Embeddings

A comprehensive reference guide for selecting and optimizing embedding models for vector search and RAG applications. Covers model comparisons (OpenAI, Voyage, BGE, E5), chunking strategies (token-based, sentence-based, semantic), domain-specific pipelines, and retrieval quality evaluation metrics.

This skill provides comprehensive documentation for building RAG (Retrieval-Augmented Generation) systems. It covers vector database selection (Pinecone, Weaviate, Chroma), embedding models, document chunking strategies, retrieval optimization techniques, and prompt engineering patterns for knowledge-grounded LLM applications.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.