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LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

PRD.md - Product Requirements & Feature Inventory

Everything built on top of the base OpenClaw platform. Canonical reference for what exists, where it lives, and how it works. Operational use cases and workflow playbooks live in docs/USE-CASES-WORKFLOWS.md.

Markdown Converter Skill

Uses uvx markitdown to convert many document and file types to Markdown.

Great replacement for markitdown-mcp.

Works in Claude (upload markdown-converter.skill), Claude Code (expand the skill zip file into ~/.claude/skills/ or just create a skill directory with SKILL.md) or in any agent using Skillz.

Building Your Own MCP Server for Claude Code

This guide will walk you through creating a custom MCP (Model Context Protocol) server that integrates with Claude Code, allowing you to extend Claude's capabilities with external tools, APIs, or even other AI models.

What is MCP?

MCP (Model Context Protocol) is a protocol that allows Claude to communicate with external servers to access tools and capabilities beyond its built-in features. Think of it as a plugin system for Claude.

	# OpenClaw Implementation Prompts

	Each prompt below is a self-contained brief you can hand to an AI coding assistant (or use as a project spec) to build that use case from scratch. Adapt the specific services to whatever you already use — the patterns are what matter.

	---

	## 1) Personal CRM Intelligence

	```
	Build me a personal CRM system that automatically tracks everyone I interact with, with smart filtering so it only adds real people — not newsletters, bots, or cold outreach.

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	import openai
	import streamlit as st
	from streamlit_chat import message
	from dotenv import load_dotenv
	import os
	from langchain.embeddings.openai import OpenAIEmbeddings
	from langchain.vectorstores import Chroma
	import openai
	from langchain.document_loaders import UnstructuredMarkdownLoader
	from langchain.chains.question_answering import load_qa_chain

	import openai
	import streamlit as st
	from streamlit_chat import message
	from dotenv import load_dotenv
	import os
	from langchain.embeddings.openai import OpenAIEmbeddings
	from langchain.vectorstores import Chroma
	import openai
	from langchain.document_loaders import UnstructuredMarkdownLoader
	from langchain.chains.question_answering import load_qa_chain