mcp-memory-keeper

🥈Silver

MCP Memory Keeper is a server for managing persistent context in AI coding assistants. It helps developers maintain context across sessions, improving efficiency and accuracy. Integrates with Claude AI and uses SQLite for data storage.

96180Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/mkreyman/mcp-memory-keeper.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/mkreyman/mcp-memory-keeper

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Create a persistent memory context for my AI coding assistant named [ASSISTANT_NAME]. The context should include [NUMBER] key pieces of information about [PROJECT_NAME], such as its purpose, key dependencies, and any specific coding standards we're following. Ensure the memory is structured to help the assistant provide more accurate and relevant suggestions over time.

Example Output

I've created a persistent memory context for your AI coding assistant named 'CodeBuddy'. This context includes 5 key pieces of information about the 'EcoTrack' project, such as its purpose (a carbon footprint tracking application), key dependencies (React, Node.js, MongoDB), and specific coding standards (ESLint with Airbnb style guide). The memory is structured to help CodeBuddy provide more accurate and relevant suggestions over time. For example, when you ask CodeBuddy about a new feature, it will automatically consider the project's existing architecture and coding standards. It will also remember any specific preferences you've mentioned, like your preference for functional components over class components in React.