Browse skills

Pinecone enables efficient semantic search by indexing and querying vector embeddings, quickly finding similar content for applications like recommendation & knowledge retrieval.

This skill uses Sentence Transformers to generate high-quality embeddings for text, enabling semantic search and similarity analysis.

Generates high-quality embeddings from text using Grepai and LMStudio for semantic search, similarity analysis, and AI applications.

Generates OpenAI embeddings from text data using grepai's infrastructure for efficient vector representation and similarity search.

This skill integrates Upstash with Vector DBs for efficient similarity search and retrieval of vector embeddings, boosting AI applications.

Find relevant data quickly using Timescale's pgvector semantic search powered by vector embeddings.

This skill generates Google Gemini embeddings for your text data, enabling semantic search and powerful AI applications like similarity analysis.

This skill generates CLIP-aware embeddings to capture visual context for richer semantic understanding and improved AI model performance.

Generates vector representations (embeddings) of text, enabling semantic similarity searches and powerful AI applications like clustering & recommendation.

Vectordb allows you to efficiently store and search vector embeddings for semantic similarity searches, boosting your AI applications' understanding.

Generates vector embeddings from text using Google Gemini, enabling semantic search, similarity analysis, and powerful AI applications.

Scvi Tools provides streamlined access to SCVI embeddings and related analysis for single-cell data, accelerating research insights.

This skill compares embeddings to find similar concepts or identify anomalies by measuring semantic similarity.

This skill generates dense vector embeddings from text modules for semantic similarity searches and enhanced retrieval within RAG systems.

This skill generates vector embeddings of text, enabling semantic similarity searches and powerful AI applications like clustering & recommendation.

This skill generates vector embeddings from text, enabling semantic similarity searches and powering AI applications needing contextual understanding.

This skill generates high-quality embeddings for text using Facebook's (FT) transformer model, boosting semantic understanding and similarity search.

Quickly retrieve relevant documents from large datasets using semantic similarity search powered by vector embeddings.

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

This skill creates isolated Tauri sidecar processes for running embeddings, enhancing performance and security by offloading heavy computations.

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

This skill intelligently combines multiple embeddings using a dynamic fusion strategy for enhanced AI/ML model performance and accuracy.

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.

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