memory

Store, retrieve, and search persistent AI agent memory across sessions.

5|Updated Feb 26, 2026

Search Tags:#context #recall #search #knowledge base #memory #persistence

Ready to run

npx skills add https://github.com/lidge-jun/cli-jaw-skills --skill memory-lidge-jun

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/lidge-jun/cli-jaw-skills/tree/main/memory
Command: npx skills add https://github.com/lidge-jun/cli-jaw-skills --skill memory-lidge-jun

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a persistent, long-term memory for the AI, allowing it to recall past interactions, decisions, and user preferences across sessions, preventing the need to re-learn information.

Core Features & Use Cases

Persistent Storage: Saves durable facts, user preferences, and project context.
Information Retrieval: Searches and retrieves specific information from memory.
Use Case: After a user states a preference for a specific coding style, the AI can save this to memory and recall it in future interactions to tailor its responses.

Quick Start

Search memory for information about user preferences.

Frequently Asked Questions about memory

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

FAQPage Schema

How do I add persistent memory to an AI agent for recalling knowledge across sessions?▼

You enable persistent long-term memory by saving durable facts and user preferences to specific file paths. The memory system uses SQLite indexing across profile, episodic, and semantic directories to retrieve this knowledge across conversational sessions.

What's the best way to store user preferences so an AI remembers them later?▼

Storing user preferences requires saving them as durable facts into structured memory file paths. Once saved, the AI can search and recall these preferences in future interactions to tailor its responses without re-learning.

How does keyword search work for retrieving context from a knowledge base?▼

Keyword search retrieves context by querying a persistent knowledge base indexed by SQLite. This allows the AI to perform broad searches across stored profile, episodic, and semantic data to find specific information efficiently.

Do I need a specific storage layout to save and retrieve long-term memory context?▼

Yes, saving and retrieving long-term memory context requires a defined storage layout. You must set up profile, episodic, and semantic directories indexed by SQLite to ensure durable knowledge is structured for efficient recall.

Can I search across past interactions to recall specific project context?▼

Yes, you can search past interactions to recall specific project context. The memory system supports broad keyword searching across persistent storage, allowing the AI agent to retrieve previously saved durable facts and decisions.