Browse skills

Claude Browser allows you to seamlessly access and integrate web content into your Claude conversations, boosting research & creative output.

This skill generates vector embeddings of text, enabling semantic similarity searches and powering advanced AI applications like recommendation systems.

Eval Relevance assesses how well a given query matches a document’s content, boosting search accuracy and information retrieval effectiveness.

Leverages Anthropic's Claude models to provide expert-level research, analysis, and concise summaries on diverse topics.

Find relevant documents or data points based on semantic similarity using vector embeddings powered by winsorllc.

This Embedding Service converts text into numerical representations (embeddings) for semantic search and machine learning tasks, boosting AI performance.

Ralph Prompt Research — provides AI agent assistance for software development workflows.

Together Embeddings generates high-quality vector representations of text data for semantic search and AI applications, boosting performance & understanding.

This skill generates CLIP-aware embeddings for images and text, enabling powerful semantic search and similarity matching across diverse content.

Semantic memory search using Qdrant + Ollama embeddings (QRAS). Use as primary method for recalling workspace files, notes, decisions, and prior work before falling back to built-in search.

Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization.

Rag Architect constructs well-structured documents from retrieved knowledge, streamlining information synthesis and boosting research efficiency.

This GPT Researcher swiftly gathers information from the web, providing concise summaries and insights for faster research and decision-making.

Leverages PyTorch for deep learning model development and experimentation, accelerating AI research and custom solution creation.

Cocoindex enables semantic search across your documents by indexing content for faster, more relevant retrieval – boosting productivity and knowledge discovery.

Stores and retrieves vector embeddings in a Qdrant database, enabling semantic search and similarity analysis.

Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 min

Generates vector representations (embeddings) of text using Gemini, enabling semantic search and similarity analysis for powerful AI applications.

This skill automatically converts documents into vector datasets for efficient semantic search and AI model training.

Leverage OpenAI's power to conduct in-depth research, synthesize findings, and generate comprehensive reports on complex topics.

Ark Embedding generates vector embeddings for your data, enabling semantic search and similarity analysis across diverse text formats.

Semantic search sidecar for Karakeep bookmarks. Find bookmarks by meaning, not just keywords. Uses vector embeddings for intent-aware retrieval.

Async deep research via Gemini Interactions API. RAG-ground queries on local files, preview costs with --dry-run, get structured JSON output. Universal skill for 30+ AI agents.

Claude Code slash commands for Universal AI Agent Discovery - search 59,000+ agents, resolve UAIDs, chat with AI agents | https://hol.org