Skill UI

A comprehensive SDK for the Azure AI Document Intelligence service, designed for .NET developers. This tool allows you to programmatically extract text, structured data, and tables from various document types, including invoices, receipts, ID cards, and general documents. It supports both powerful prebuilt models and custom model training.

A comprehensive SDK for the Azure AI Document Intelligence service, designed for .NET developers. This tool allows you to programmatically extract text, structured data, and tables from various document types, including invoices, receipts, ID cards, and general documents. It supports both powerful prebuilt models and custom model training.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

A comprehensive TypeScript client for the Azure Document Intelligence REST API. This SDK allows developers to programmatically extract structured data, text, and tables from various document types, including PDFs and images. It supports using prebuilt models (like invoices, receipts, and IDs) and building custom models for specialized document processing workflows.

This SDK enables developers to build robust document analysis applications using Azure AI Document Intelligence. It provides powerful capabilities to extract structured data from diverse document types, including receipts, invoices, ID cards, and general forms. Developers can analyze documents from local files or URLs to extract key-value pairs, tables, and specific fields automatically.

This SDK enables developers to build robust document analysis applications using Azure AI Document Intelligence. It provides powerful capabilities to extract structured data from diverse document types, including receipts, invoices, ID cards, and general forms. Developers can analyze documents from local files or URLs to extract key-value pairs, tables, and specific fields automatically.

This skill manages and refines search results obtained from Exa's neural search API. It provides comprehensive control over content extraction scope, allowing users to selectively retrieve metadata, highlights, full text, or structured summaries. Key features include implementing result caching with TTL, deduplicating citations, and crucial token budget management, ensuring the retrieved context fits within LLM context window limits for robust RAG pipelines.

This workflow connects to the Fireflies.ai API via GraphQL to fetch and process detailed meeting transcripts. It handles speaker-diarized data, extracting structured insights such as action items, key topics, sentiment analysis, and speaker breakdowns. It enables the creation of comprehensive meeting intelligence reports and robust downstream pipelines. Use this when you need to automate the retrieval, summarization, or deep analysis of recorded meeting content.

This comprehensive guide provides documentation and code examples for utilizing the Gemini API, Google's advanced multimodal AI platform. It enables developers to power applications with cutting-edge capabilities, including text generation, multimodal understanding (images, audio, video), function calling, and structured data output. Supports major languages like Python, JavaScript, and Go.

A comprehensive guide to implementing advanced LLM patterns using the Groq API. This workflow demonstrates how to handle chat completions, robust tool/function calling for integrating external services (like weather or search), and enforcing structured data output using JSON mode and strict schemas. Ideal for building real-time, production-grade AI features.

Provides expert guidance for GRPO training with TRL, covering dataset prep, reward engineering, structured outputs, and multi-objective alignment when preference pairs are scarce.

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.