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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Dependency auditing, version management, and security vulnerability scanning
SAST analysis, dependency vulnerability scanning, OWASP Top 10 compliance, container security scanning, and automated security hardening
Database architecture, schema design, and SQL optimization for production systems
Comprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation
ML model training pipelines, hyperparameter tuning, model deployment automation, experiment tracking, and MLOps workflows
npx claudepluginhub p/wshobson-wshobson-rag-implementation-plugins-llm-application-dev-skills-rag-implementationLLM 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