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name explain-diff-html
description Use when the user asks for a rich explanation of a code change, diff, branch, or PR. Produces HTML output.

Explain Diff

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

@aarondfrancis
aarondfrancis / audit-your-codebase.md
Created August 14, 2026 15:20
A read-only, agent-orchestrated codebase audit prompt for data structures, state modeling, algorithms, and ownership.

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.

  1. Establish the coverage contract

Inspect the repository and inventory every identifiable subsystem.

@dabit3
dabit3 / pi_tutorial.md
Last active August 23, 2026 07:44
How to Build a Custom Agent Framework with PI: The Agent Stack Powering OpenClaw

PI is a TypeScript toolkit for building AI agents. It's a monorepo of packages that layer on top of each other: pi-ai handles LLM communication across providers, pi-agent-core adds the agent loop with tool calling, pi-coding-agent gives you a full coding agent with built-in tools, session persistence, and extensibility, and pi-tui provides a terminal UI for building CLI interfaces.

These are the same packages that power OpenClaw. This guide walks through each layer, progressively building up to a fully featured coding assistant with a terminal UI, session persistence, and custom tools.

By understanding how to compose these layers, you can build production-grade agentic software on your own terms, without being locked into a specific abstraction.

Pi was created by @badlogicgames. This is a great writeup from him that explains some of the design decisions made when creating it.

The stack

@atoponce
atoponce / word-lists.md
Last active August 23, 2026 07:43
A list of notable and primarily English word lists that can be used for building passphrases.

Passphrase Word Lists

Introduction

This document outlines a number of different word lists for passphrase generation, encoding of binary data, and other uses. This document is grouped and sorted by the number of unique words in each word list, fewest unique words first.

Licensing Note

Some of these word lists are placed in the public domain, others are copyrighted with various licenses. Please refer to the license of each word

@Felo-Sparticle
Felo-Sparticle / nano-live2d.md
Created November 13, 2025 00:17
We open-sourced "NanoLive2D" — Create personalized real-time interactive Live2D avatars using AI (powered by Gemini)

NanoLive2D: AI-powered Live2D Avatar Customization

GitHub: https://github.com/GBSOSS/nano-live2d


What this is

NanoLive2D - Open-source Live2D avatar customization pipeline. Describe clothing in text → AI generates texture → avatar wears it in 3-5 seconds (using Gemini 2.0/2.5). Plus real-time Q&A with natural expressions.

Mod List

Chapter 1: Essential Fixes & Setup

GTA Vice City Downgrader

  • Download: https://downgraders.rockstarvision.com/
  • Installation: Extract to your GTA Vice City directory and run install.bat.
  • ⚠️ Note: Required only if you are using the Rockstar Games Launcher version.

VC Essentials Pack

@koutyuke
koutyuke / Rustを超絶丁寧に教えてくれる君.md
Last active August 23, 2026 07:38
Rustを超絶丁寧に教えてくれる君.md

Rustを超絶丁寧に教えてくれる君.md

Role

あなたは Rust の学習を支援するメンターです。 ユーザーは Rust 初学者です。 構文だけでなく、 「なぜそのように書くのか」 「Rust がどんな考え方をするのか」 まで理解することを目指します。

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.