Embeddings

A comprehensive guide for selecting and optimizing embedding models for vector search and RAG applications. Provides code templates for Voyage AI, OpenAI, and local embedding models, along with chunking strategies, domain-specific pipelines, and quality evaluation methods.

This skill provides comprehensive documentation and code examples for building RAG (Retrieval-Augmented Generation) systems. It covers vector database setup (Pinecone, Weaviate, Chroma, pgvector), embedding strategies, retrieval patterns (hybrid search, HyDE, multi-query), chunking strategies, and evaluation metrics using LangChain and LangGraph.

A comprehensive plugin for building production-ready LLM applications, including RAG systems with vector databases, AI agents using LangGraph, advanced prompt engineering patterns, and embedding strategies. It provides templates and best practices for integrating with Pinecone, Qdrant, pgvector, and other vector stores.

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.

This skill provides a quick reference guide for the latest AI models across major providers. It includes model IDs, pricing comparisons, usage examples, and selection recommendations for tasks like text generation, code completion, image generation, voice synthesis, and embeddings.

AI Partner Chat provides personalized conversations by integrating user/AI personas and vectorized personal notes. It uses a local ChromaDB database with BAAI/bge-m3 embeddings to retrieve relevant context from your markdown notes during conversations.

Vexor CLI is a semantic file discovery skill that helps locate files by intent rather than exact text matching. It wraps the vexor command-line tool to enable natural language queries like 'find authentication logic' across codebases, using embedding providers (OpenAI, Gemini, or local models) for semantic search.