Agent Skills Hub

AgentDB Vector Search

High-performance vector search engine for AI agents featuring sub-millisecond retrieval, HNSW indexing, and quantization for efficient RAG, similarity matching, and knowledge base management.

claude-rag-skills

A suite of professional tools for auditing, evaluating, chunking, and scaffolding production-ready RAG pipelines within Claude Code.

qras

A local RAG semantic memory system using Qdrant and Ollama. Ideal for recalling workspace files, notes, project decisions, and user preferences with high-relevance vector search.

backend-rag-implementation

Build RAG systems to ground LLMs in proprietary data. Includes vector database integration, embedding strategies, hybrid search, and advanced retrieval patterns for FastAPI backends.

upstash-vector-db-skills

Upstash Vector DB setup, semantic search, namespaces, and embedding models. Ideal for building high-performance vector search features in Next.js 16/Vercel projects.

simplemem-skill

Persistent, semantic long-term memory for AI agents. Save, query, and retrieve cross-session dialogues, decisions, and multimodal context using semantic compression.

rag-implementation

Build production-grade RAG systems using vector databases, semantic search, and LangGraph to ground LLMs in external knowledge.

rag-engineer

Architect and optimize production-grade RAG systems. Master embedding models, vector databases, chunking strategies, and retrieval pipelines for high-accuracy LLM applications.

langchain-architecture

Architect production-grade LLM applications using LangChain 1.x and LangGraph. Implement stateful AI agents, multi-step workflows, and custom memory systems for complex conversational and automation tasks.

ReasoningBank with AgentDB

Implement ReasoningBank adaptive learning with AgentDB's ultra-fast vector backend. Features trajectory tracking, verdict judgment, memory distillation, and pattern recognition for self-learning autonomous agents.