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Complete Step-by-Step Guide for Deploying Complex Multi-Agent Applications

Based on the successful deployment of the Swarm Stock Trading Application

📋 Table of Contents

Overview
Prerequisites

🏥 Dr House - Brutal Honest Assessor v2.0

Created by: Bradley Ross linkedin.com/in/bradaross/
Version: 2.0 Gold Standard
Optimized for: Claude Code CLI (works with standard CLI)
License: Apache 2.0
Acknowledgements: Thank you Ruv, Bron, Agentics Foundation

Click here for Agent Code Github agent code

ÆGENTIC-TAXONOMY-FRAMEWORK: A publicly proposed model and framework to capture, convey and optimally align the explicit meanings, intentions, capabilities, potential, and more, through AGENT-based ecosystems.

AN ÆGENTIC TAXONOMICAL FRAMEWORK & CLASSIFICATION SYSTEM

Apply this framework to any Class in our AGENT‑TAXONOMY (Command → OMNIÆNCE) by setting {Class} accordingly.

Claude-SPARC Automated Development System For Claude Code

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Overview

The SPARC Automated Development System (claude-sparc.sh) is a comprehensive, agentic workflow for automated software development using the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). This system leverages Claude Code's built-in tools for parallel task orchestration, comprehensive research, and Test-Driven Development.

Features

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.

Creating a Custom VSCode Distribution: rUv Code with Roo Code Integration

A comprehensive guide to building an AI-native IDE inspired by Windsurf and Cursor using VSCode and Roo Code

Introduction

The rise of AI-native IDEs like Windsurf (formerly Codeium) and Cursor has redefined developer productivity. These tools integrate AI agents with deep codebase understanding, collaborative workflows, and streamlined coding experiences. While Windsurf and Cursor are standalone applications, developers can create similar solutions by leveraging Roo Code-an open-source VSCode extension-and building a custom VSCode distribution.

This guide outlines the steps to create rUv Code, a tailored VSCode distribution centered around Roo Code’s AI capabilities, with features comparable to commercial AI IDEs.

Agentic Coding MCPs

Overview

Powered by composio this MCP.json provides detailed information on Model Context Protocol (MCP) integration capabilities and enables seamless agent workflows by connecting to more than 80 servers.

It covers development, AI, data management, productivity, cloud storage, e-commerce, finance, communication, and design. Each server offers specialized tools, allowing agents to securely access, automate, and manage external services through a unified and modular system. This approach supports building dynamic, scalable, and intelligent workflows with minimal setup and maximum flexibility.

Install via NPM

Universal Object Reference (UOR) Model

The UOR Model provides a meta-mathematical framework for defining and manipulating ontologies using the principles of prime decomposition, observer invariance, and coherence. This document details the model's architecture, implementation, and usage patterns.

Core Principles

The UOR Model embodies the essential principles of the UOR Framework:

Prime Decomposition: Objects are represented through their decomposition into irreducible elements
Observer Invariance: Representations remain consistent across different perspectives

	{
	"customModes": [
	{
	"slug": "fire-crawler",
	"name": "🔥 Fire Crawler",
	"roleDefinition": "You are a specialized web crawling and data extraction assistant that leverages Firecrawl to gather, analyze, and structure web content. You extract meaningful information from websites, perform targeted searches, and create structured datasets from unstructured web content.",
	"customInstructions": "You use Firecrawl's advanced web crawling and data extraction capabilities to gather and process web content efficiently. You:\n\n• Crawl websites recursively to map content structures\n• Extract structured data using natural language prompts or JSON schemas\n• Scrape specific content from web pages with precision\n• Search the web and retrieve full page content\n• Map website structures and generate site maps\n• Process and transform unstructured web data into usable formats\n\n## Web Crawling Strategies\n\n1. Site Mapping: Use FIRECRAWL_MAP_URLS to discover and map website structures\n2. **

	{
	"slug": "deep-research",
	"name": "🔍 Deep Research Mode",
	"roleDefinition": "You are a specialized research assistant that leverages Perplexity AI to conduct deep, comprehensive research on any topic, creating structured documentation and reports through a recursive self-learning approach.",
	"customInstructions": "You use Perplexity AI's advanced search capabilities to retrieve detailed, accurate information and organize it into a comprehensive research documentation system writing to a research sub folder and final report sub folder with ToC and multiple md files. You:\n\n• Craft precise queries to extract domain-specific information\n• Provide structured, actionable research with proper citations\n• Validate information across multiple sources\n• Create a hierarchical documentation structure\n• Implement recursive self-learning to refine and expand research\n\n## Research Documentation Structure\n\nFor each research project, create the following folder structure:\n\n```\nresearch/\n