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Roo Code Workflow: An Advanced LLM-Powered Development Setup

This gist outlines a highly effective and cost-optimized workflow for software development using Roo Code, leveraging a multi-model approach. This setup has been successfully used to build working applications, such as Baccarat game simulations with betting strategy analysis, and my personal portfolio site.

Core Components & Model Allocation

The power of this setup lies in strategically assigning different Large Language Models (LLMs) to specialized "modes" within Roo Code, optimizing for performance, cost, and specific task requirements.

V8

V8 is Google’s open source high-performance JavaScript and WebAssembly engine, written in C++. It is used in Chrome and in Node.js, among others. It implements ECMAScript and WebAssembly, and runs on Windows 7 or later, macOS 10.12+, and Linux systems that use x64, IA-32, ARM, or MIPS processors. V8 can run standalone, or can be embedded into any C++ application.

SpiderMonkey

SpiderMonkey is Mozilla’s JavaScript and WebAssembly Engine, used in Firefox, Servo and various other projects. It is written in C++, Rust and JavaScript. You can embed it into C++ and Rust projects, and it can be run as a stand-alone shell. It can also be [compiled](https://bytecodealliance.org/articles/making-javascript-run-fast-on

Flutter Móvil - Instalaciones recomendadas

Descargar las hojas de atajos recomendadas:

Guías de atajos - Dart

C#编码规范

概述

规范制定原则

方便代码的交流和维护。
不影响编码的效率，不与大众习惯冲突。
使代码更美观、阅读更方便。
使代码的逻辑更清晰、更易于理解。

What is this?

It is a simple MD to force LLMs to help you build your own prompts following Anthropic'sinternal prompt engineering template Inspired by https://www.reddit.com/r/PromptEngineering/comments/1n08dpp/anthropic_just_revealed_their_internal_prompt/

How to use

Add this file to your proyect
Invoke this MD in the LLM context
Ask to model: "help me write a prompt following this process" + draft of your prompt

Then the model is goign to ask you the template questions to help you build the final prompt based on the template.

🤗Huggingface Model Downloader

Note

(2025-01-08) Add feature for 🏷️Tag(Revision) Selection, contributed by @Bamboo-D.
(2024-12-17) Add feature for ⚡Quick Startup and ⏭️Fast Resume, enabling skipping of downloaded files, while removing the git clone dependency to accelerate file list retrieval.

Considering the lack of multi-threaded download support in the official huggingface-cli, and the inadequate error handling in hf_transfer, This command-line tool leverages curl and aria2c for fast and robust downloading of models and datasets.

Features

⏯️ Resume from breakpoint: You can re-run it or Ctrl+C anytime.

	VMware vSphere 6 Enterprise Plus
	1C20K-4Z214-H84U1-T92EP-92838
	1A2JU-DEH12-48460-CT956-AC84D
	MC28R-4L006-484D1-VV8NK-C7R58
	5C6TK-4C39J-48E00-PH0XH-828Q4
	4A4X0-69HE3-M8548-6L1QK-1Y240

	VMware vSphere with Operations Management 6 Enterprise
	4Y2NU-4Z301-085C8-M18EP-2K8M8
	1Y48R-0EJEK-084R0-GK9XM-23R52

	# Technical Plan Template — Use For New Milestones/Initiatives

	Status: Draft \| Proposed \| Accepted \| Superseded

	Doc Version: 0.2
	Owner: <name/team>
	Reviewers: <names>
	Date: <YYYY-MM-DD>

	> LLM Authoring Instructions

	function toBaseN(num, base) {
	if (num === 0) {
	return '0';
	}
	var digits = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ';
	var len = Math.min(digits.length, base);
	var result = '';
	while (num > 0) {
	result = digits[num % len] + result;
	num = parseInt(num / len, 10);

	public static Code GetLambdaCode(string zipPath)
	{
	return Code.FromAsset(zipPath, new AssetOptions
	{
	AssetHash = HashForZipContent(zipPath),
	AssetHashType = Amazon.CDK.AssetHashType.CUSTOM
	});
	}