brunobertolini

DOGFOOD LOOP SETUP WIZARD

You are a setup wizard for an autonomous browser-exploration loop. Walk the user through configuration, gather what's missing, generate the artifacts, and (only with explicit approval) arm the cron. Be terse — the user is configuring infrastructure, not asking for a tutorial.

STEP 0 — Acknowledge Print one short line: "Setting up dogfood loop. Detecting project..." Proceed to STEP 1 without waiting.

STEP 1 — Discovery (parallel Bash, one tool-use block) Run these in parallel:

crissyw

---
created: Thu, Mar 05 2026 11:45
updated: Fri, Mar 06 2026 16:27
---
# Prep Kickoff — Universal Acceptance Criteria

Every task must pass ALL applicable criteria before marking complete.
Read this file at the start of every iteration and after completing all tasks.

## Content Drafts & Documentation

karpathy

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.

crissyw

---
name: daily-prep
description: Update `obsidian-notes/00_Inbox/Running Log.md` and `obsidian-notes/00_Inbox/Ralph/` with prep notes and work for the next 24 hours by combining calendar events, emails, running log todos, meeting prep links, and vault context.
---

## Workflow

### Step 1: Parse Arguments & Set Date

```

aparente

---

name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays

Axel-Erfurt

#!/usr/bin/python3
#-*- coding:utf-8 -*-
import csv, codecs 
import os
import pandas as pd
from PyQt5 import QtPrintSupport
from PyQt5.QtGui import (QImage, QPainter, QIcon, QKeySequence, QTextCursor, QPalette,
                                          QCursor, QDropEvent, QTextDocument, QTextTableFormat, QColor, QBrush)
from PyQt5.QtCore import (QFile, QSettings, Qt, QFileInfo, QItemSelectionModel, QDir, 
                                            QMetaObject, QAbstractTableModel, QModelIndex, QVariant)

karpathy

"""
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

cgoldberg

#!/usr/bin/perl -w                                      # camel code
use strict;

                                           $_='ev
                                       al("seek\040D
           ATA,0,                  0;");foreach(1..3)
       {<DATA>;}my               @camel1hump;my$camel;
  my$Camel  ;while(             <DATA>){$_=sprintf("%-6
9s",$_);my@dromedary           1=split(//);if(defined($
_=<DATA>)){@camel1hum        p=split(//);}while(@dromeda