Skill UI

This guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.

This minimal working example demonstrates Abridge's ambient AI clinical documentation capabilities. It simulates a complete patient encounter session, allowing users to submit transcripts and receive a highly structured clinical note. Ideal for testing EHR connectivity, verifying AI workflow, or learning how structured medical data (SOAP notes, ICD/CPT codes) is automatically generated from free-text conversation.

This guide demonstrates how to implement tool use (function calling) with Claude's Messages API. It covers defining structured tools using JSON Schema, handling tool calls, executing the corresponding code, and feeding the results back to create sophisticated, multi-step AI agents and complex workflows.

Learn how to build complex, multi-step AI agents using Cohere's Chat API v2. This skill demonstrates defining external tools (like weather APIs or database searches) and implementing the full agent loop: model calls tool -> execute tool -> feed result back -> final answer. Ideal for creating autonomous, tool-using workflows that interact with external systems.

This comprehensive guide demonstrates how to implement robust, real-time streaming UIs using Cohere's `chatStream` method. It details handling various event types, including content deltas, citation information for RAG, and structured tool call streams. A practical example is provided for creating a Server-Sent Events (SSE) endpoint, making it ideal for front-end integration with advanced AI features.

This workflow demonstrates the core capabilities of the Glean Client API, allowing users to build comprehensive knowledge assistants. It covers advanced full-text search with filters and facets, implements AI-powered chat for contextual Q&A, and provides autocomplete suggestions. Ideal for integrating deep search and conversational AI into enterprise applications.

This comprehensive guide details advanced workflows for high-quality video generation using platforms like Runway. Learn essential techniques including advanced prompt engineering (Subject + Action + Setting + Style), optimal model selection (e.g., gen4_turbo), and parameter tuning for duration and aspect ratio. The workflow also demonstrates efficient batch generation for creating multiple, consistent video assets.

This skill provides the implementation pattern and comprehensive guide for integrating the Runway ML API. It demonstrates using the Python SDK to perform advanced creative tasks, such as image-to-video generation. Furthermore, it includes crucial error handling instructions for managing common issues like API rate limits, unauthorized access (401), and credit exhaustion.

This guide provides a comprehensive tutorial on running various AI inference tasks using the Together AI API, which is compatible with the OpenAI standard. It demonstrates key functionalities including chat completions, streaming responses, image generation, and creating embeddings. This is ideal for developers who need to test open-source models, compare performance across different LLMs (like Llama or Mixtral), or integrate powerful generative AI features into their applications using Python or Node.js.

A comprehensive resource hub for ML researchers working in scientific domains such as biology, chemistry, genetics, and medicine. It provides a curated catalog of high-signal datasets, pre-trained models, and interactive demos, streamlining the discovery and use of specialized AI/ML resources beyond general search.

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

Automates the creation and publication of polished, functional Gradio demos on Hugging Face Spaces for any user-provided LoRA model. This skill handles the complex workflow of designing the optimal UI, selecting the correct inference pipeline (e.g., text-to-image, video-to-video), and generating all necessary files (app.py, requirements.txt, README.md) to deploy a complete, ready-to-use AI playground.