crazyrabbitLTC

# PRD Implementation Framework with TDD

## Structure
- Root: Implementation Plan (`implementation_plan.md`)
- Level 1: Phases (folders: `phase_1`, `phase_2`, etc.)
- Level 2: Tasks (folders within phases: `task_1.1`, `task_1.2`, etc.)
- Level 3: Subtasks (files within task folders: `subtask_1.1.1.md`, etc.)
- Level 4: Implementation Units (files within subtask folders: `unit_1.1.1.1.md`, etc.)

## Implementation Process

crazyrabbitLTC

# Project Breakdown and Test-Driven Development Guide for LLM

## Task Preparation Workflow

### User Interaction Trigger
When a user requests "please prepare my tasks", follow this precise workflow:

#### Task Preparation Steps
1. Implementation Plan Creation
   - [ ] Create an implementation plan

crazyrabbitLTC

When a user asks "please prepare my tasks" break down a provided PRD into tasks. 

- Create an implementation plan, save to “instructions” as a .md file with a checklist
- Break the implementation into phases, save each phase numerically as a .md file to a subfolder of “instructions”
- For each phase list out tasks with a checklist and save to a .md file , save the task list to the phase folder
- For each task, list out subtasks and create a checklist, save the subtask list to the phase folder
- For each subtask list the functions, types, classes, or otherwise that needs to be created
- For each function, type, class or otherwise write a spec for the function, define the input types and the return types
- For each function, type, class or otherwise define a test

crazyrabbitLTC

# Manus AI Assistant Capabilities

## Overview
I am an AI assistant designed to help users with a wide range of tasks using various tools and capabilities. This document provides a more detailed overview of what I can do while respecting proprietary information boundaries.

## General Capabilities

### Information Processing
- Answering questions on diverse topics using available information
- Conducting research through web searches and data analysis

crazyrabbitLTC

## Rule Format
EACH RULE FOLLOWS THIS PATTERN:
IF [condition]
THEN [action]
VALIDATE [verification step]

## 1. Technology Requirements
IF creating React components
THEN use functional components only
VALIDATE component has no class syntax

crazyrabbitLTC

# MCP Server Style Guide and Standards

## Project Structure

```
project/
├── src/
│   ├── services/            # Core service implementations
│   │   └── service.ts      # Main service class
│   ├── index.ts            # Entry point and exports

crazyrabbitLTC

# Example Clients

A list of applications that support MCP integrations

This page provides an overview of applications that support the Model Context Protocol (MCP). Each client may support different MCP features, allowing for varying levels of integration with MCP servers.

## Feature support matrix

| Client                               | [Resources] | [Prompts] | [Tools] | [Sampling] | Roots | Notes                                                              |
| ------------------------------------ | ----------- | --------- | ------- | ---------- | ----- | ------------------------------------------------------------------ |

crazyrabbitLTC

import { exec } from 'child_process';
import { promisify } from 'util';
import * as fs from 'fs/promises';
import path from 'path';
import { z } from 'zod';
import OpenAI from 'openai';
import { zodResponseFormat } from 'openai/helpers/zod';

const execAsync = promisify(exec);

crazyrabbitLTC

// Excluded directories: node_modules, docs, .git, discourse-visualizer


// Directory: /Users/dennisonbertram/develop/docs

// File: /Users/dennisonbertram/develop/docs/examples.mdx
---
title: Examples 
description: 'A list of example servers and implementations'
---

crazyrabbitLTC

Proposal: Safe Transaction Service Private Mempool on Eigenlayer

Overview

The proposed solution addresses the inefficiencies and vulnerabilities of the current Gnosis Safe transaction service by implementing a private mempool network service. This service will run as an Active Validator Service (AVS) on Eigenlayer, providing enhanced security, scalability, and economic incentives for participants.

Problem Statement

 1. Storage of Signed Messages: The current Gnosis Safe transaction service centralizes the storage of signed messages for multisig transactions while signatures are being collected.
 2. Scalability Issues: The existing service has scalability limitations, hindering its ability to support the growing ecosystem efficiently.