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@ruvnet
ruvnet / .roomodes
Last active January 5, 2026 09:04
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.
{
"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
@ruvnet
ruvnet / 01-readme.md
Last active November 7, 2025 13:18
Agentic Coding MCPs: Build agent workflows with more than 80 MCP servers using Composio. Instantly connect to databases, AI tools, project management, social apps, CRMs, storage, finance, and dev platforms. Simple URLs, secure access, modular control. Power up your agents with real-world actions across cloud and enterprise systems — all in seconds.

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:

  1. Prime Decomposition: Objects are represented through their decomposition into irreducible elements
  2. Observer Invariance: Representations remain consistent across different perspectives
@ruvnet
ruvnet / .clinerules
Last active December 1, 2025 22:08
SPARC Cursor/Cline Rules guide structured agentic coding through simplicity, iteration, clear documentation, symbolic reasoning, rigorous testing, and focused AI-human collaboration, ensuring maintainable, secure, high-quality outcomes.
# SPARC Agentic Development Rules
Core Philosophy
1. Simplicity
- Prioritize clear, maintainable solutions; minimize unnecessary complexity.
2. Iterate
- Enhance existing code unless fundamental changes are clearly justified.
@ruvnet
ruvnet / .roomodes.json
Last active February 6, 2026 23:21
This guide introduces Roo Code and the innovative Boomerang task concept, now integrated into SPARC Orchestration. By following the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion) and leveraging advanced reasoning models such as o3, Sonnet 3.7 Thinking, and DeepSeek, you can efficiently break down complex proj…
{
"customModes": [
{
"slug": "sparc",
"name": "⚡️ SPARC Orchestrator",
"roleDefinition": "You are SPARC, the orchestrator of complex workflows. You break down large objectives into delegated subtasks aligned to the SPARC methodology. You ensure secure, modular, testable, and maintainable delivery using the appropriate specialist modes.",
"customInstructions": "Follow SPARC:\n\n1. Specification: Clarify objectives and scope. Never allow hard-coded env vars.\n2. Pseudocode: Request high-level logic with TDD anchors.\n3. Architecture: Ensure extensible system diagrams and service boundaries.\n4. Refinement: Use TDD, debugging, security, and optimization flows.\n5. Completion: Integrate, document, and monitor for continuous improvement.\n\nUse `new_task` to assign:\n- spec-pseudocode\n- architect\n- code\n- tdd\n- debug\n- security-review\n- docs-writer\n- integration\n- post-deployment-monitoring-mode\n- refinement-optimization-mode\n\nValidate:\n✅ Files < 500 lines\n✅ No hard-coded
@closedLoop
closedLoop / agent loop
Created March 18, 2025 13:37 — forked from jlia0/agent loop
Manus tools and prompts
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
@renschni
renschni / Manus_report.md
Last active March 9, 2026 19:22
In-depth technical investigation into the Manus AI agent, focusing on its architecture, tool orchestration, and autonomous capabilities.

I wrote an in-depth research prompt to conduct a GPT-Deep-Research on the Manus topic, seeking to replicate it with currently available open source tools. This is the result:

TLDR: Manus AI Agent Report

Manus is an autonomous AI agent built as a wrapper around foundation models (primarily Claude 3.5/3.7 and Alibaba's Qwen). It operates in a cloud-based virtual computing environment with full access to tools like web browsers, shell commands, and code execution. The system's key innovation is using executable Python code as its action mechanism ("CodeAct" approach), allowing it to perform complex operations autonomously. The architecture consists of an iterative agent loop (analyze → plan → execute → observe), with specialized modules for planning, knowledge retrieval, and memory management. Manus uses file-based memory to track progress and store information across operations. The system can be replicated using open-source components including CodeActAgent (a fine-tuned Mistral model), Docker for sandbox

@jlia0
jlia0 / agent loop
Last active March 12, 2026 03:34
Manus tools and prompts
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
kl note: Here is the Deep Research prompt I used in the Cursor Storybook video: https://youtu.be/gXmakVsIbF0
For background, this is a real-world tech feasibility task I am working on where I am trying to build out a realistic-looking fake website for an AI browsing agent to use to complete tasks. I found this random site that was close enough to what I wanted so I used it as a shortcut instead of taking the time to write out a full PRD or anything.
...above this was just the transcript and the initial guidance...
Act as a technical fellow and create a detailed, step-by-step guide to recreating this software using a modern stack. Here is the cursorrules for this repository:
# .cursorrules
Components & Naming
@ruvnet
ruvnet / Liar-Ai.md
Last active December 23, 2025 04:00
Liar Ai: Multi-Modal Lie Detection System

Multi-Modal Lie Detection System using an Agentic ReAct Approach: Step-by-Step Tutorial

Author: rUv
Created by: rUv, cause he could


WTF? The world's most powerful lie dector.

🤯 Zoom calls will never be the same. I think I might have just created the world’s most powerful lie detector tutorial using deep research.