AVAudioVision

name

code-review-turbo

description

Run a triple-agent code review on the current branch's PR. Waits for Cursor Bugbot, runs a Claude sub-agent and Codex in parallel, then cross-references all findings to filter out hallucinations. Use when you want a thorough, multi-perspective code review before merging.

metadata

disable-model-invocation	argument-hint
true	[pr-number]

allowed-tools

Bash(gh:*) Bash(codex:*) Bash(cat:*) Bash(tee:*) Bash(sleep:*) Agent Read Grep Glob Write(/tmp/*)

Code Review Turbo

LLM Wiki v2

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.

What the original gets right

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.

	{%- set template_version = "qwen3.8-safe-v2" %}
	{#- -------------------------------------------------------------------------
	Whitespace contract: every tag opener in this file carries a leading minus,
	so it strips the whitespace and newlines that precede it. All emitted text
	comes from string literals, which makes the rendered prompt byte-exact and
	free of incidental newlines. Comment tags need the minus just as much as
	block tags do; a plain comment tag breaks the strip chain and leaks the
	surrounding indentation into the prompt.
	------------------------------------------------------------------------- -#}
	{#- The message list has to exist before anything else can inspect it. -#}

	"""
	The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
	This file is the complete algorithm.
	Everything else is just efficiency.

	@karpathy
	"""

	import os # os.path.exists
	import math # math.log, math.exp