Create and manage Pinecone RAG assistants for document-based question answering.
npx skills add https://github.com/FacuM/yolo-agent --skill pinecone-assistant
Please help me install this Agent Skill. Skill: pinecone-assistant Source: https://github.com/FacuM/yolo-agent/tree/main/.claude/skills/assistant Command: npx skills add https://github.com/FacuM/yolo-agent --skill pinecone-assistant
This Skill allows you to easily create AI assistants that can understand and answer questions based on your own documents, eliminating the need to sift through large amounts of text manually.
Use the pinecone-assistant skill to create an assistant named 'my-docs' and upload the file './documentation.pdf' to it.
High-intent search queries and answers about installing and using this skill.
Build a document Q&A assistant by uploading PDFs, Markdown, or text files to Pinecone's RAG service to query content and retrieve cited responses with detailed source references and page numbers.
Pinecone RAG is a retrieval augmented generation service that creates AI assistants to answer natural language questions based on your uploaded documents, returning cited responses with direct references to source files.
Yes, you can chat with PDFs by uploading them to an assistant and asking natural language questions, which returns cited answers with direct references to source documents and relevant page numbers.
Yes, the Pinecone assistant integrates with MCP to enable agent-to-agent communication, allowing multiple assistants to interact while managing document-based knowledge bases.
You can upload PDF, Markdown, and text files to a Pinecone knowledge base, enabling the assistant to parse the content and provide cited responses for natural language queries.
PaldomGenerate synthetic PDF documents with JSON evaluation files for RAG systems.
agilebydesignQuery the abd-answers Pinecone vector index for grounding chunks with similarity scores.
tawf-labsBuild retrieval-augmented generation workflows that ground LLM answers in external documents.
HarriesStore and search embeddings with metadata in a self-hosted database.
