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MICHAEL-BODO
MICHAEL-BODOCOMMUNITY
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agent-memory

Store, organize, and recall agent information across conversations.

2|Updated Feb 12, 2026
Search Tags:#recall#knowledge base#memory#organization#persistence#notes
One-click install
npx skills add https://github.com/MICHAEL-BODO/MigrationBox-v4 --skill agent-memory-michael-bodo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-memory
Source: https://github.com/MICHAEL-BODO/MigrationBox-v4/tree/main/.agent/skills/agent-memory
Command: npx skills add https://github.com/MICHAEL-BODO/MigrationBox-v4 --skill agent-memory-michael-bodo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a persistent memory space to store, organize, and recall important information, preventing knowledge loss across conversations and aiding in future tasks.

Core Features & Use Cases

  • Persistent Storage: Saves information that survives conversation turns.
  • Organization: Allows structuring memories into categories for easy retrieval.
  • Recall: Enables searching and retrieving past information based on summaries, tags, or full text.
  • Use Case: You've just solved a complex bug. Save the solution, including the steps taken and the root cause, as a memory. Later, when a similar issue arises, you can quickly recall the fix, saving significant debugging time.

Quick Start

Use the agent-memory skill to save the following information about the recent performance optimization.

Frequently Asked Questions about agent-memory

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

FAQPage Schema
How do I persist information across conversations for an AI agent?▼

To persist information across conversations for an AI agent, you use a persistent memory store that saves data to the file system using categorized directories and YAML frontmatter. This approach prevents knowledge loss between sessions.

How does an AI agent recall past notes and knowledge from a file system?▼

An AI agent recalls past notes by using ripgrep to efficiently search the persistent memory store. It retrieves stored information based on summaries, tags, or full text found within the structured YAML frontmatter and directory contents.

What is the best way to organize a persistent knowledge base for an AI assistant?▼

The best way to organize a persistent knowledge base is by structuring memories into categorized directories and using YAML frontmatter for metadata. This organization allows efficient searching and easy retrieval of past information.

Do I need a specific file system setup to save memories for an AI agent?▼

Yes, saving memories for an AI agent requires a file system for storage and adherence to specific frontmatter and organizational guidelines. This setup enables structured storage in categorized directories with YAML metadata.

Can I store debugging solutions as memories to save time on future similar issues?▼

Yes, you can store debugging solutions as memories by saving the steps taken and root cause. When a similar issue arises later, you can quickly recall the fix from the persistent memory store, saving significant debugging time.

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