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lsg1103275794
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project-memory

Manage persistent project memory via MemOS MCP server tool calls.

5|Updated Jan 25, 2026
Search Tags:#project management#knowledge management#mcp#memory#persistence#ai assistant
One-click install
npx skills add https://github.com/lsg1103275794/MemOSLocal-SM --skill project-memory-lsg1103275794
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: project-memory
Source: https://github.com/lsg1103275794/MemOSLocal-SM/tree/main/project-memory
Command: npx skills add https://github.com/lsg1103275794/MemOSLocal-SM --skill project-memory-lsg1103275794

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill transforms AI assistants from stateless chatbots into persistent project partners by providing them with a robust, context-aware memory system that learns from project history, code patterns, and decisions.

Core Features & Use Cases

  • Persistent Project Memory: Ensures AI remembers project context, past mistakes, and solutions across sessions.
  • Smart Triggers: Automatically searches history, suggests code patterns, and warns about past errors.
  • Use Case: When you encounter a recurring error, the AI instantly recalls the solution from a previous ERROR_PATTERN memory, saving you debugging time.

Quick Start

Use the project memory skill to search for past solutions to the 'ModuleNotFoundError'.

Frequently Asked Questions about project-memory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I give my AI assistant persistent project memory across sessions?▼

To give an AI assistant persistent project memory, you use the MemOS MCP server to save and recall context, enabling the AI to remember past decisions, code patterns, and errors across different sessions.

How does an AI learn from past error patterns during debugging?▼

An AI learns from past error patterns by saving them as ERROR_PATTERN memory entries via direct MCP tool invocation, allowing it to instantly recall and suggest previous solutions when a recurring error is encountered.

What is the best way to track project decisions using an MCP server?▼

The best way to track project decisions using an MCP server is by invoking memory tools to save decision context, ensuring the AI can search and graph this historical information for future project management tasks.

Do I need to specify a project path when saving context to MCP memory?▼

Yes, you need to specify a project_path parameter when saving context to MCP memory to ensure correct cube routing, which isolates and organizes the persistent memory data for your specific project.

Can I use MCP memory tools to search for past code solutions?▼

Yes, you can use MCP memory tools to search for past code solutions by querying the persistent memory history, allowing the AI to automatically suggest relevant patterns and warn about previous mistakes.

Why does my AI forget project context after restarting the session?▼

An AI forgets project context after restarting because it is inherently stateless; implementing a persistent memory system via the MemOS MCP server transforms it into a context-aware partner that retains history.

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