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Found 1,562 Skills
Build Retrieval-Augmented Generation (RAG) applications that combine LLM capabilities with external knowledge sources. Covers vector databases, embeddings, retrieval strategies, and response generation. Use when building document Q&A systems, knowledge base applications, enterprise search, or combining LLMs with custom data.
Complete RAG and search engineering skill. Covers chunking strategies, hybrid retrieval (BM25 + vector), cross-encoder reranking, query rewriting, ranking pipelines, nDCG/MRR evaluation, and production search systems. Modern patterns for retrieval-augmented generation and semantic search.
Implements drag-and-drop and sortable interfaces with React/TypeScript including kanban boards, sortable lists, file uploads, and reorderable grids. Use when building interactive UIs requiring direct manipulation, spatial organization, or touch-friendly reordering.
Implement GraphRAG patterns combining knowledge graphs with retrieval for complex reasoning. Use this skill when building RAG over interconnected data or needing relationship-aware retrieval. Activate when: GraphRAG, knowledge graph, graph retrieval, entity relationships, Neo4j RAG, graph database, connected data.
Use when debugging 'files disappeared', 'data missing after restart', 'backup too large', 'can't save file', 'file not found', 'storage full error', 'file inaccessible when locked' - systematic local file storage diagnostics
Learn how to create an interactive, draggable DOM using a Lit web component with CSS transforms and slots, enabling you to manipulate HTML and SVG elements within a canvas-like environment.
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, constructing context from retrieved documents, adding citations, or implementing hybrid search.
Provides patterns to build Retrieval-Augmented Generation (RAG) systems for AI 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.
Vercel data and storage services including Postgres, Redis, Vercel Blob, Edge Config, and data cache. Use when selecting data storage or caching on Vercel.
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.
Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.