memory

Load MEMORY.md and log events to HISTORY.md for persistent AI context.

29|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/whanyu1212/Krill.jl --skill memory-whanyu1212
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/whanyu1212/Krill.jl/tree/main/context/skills/memory
Command: npx skills add https://github.com/whanyu1212/Krill.jl --skill memory-whanyu1212

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Two-layer memory system provides persistent long-term context and an append-only history, ensuring consistent AI behavior across sessions.

Core Features & Use Cases

  • Two-layer memory: Long-term memory stored in MEMORY.md and a separate HISTORY.md log for events.
  • Auto-consolidation: Periodically distills durable facts to MEMORY.md and logs events with timestamps.
  • Guided recall: Searches history when needed; loads long-term facts at session start for immediate context.
  • Use Case: An agent working on a multi-turn project can remember preferences and key relationships across days.

Quick Start

Load MEMORY.md into memory at session start and log events to HISTORY.md.

Frequently Asked Questions about memory

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

FAQPage Schema
How do I maintain persistent AI context across multiple sessions?

Long-term AI memory works by loading durable facts from MEMORY.md into the session at start, while appending ongoing events to HISTORY.md, allowing periodic auto-consolidation of context over extended conversations.

How does auto-consolidation manage long-term memory for AI agents?

Auto-consolidation manages long-term memory by periodically distilling durable facts from the history log into MEMORY.md, ensuring the AI agent retains key relationships and preferences without manual summarization.

Can I use a two-layer memory system for multi-turn AI projects?

Yes, a two-layer memory system is designed for multi-turn AI projects, allowing an agent to remember preferences and key relationships across days by separating long-term facts from an append-only event history.

What is the best way to log AI agent history with timestamps?

Logging AI agent history with timestamps is achieved by appending events to a HISTORY.md file, separating the append-only event log from the distilled facts in MEMORY.md to preserve context.

Does guided recall search the history log when AI context is missing?

Yes, guided recall searches the history log when needed, retrieving specific past events from HISTORY.md while loading long-term facts into MEMORY.md for immediate AI context at session start.