Pinecone RAG Skill

This skill guides you through building a production RAG pipeline or persistent agent memory system using Pinecone. Follow the workflow from start to finish — don't skip steps or jump to code before understanding what the user actually needs.

Before you start — ask one question

Before writing any code, identify which of these two use cases applies:

A — RAG over documents: User wants to index a corpus (PDFs, docs, code, web pages) and retrieve relevant chunks to ground LLM responses.

B — Agent memory: User wants an agent to remember facts, decisions, or context across sessions or across multiple agents sharing a knowledge base.