Loading...
Loading...
Found 89 Skills
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when "building RAG, vector search, embeddings, semantic search, document retrieval, context retrieval, knowledge base, LLM with documents, chunking strategy, pinecone, weaviate, chromadb, pgvector, rag, embeddings, vector-database, retrieval, semantic-search, llm, ai, langchain, llamaindex" mentioned.
Search and navigate large codebases efficiently. Use when finding specific code patterns, tracing function calls, understanding code structure, or locating bugs. Handles semantic search, grep patterns, AST analysis.
Search ChEMBL bioactive molecules database with natural language queries. Find compounds and assay data with Valyu semantic search.
End-to-end drug discovery platform combining ChEMBL compounds, DrugBank, targets, and FDA labels. Natural language powered by Valyu.
Search FDA drug labels with natural language queries. Official drug information, indications, and safety data via Valyu.
Search bioRxiv biology preprints with natural language queries. Semantic search powered by Valyu.
Search personal markdown knowledge bases, notes, meeting transcripts, and documentation using QMD - a local hybrid search engine. Combines BM25 keyword search, vector semantic search, and LLM re-ranking. Use when users ask to search notes, find documents, look up information in their knowledge base, retrieve meeting notes, or search documentation. Triggers on "search markdown files", "search my notes", "find in docs", "look up", "what did I write about", "meeting notes about".
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
Semantic search skill using Exa API for embeddings-based search, similar content discovery, and structured research. Use when you need semantic search, find similar pages, or category-specific searches. Triggers: exa, semantic search, find similar, research paper, github search, 语义搜索, 相似内容
Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, or context augmentation.