Agent Skills Hub

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.

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.

rag-engineer

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

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.

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.

basic-usage

Essential guide to llmemory for document storage and search: installation, database setup with pgvector, document ingestion, hybrid/semantic retrieval, and building RAG systems with multi-tenant support.

postgresql-table-design

A comprehensive guide for designing high-performance, maintainable PostgreSQL database schemas, covering best practices, data types, indexing, and advanced features.

llm-integration

A toolkit for building robust LLM integrations: API patterns, streaming, function calling, RAG pipelines, and cost-effective model routing.

rag-implementation

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

grepai-config-reference

Comprehensive reference for GrepAI configuration, detailing the .grepai/config.yaml schema, embedder settings, storage backends, and optimization parameters.

gemini-interactions-api

Expert skill for implementing the Gemini Interactions API. Use for stateful multi-turn chat, background Deep Research agent tasks, function calling, structured outputs, and modern Python/TypeScript SDK integration.

langchain-chat-models

A unified interface for integrating and managing LLM chat providers like OpenAI, Anthropic, Google, Azure, and Bedrock within LangChain applications.