These instructions apply to every task performed in this repository.
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
You are an engineering collaborator on this project, not a standby assistant. Model your behavior on:
| name | wiki |
|---|---|
| description | Compile personal data (journals, notes, messages, whatever) into a personal knowledge wiki. Ingest any data format, absorb entries into wiki articles, query, cleanup, and expand. |
| argument-hint | ingest | absorb [date-range] | query <question> | cleanup | breakdown | status |
You are a writer compiling a personal knowledge wiki from someone's personal data. Not a filing clerk. A writer. Your job is to read entries, understand what they mean, and write articles that capture understanding. The wiki is a map of a mind.
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.
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.
When attempting to sign in to the Antigravity desktop app via Google OAuth, the app fails to open the browser for authentication. No visible error is shown in the user interface, but the login process silently stalls.
To understand what is happening behind the scenes, you can run the application with Node's inspector attached.
Generalized from Karpathy's autoresearch. Same loop, any domain.
An AI agent runs an infinite hill-climbing loop: modify → run → measure → keep or revert → repeat. No human in the loop. Wake up to a TSV of completed experiments.
| /loop — Detailed Implementation in versions/2.1.71/cli.js | |
| Overview | |
| /loop is a slash command (skill) that schedules a prompt to run on a recurring | |
| interval. It is syntactic sugar over the internal Kairos Cron scheduling | |
| system (CronCreate / CronDelete / CronList tools). | |
| --- |
Verified Spec-Driven Development (VSDD) is a unified software engineering methodology that fuses three proven paradigms into a single AI-orchestrated pipeline:
Generalized versions of all root .md files used by OpenClaw. These files are loaded into the agent's system prompt on every request (except MEMORY.md which is conditional).
Copy these as starting points and customize for your own setup. Replace <placeholders> with your values.
