directly within JavaScript/TypeScript environments (browser, Node.js). It supports a comprehensive range of tasks including NLP (text generation, summarization, translation), Computer Vision (image classification, object detection), and Audio processing. This capability enables building fully client-side, multimodal AI applications without requiring a dedicated Python backend or server infrastructure.