Browse skills

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.

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

This skill generates insightful visualizations of fiftyone image embeddings, aiding in understanding and evaluating model similarity and performance.

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

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.

Umap Learn generates low-dimensional embeddings of data using UMAP, revealing underlying structure and patterns for visualization & analysis.