conversation-memory

Name: conversation-memory
Availability: InStock
Author: jokken79

Manage and retrieve tiered conversation memories across AI assistant sessions.

1|Updated Dec 15, 2025

Search Tags:#context #llm #retrieval #memory #prompting #memory-persistence #entity-memory

Ready to run

npx skills add https://github.com/jokken79/YuKyuDATA-app1.0v --skill conversation-memory-jokken79

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: conversation-memory
Source: https://github.com/jokken79/YuKyuDATA-app1.0v/tree/main/.agent/skills/conversation-memory
Command: npx skills add https://github.com/jokken79/YuKyuDATA-app1.0v --skill conversation-memory-jokken79

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory support memory persistence across sessions and users. Use when memory of past interactions influences current responses, such as long chats or multi-turn assistant tasks.

Core Features & Use Cases

tiered memory system (short-term, long-term, entity-memory)
memory-persistence, memory-retrieval, memory-consolidation
memory-aware prompting and tiered retrieval workflows
use cases include long-running chats, multi-session support, and context-aware assistants

Quick Start

Initiate a memory-enabled session and prompt the assistant to recall a remembered detail from a prior chat.

Frequently Asked Questions about conversation-memory

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

FAQPage Schema

How do I persist context across LLM conversations for long-running chats?▼

To persist context across LLM conversations, use a tiered memory system that manages short-term, long-term, and entity memory. This approach stores and retrieves past interactions, allowing your assistant to recall details from prior sessions.

What is entity memory and when do I need it for AI assistants?▼

Entity memory tracks specific facts about users or objects within AI assistants. You need it when memory of past interactions influences current responses, such as in multi-session support bots or personal assistants requiring context across months.

How do I implement memory retrieval and consolidation for multi-turn assistant tasks?▼

Implement memory retrieval and consolidation by applying memory-aware prompting and tiered retrieval workflows. This consolidates short-term interactions into long-term memory, ensuring relevant historical context is surfaced for multi-turn tasks.

Does conversation memory support strict isolation to prevent context leakage between users?▼

Yes, persistent memory systems support strict isolation to prevent context leakage between users. This ensures that memory retrieval and persistence workflows remain segregated, maintaining privacy across multi-session support and long-running chats.

What's the best way to structure memory-aware prompting for customer support bots?▼

The best way to structure memory-aware prompting for customer support bots is to integrate tiered memory types with retrieval workflows. This allows the assistant to dynamically pull relevant long-term or entity memory into the prompt context.

Why does my LLM forget previous session details without memory persistence?▼

Your LLM forgets previous session details because it lacks memory persistence. Without a system to manage and consolidate short-term, long-term, and entity memory, the assistant cannot retrieve context from past interactions.