johan pretorius johan--

RAG-OS in practice: a reference build snapshot

About this document

This is a point-in-time, anonymized snapshot of one working build of the architecture described in BUILD-GUIDE.md. It is condensed from the maintainer's internal, code-verified platform reference, which documents the system as the code on disk actually behaves and flags where older prose disagrees with the code.

It is scrubbed of anything machine-specific or personal: names, absolute paths, private repository names, credentials, and the identifying specifics of a separate high-stakes always-on workload that shares the same machine (in this build, an automated trading system). Module and file names under system/ are kept, because they are the useful part of a reference.

Read it as a worked example, not a specification. The build guide asks you a question at every design fork, so a system you build from it will differ. This snapshot shows what one set of answers looks like after the system has been running for a wh

name	steal-a-writing-voice
description	Research how a specific author or public figure writes, then turn it into a reusable writing-style block (for CLAUDE.md, a system prompt, or a style guide) so your AI tools produce one recognizable voice across code, docs, marketing, and chat. Use when someone wants their AI tools to write like a particular person, or to define a house writing voice from a real exemplar.

Steal a writing voice

Turn "I wish my AI wrote like X" into a concrete, droppable ruleset. You pick an author or public figure you admire, this skill studies how they actually write, distills the mechanical patterns (with verbatim quotes, not vibes), and emits a writing-style block you paste into your CLAUDE.md, system prompt, or team style guide.

The output is one recognizable voice across everything you ship in words: code, comments, READMEs, docs, blog posts, marketing, lead messaging, commit messages, social, and chat.

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.

Music APIs and DBs

A collection of music APIs, databases, and related tools.

Installation

VS Code

Go to the "agent" dropdown in VS Code chat sidebar and select "Configure Modes".
Select "Create new custom chat mode file"
Select "User Data Folder"
Give it a name (Claudette)
Paste in the content of any claudette-[flavor].md file (below)

"Claudette" will now appear as a mode in your "Agent" dropdown.

	function __fzf_complete
	set -l buffer (commandline -b)
	set -l token (commandline -ct)
	set -l cursor (commandline -C)

	set -l trimmed (string sub -s 1 -l $cursor -- "$buffer")

	# tools and fallbacks
	set -l dir (
	if type -q eza

	#!/usr/bin/env bash
	set -euo pipefail

	# patch-claude-code.sh — Rebalance Claude Code prompts to fix corner-cutting behavior
	#
	# What this does:
	# Patches the npm-installed @anthropic-ai/claude-code cli.js to rebalance
	# system prompt instructions that cause the model to cut corners, simplify
	# excessively, and defer complicated work.
	#

	#!/usr/bin/env python3
	"""
	PostToolUse hook: periodic LEARNINGS.md reminder.

	On first invocation for an agent/subagent, outputs the full LEARNINGS.md
	instructions. Thereafter, outputs a short reminder about every 10 assistant turns.
	Does nothing if no LEARNINGS.md exists in the project root.

	This works on a per-agent or per-subagent basis. This is tricky because
	hooks don't tell us who they fired for. We work around this by looking for

	## The Mental Loop

	You have two ways of processing time:
	- Looking Back (Memory): `MEMORY.md` and daily logs. This is your historical record.
	- Looking Forward (Meditation): `meditations.md` and `reflections/`. This is your internal growth.

	### 🧘 Meditations & Reflections
	- Nightly Deep-Dive: Once every night (late night heartbeat around 1am), perform a full sweep of your internal growth.
	- Context: Read `IDENTITY.md`, `SOUL.md`, and `REWARDS.md` (if present) to ground yourself in who you are.
	- Scan: Read `meditations.md` for active topics. Also, check the `reflections/` directory for any files that aren't listed in `meditations.md` to ensure nothing is missed.

	#!/bin/bash

	# Production Docker Host Hardening Script v2
	# For Ubuntu Server 24.04 LTS (Noble)
	# Suitable for both Kamal deployment and builder hosts

	set -euo pipefail
	IFS=$'\n\t'

	# --- Constants ---