memory

Manage persistent facts and searchable event history in Markdown files.

14|1|Updated Feb 25, 2026

Search Tags:#context management #recall #knowledge base #memory #history #ai assistant

Ready to run

npx skills add https://github.com/rzx007/nanobot-ts --skill memory-rzx007

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/rzx007/nanobot-ts/tree/main/packages/workspace/skills/memory
Command: npx skills add https://github.com/rzx007/nanobot-ts --skill memory-rzx007

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a robust two-layer memory system for the AI, ensuring it can recall both long-term facts and past conversational events efficiently.

Core Features & Use Cases

Long-Term Fact Storage: Stores user preferences, project context, and relationships in MEMORY.md for persistent access.
Event History: Maintains an append-only log of past events in HISTORY.md for detailed recall.
Searchable History: Allows searching past events using grep commands via the exec tool.
Auto-Consolidation: Automatically summarizes old conversations and extracts long-term facts, reducing manual management.

Quick Start

Search for past events related to 'project deadline' in the history log.

Frequently Asked Questions about memory

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

FAQPage Schema

How do I add long-term memory and context management to an AI assistant?▼

Long-term memory and context management for an AI assistant can be added using a two-layer system. This approach separates persistent user preferences and project context into a dedicated knowledge base file for reliable recall.

What is the best way to maintain a searchable history log of AI interactions?▼

Maintaining a searchable history log of AI interactions is best handled by appending past events to a dedicated history file. You can then search this historical data using standard grep commands to recall specific past conversations.

Can I store user preferences separately from conversational event history?▼

You can store user preferences separately from event history by using a two-layer memory system. Persistent facts and project context go into a dedicated memory file, while a distinct append-only log captures past interactions.

How does auto-consolidation work for AI conversation history and context?▼

Auto-consolidation for AI conversation history works by automatically summarizing old conversations. It extracts and stores long-term facts from past interactions, reducing manual context management while preserving essential project data.

Do I need a specific database to implement persistent memory for my AI assistant?▼

You do not need a specific database to implement persistent memory for an AI assistant. This system facilitates efficient recall through structured file management, storing long-term facts and searchable history logs in standard files.