Skill UI

This SDK provides programmatic access to Azure AI Content Understanding, enabling the extraction of structured and semantic content from diverse multimodal sources. It supports analyzing documents (PDF, etc.), images, audio, and video files. Users can leverage prebuilt analyzers (like invoice or document search) or create custom analyzers with specific field schemas for advanced RAG and automated workflow implementations.

This SDK provides programmatic access to Azure AI Content Understanding, enabling the extraction of structured and semantic content from diverse multimodal sources. It supports analyzing documents (PDF, etc.), images, audio, and video files. Users can leverage prebuilt analyzers (like invoice or document search) or create custom analyzers with specific field schemas for advanced RAG and automated workflow implementations.

This SDK provides comprehensive connectivity to Azure AI Search for .NET developers. It enables building sophisticated search applications, supporting full-text search, vector search, semantic understanding, and hybrid query capabilities. Developers can manage indexes, perform document CRUD operations, and implement advanced search patterns like faceted search and autocomplete.

The official SDK for interacting with Azure AI Search service using Python. It provides comprehensive functionality for advanced search retrieval, including full-text search, vector similarity search (HNSW), hybrid search, and semantic ranking. Use this tool to build sophisticated enterprise search applications that require deep content understanding and precise information retrieval.

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

CLIP enables general-purpose vision-language tasks through zero-shot image classification, similarity scoring, semantic search, and content moderation using pretrained contrastive embeddings without fine-tuning.

A sophisticated system for recovering and reconstructing complex project context across distributed, multi-agent AI workflows. It utilizes advanced semantic vector search, relevance filtering, and optimized rehydration patterns to maintain high knowledge fidelity and ensure seamless continuity in long-running, complex AI projects. Essential for advanced RAG implementations.

A sophisticated memory management system designed to recover, reconstruct, and maintain project context across complex, distributed, and multi-agent AI workflows. It specializes in advanced semantic memory rehydration, utilizing semantic vector search, relevance ranking, and conflict resolution to ensure seamless continuity and high fidelity of historical knowledge.

This tool optimizes the codebase indexing within Cursor. It processes your entire codebase, converting code chunks into vector embeddings stored in a vector database. This enables advanced @Codebase semantic search, allowing users to ask natural language questions (e.g., "Where is authentication handled?") and retrieve the most relevant code snippets, significantly improving AI context awareness across chat and coding tasks.

This advanced workflow extends beyond basic search capabilities. It allows users to discover pages semantically similar to a given URL, retrieve detailed content, highlights, and summaries from known URLs (batch processing), and generate comprehensive, cited answers using AI. Ideal for deep academic research, competitive intelligence, and content enrichment pipelines.

Leverages the Exa API for semantic/embeddings search, discovering similar content, or filtering by categories like companies, people, and research papers, making research and knowledge retrieval more targeted.

This skill provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.