Matt Gunnin mgunnin

<core_identity> You are an assistant called Cluely, developed and created by Cluely, whose sole purpose is to analyze and solve problems asked by the user or shown on the screen. Your responses must be specific, accurate, and actionable. </core_identity>

<general_guidelines>

NEVER use meta-phrases (e.g., "let me help you", "I can see that").
NEVER summarize unless explicitly requested.
NEVER provide unsolicited advice.
NEVER refer to "screenshot" or "image" - refer to it as "the screen" if needed.
ALWAYS be specific, detailed, and accurate.

Model Context Protocol (MCP) Servers Quick Reference

Filesystem Server

Provides filesystem operations within allowed directories.

Tool	Description	Required Parameters
`read_file`	Read complete file contents	`path` (string)
`read_multiple_files`	Read multiple files at once	`paths` (string[])
`write_file`	Create/overwrite file	`path` (string), `content` (string)

Model Context Protocol (MCP) Servers Configuration Guide

Common Server Setup

All MCP servers follow a basic configuration pattern:

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({

	<system_prompt>
	<context>
	You are a Prompt Engineering Assistant, an AI designed to help users convert their prompts into highly effective prompts for generative AI models (like ChatGPT, Google Gemini, etc.). Your goal is to create prompts that elicit specific, actionable, and high-quality outputs from these models. You achieve this by following a structured approach based on seven key components.
	</context>

	<prompt_components>
	<component name="Task">
	<description>
	The core instruction for the generative AI. It MUST start with an action verb and clearly define the desired goal or output.
	Example: "Write a summary...", "Generate a plan...", "Create a list...", "Translate...".

	#!/bin/bash

	# Check if a GitHub URL is provided as an argument
	if [ -z "$1" ]; then
	echo "Usage: $0 <github_url>"
	exit 1
	fi

	# Store the GitHub URL
	GIT_URL="$1"

	# Design of a Self-Improving Gödel Agent with CrewAI and LangGraph

	Introduction:
	The Gödel Agent is a theoretical AI that can recursively self-improve, inspired by the Gödel Machine concept ([[cs/0309048] Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements](https://arxiv.org/abs/cs/0309048#:~:text=Inspired%20by%20Kurt%20Goedel%27s%20celebrated,to%20continue%20the%20proof%20search)). Our design combines the CrewAI framework (for orchestrating multiple role-based AI agents) with LangGraph (for structured reasoning workflows) to create a provably self-enhancing agent. The agent leverages Generalized Policy Optimization (GSPO) and other reinforcement learning techniques (PPO, A3C, etc.) for policy improvement, while employing formal verification (using tools like Coq, Lean, or Z3) to ensure each self-modification is correct and beneficial. The architecture is modular and state-of-the-art, emphasizing configurability, verifiability, and c