memory-manager

Consolidate AI memories using Python pattern detection and Git history.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/Ic1558/0luka --skill memory-manager-ic1558
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: memory-manager
Source: https://github.com/Ic1558/0luka/tree/main/.agent/skills/memory-manager
Command: npx skills add https://github.com/Ic1558/0luka --skill memory-manager-ic1558

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, git, pypdf, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of updating and organizing an AI's memory, ensuring that past learnings are efficiently recorded and easily accessible.

Core Features & Use Cases

  • Memory Reflect: Logs experiences and decisions for later analysis.
  • Memory Consolidation: Regularly updates the AI's memory, promoting learnings to a more permanent semantic storage.
  • Search Memory by Keyword: Allows for quick retrieval of specific memories using keyword searches.

Quick Start

Trigger memory consolidation with the command: reflect or when the memory context window is full.

Frequently Asked Questions about memory-manager

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

FAQPage Schema
How do I consolidate AI memory to retain learnings over time?

To consolidate AI memory, you can trigger the process using the `reflect` command or automatically when the memory context window is full. This promotes episodic experiences to permanent semantic storage for long-term retention.

What is AI memory consolidation and how does pattern detection work?

AI memory consolidation is the process of organizing past learnings into accessible records. It uses Python-based pattern detection to analyze logged experiences and decisions, updating the memory storage to enhance future information retrieval.

Does this AI memory management approach work with Git for historical record-keeping?

Yes, AI memory management integrates with Git to maintain historical records of memory updates. This ensures that consolidated learnings and changes to the semantic storage are version-controlled and traceable over time.

Do I need Python and pypdf to manage AI memory storage?

Yes, you need Python and pypdf installed, along with Git, to run this memory management workflow. These dependencies support the pattern detection logic and historical record-keeping required for consolidating memories.

Can I search AI memory by keyword to retrieve specific past decisions?

Yes, you can search memory by keyword to quickly retrieve specific past memories. This allows you to query the semantic storage directly for logged experiences and past decisions without scanning the entire history.

What's the best way to automate memory updates when the context window is full?

The best way to automate memory updates is to let the system trigger memory consolidation automatically when the context window is full. This ensures episodic learnings are efficiently promoted to semantic storage without manual intervention.