Memory

Create categorized long-term memory storage with per-category INDEX.md files.

1|Updated Feb 26, 2026
One-click install
npx skills add https://github.com/zhanbei1/OpenOcta --skill memory-zhanbei1
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Memory
Source: https://github.com/zhanbei1/OpenOcta/tree/main/src/skills/memory
Command: npx skills add https://github.com/zhanbei1/OpenOcta --skill memory-zhanbei1

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Infinite organizational memory is needed to extend the built-in agent memory with scalable, categorized storage, preventing memory fragmentation and data loss over time.

Core Features & Use Cases

  • Infinite categorized storage under ~/memory/ that runs in parallel with built-in memory.
  • Category-based indexing with per-category INDEX.md and optional syncing from built-in memory.
  • Use cases include long-term project histories, knowledge bases, and decision trails that expand without bound.

Quick Start

Create ~/memory/ with your chosen categories and add your first memory item to verify the system is live.

Frequently Asked Questions about Memory

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

FAQPage Schema
How do I organize long-term AI agent memory to prevent data fragmentation?▼

Organize long-term AI agent memory by creating a root ~/memory structure with infinite categorized storage. This prevents memory fragmentation and data loss by running parallel to built-in memory.

How do I create an index for categorized AI memory?▼

Create an index for categorized AI memory by adding per-category INDEX.md files within your ~/memory directory. This category-based indexing structures project histories and knowledge bases effectively.

Can I sync built-in agent memory with a separate long-term storage layer?▼

Yes, you can optionally sync built-in agent memory with your categorized long-term storage. This synchronization expands decision trails and knowledge bases without bound while maintaining separation.

What is the best way to store expanding project histories for AI agents?▼

The best way to store expanding project histories is using an infinite categorized memory layer under ~/memory/. This approach scales structured decision trails and histories beyond built-in agent limits.

Do I need a specific framework to set up scalable AI memory?▼

No specific framework is needed to set up scalable AI memory. You simply create the ~/memory directory with your chosen categories and add your first memory item to verify the system is live.