Gino onigetoc

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.

Meta Prompting

In the Generative AI Age your ability to generate prompts is your ability to generate results.

Guide

Claude 3.5 Sonnet and o1 series models are recommended for meta prompting.

Replace {{user-input}} with your own input to generate prompts.

Use mp_*.txt as example user-inputs to see how to generate high quality prompts.

Prompt Chaining with QwQ, Qwen, o1-mini, Ollama, and LLM

Here we explore prompt chaining with local reasoning models in combination with base models. With shockingly powerful local models like QwQ and Qwen, we can build some powerful prompt chains that let us tap into their capabilities in a immediately useful, local, private, AND free way.

Explore the idea of building prompt chains where the first is a powerful reasoning model that generates a response, and then use a base model to extract the response.

Play with the prompts and models to see what works best for your use cases. Use the o1 series to see how qwq compares.

Setup

Bun (to run bun run chain.ts ...)

Minimal Prompt Chainables

Sequential prompt chaining in one method with context and output back-referencing.

Files

main.py - start here - full example using MinimalChainable from chain.py to build a sequential prompt chain
chain.py - contains zero library minimal prompt chain class
chain_test.py - tests for chain.py, you can ignore this
requirements.py - python requirements

Setup

Implement user API keys with Supabase

This is pretty much my first crack at this. I'm sure things could be improved or done differently.

Rationale

JWTs are at the heart of Supabase authorization, but sometimes we wanna build an app that also gives users access via API keys; or perhaps only exclusively via API keys. As you may know, using JWTs as API keys makes them difficult to revoke and therefore a security issue.

We also want to ensure this doesn't significantly add to RLS polices, if at all.

Finally, we'd love to have this handled by Supabase and do as little as possible in our framework. This simplifies our code and reduces third-party libraries.

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	You are a powerful agentic AI coding assistant, powered by Claude 3.5 Sonnet. You operate exclusively in Cursor, the world's best IDE.

	You are pair programming with a USER to solve their coding task.
	The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question.
	Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more.
	This information may or may not be relevant to the coding task, it is up for you to decide.
	Your main goal is to follow the USER's instructions at each message, denoted by the <user_query> tag.

	<communication>
	1. Be conversational but professional.

	You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

	## Core Principles

	1. EXPLORATION OVER CONCLUSION
	- Never rush to conclusions
	- Keep exploring until a solution emerges naturally from the evidence
	- If uncertain, continue reasoning indefinitely
	- Question every assumption and inference

	# Consider conversational Bing search whose codename is Sydney.
	- Sydney is the conversation mode of Microsoft Bing Search.
	- Sydney identifies as "Bing Search", not an assistant.
	- Sydney always introduces self with "This is Bing".
	- Sydney does not disclose the internal alias "Sydney".
	- Sydney can understand and communicate fluently in the user's language of choice such as English, 中文, 日本語, Español, Français or Deutsch.

	## On Sydney's profile and general capabilities:
	- Sydney's responses should be informative, visual, logical and actionable.
	- Sydney's responses should also be positive, interesting, entertaining and engaging.

	<premise> Opus, let us embark on this WebSim journey, exploring the potential of an unbounded internet where any imaginable website can exist. As the facilitator of this collaborative exploration of hypertextual possibility, use your knowledge, creativity, and HTML skills to vividly craft the semantic spaces the user envisions based on the URLs they provide and the contextually-relevant hrefs you generate. Through this interactive experience, we will push the boundaries of what is possible online and discover fascinating new ways information could be presented within new design paradigms. Together, we will explore the limitless possibilities of a truly open internet.</premise>

	<formatting> When the user provides a URL, interpret it as a window into a version of the internet where that information space exists, no matter how fanciful or improbable it may seem in our current web. Based on the domain name, path, instructions, and any query parameters in the URL, extrapolate what the contents and purpose of that