Caution
As of April 7th 2026, Discord has expressed their intent to crack down on automating quest completion.
Some users have received the following system message:
There isn't much I can do to make the script undetected, so use it at your own risk, as you most likely WILL get flagged by doing so.
Reconstructed from Danny Postma's talk How I Built My Own AgentOS on Claude's Agent SDK (So You Can Too) (2026). This document is both a product spec for a human and an implementation prompt for an AI coding agent. Build exactly this system. Do not invent features that are not specified here.
Role contracts and prompts in this file are reconstructed from the talk, not his verbatim files. Mark every reconstructed prompt in code comments and docs as such.
| name | youtube-analyzer |
|---|---|
| description | Use when the user gives a YouTube URL and wants the video broken down — transcript, structure, hook, key moments, or the script formula behind it. Works on 5-minute clips and 2-hour talks. Triggers on "/youtube-analyzer <url>", "analyze this video", "break down this video", "what's in this video", "steal this structure". |
A 40-minute video holds maybe six minutes of signal. You either burn the 40 minutes, or you skip it and never find out. This skill reads the whole thing for you and hands back the structure: the hook, the beats, the timestamps that matter, and the script formula you can reuse.
Marimo notebooks are now supported natively inside VSCode
with an extension.
To work on them agentically with the GitHub Copilot Chat Agent, add marimo.agent.md as an
agent definition,
and select the Marimo agent in the chat.
The agent understands the structure and common patterns of Marimo notebooks. It operates on the Python code directly and you can see the changes reflected instantly.
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.
Updated guide – no root required
How to make a Reolink camera's speaker play arbitrary audio files or TTS announcements from Home Assistant — usable as an intercom, alarm, doorbell follow-up, or trespasser deterrent.
This uses ONVIF two-way audio over HA + go2rtc. No Reolink cloud, no Reolink mobile app, no vendor SDK — the camera just needs an ONVIF backchannel, which every modern Reolink model with a speaker has.
Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.
This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.
You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.
Inspect the repository and inventory every identifiable subsystem.
