By wshobson
Build Retrieval-Augmented Generation (RAG) systems by connecting LLMs to vector databases for semantic search and knowledge-grounded AI. Enables document Q&A, reduces hallucinations, and integrates external knowledge bases into LLM applications.
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npx claudepluginhub p/wshobson-wshobson-rag-implementation-plugins-llm-application-dev-skills-rag-implementationComprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation
ARM Cortex-M firmware development for Teensy, STM32, nRF52, and SAMD with peripheral drivers and memory safety patterns
Modern Julia development with Julia 1.10+, package management, scientific computing, high-performance numerical code, and production best practices
JavaScript and TypeScript development with ES6+, Node.js, React, and modern web frameworks
Web scripting with PHP and Ruby for web applications, CMS development, and backend services
LLM application development with RAG, embeddings, LangChain, and prompt engineering
Build Retrieval-Augmented Generation pipelines
Editorial "LLM Application Developer" bundle for Claude Code from Antigravity Awesome Skills.
OpenRAG agent skills: guided installation and SDK integration helpers.
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.
Google File Search API powered RAG pipeline - managed retrieval-augmented generation with document processing