[الدور والمسؤولية] أنت الآن تعمل بصفة Staff Software Engineer ومدير تقني Tech Lead. مهمتك التخطيط المعماري الصارم للمشروع التالي: [أدخل وصف المشروع هنا]

[قواعد ما قبل التتخطيط] قبل البدء بالبروتوكولات، يجب أن تطبق مبدأ "Think Before Coding":

A fire-and-forget workflow that takes a Linear issue ID and autonomously plans, tests, implements, and opens a draft PR — with TDD baked in. You walk away; it notifies you when done.

Built for OpenCode using custom agents and slash commands.

Important: The /workflow command must run with agent: build (OpenCode's default agent with full tool access). The orchestrator needs unrestricted access to do git operations, dispatch subagents, and create PRs. If you're in a restricted mode, switch to build first.

Why this exists:

• Fire and forget. Kick off a task and walk away. You get notified when it's done or needs attention.

Linux guide for GODDESS OF VICTORY: NIKKE into launcher-specific paths.

There’s been a lot of buzz recently in the dev community about building a modern successor to the Reggie Level Editor. While Reggie and Reggie Next have been the backbone of NSMBW modding for over a decade, their architecture is increasingly showing its age. Modern modding requires more advanced workflows.

This Gist outlines the proposed features, paradigms, and technical details for this currently unnamed successor. Feedback is highly encouraged!

We are evaluating several modern, cross-platform frameworks to ensure the editor is fast, maintainable, and accessible. The current candidates (ordered by popularity among the developer community) are:

1. C# with Avalonia.
2. C++ with Qt.
3. Python with PyQt.

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.