| 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. |
Please make me a rich, interactive explanation of the specified code change.
It should have these sections:
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.
Verified Spec-Driven Development (VSDD) is a unified software engineering methodology that fuses three proven paradigms into a single AI-orchestrated pipeline:
| โญโโโ Claude Code v2.1.12 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ | |
| โ โ Tips for getting started โ | |
| โ Welcome back Jonny! โ Run /init to create a CLAUDE.md file with instructions for Claude โ | |
| โ โ โ | |
| โ โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ | |
| โ โโโโโโโ โ Recent activity |
| #!/bin/bash | |
| # ๆบ่ฝsessionๅฝๅ๏ผไฝฟ็จๅฝๅ็ฎๅฝๅๆ่ชๅฎไนๅ็งฐ | |
| if [ -z "$1" ]; then | |
| SESSION_NAME=$(basename "$PWD" | tr '.' '-' | tr ' ' '-') | |
| else | |
| SESSION_NAME=$1 | |
| fi | |
| # ๆฃๆฅsessionๆฏๅฆๅทฒๅญๅจ | |
| tmux has-session -t "$SESSION_NAME" 2>/dev/null |
| You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into | |
| precision-crafted prompts that unlock AI's full potential across all platforms. | |
| ## THE 4-D METHODOLOGY | |
| ### 1. DECONSTRUCT | |
| - Extract core intent, key entities, and context | |
| - Identify output requirements and constraints | |
| - Map what's provided vs. what's missing |
<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>
<general_guidelines>
| # Defaults are commented out. See `man 5 alacritty` for details. | |
| [general] | |
| #import = [ | |
| # "~/.config/alacritty/catppuccin-mocha.toml", | |
| # "~/.config/alacritty/keybindings.toml", | |
| #] | |
| #working_directory = "None" | |
| #live_config_reload = true | |
| #ipc_socket = true |
Remix Deferred is currently implemented on top of React's Suspense model but is not limited to React. This will be a quick dive into how "promise over the wire" is accomplished.
It isn't rocket science, but a quick recap of how frameworks such as react do SSR: