memory

Manage long-term facts and short-term context with MEMORY.md and HISTORY.md files.

55|7|Updated Mar 2, 2026
One-click install
npx skills add https://github.com/wp931120/tiny_agent --skill memory-wp931120
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/wp931120/tiny_agent/tree/main/workspace/skills/memory
Command: npx skills add https://github.com/wp931120/tiny_agent --skill memory-wp931120

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Two-layer memory system with grep-based recall to persist important facts and provide quick access to past context.

Core Features & Use Cases

  • Two-layer memory: MEMORY.md for long-term facts and HISTORY.md for append-only logs.
  • Auto-consolidation: old conversations summarized and merged to long-term memory.
  • Quick retrieval: grep-based search to locate past events and facts when needed.

Quick Start

Store a key fact in MEMORY.md to enable future recall in conversations.

Frequently Asked Questions about memory

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

FAQPage Schema
How do I implement long-term memory for AI agents to recall past context?▼

Long-term memory for AI agents is managed using a two-layer architecture with MEMORY.md for persistent facts and HISTORY.md for append-only logs. This setup enables persistent recall across interactive sessions and project contexts.

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

Auto-consolidation works by summarizing old conversations and merging them into long-term memory. This process transfers relevant past context from short-term history logs into persistent facts for continuous future use.

What is the best way to grep past events and facts for AI context management?▼

The best way to grep past events and facts is through grep-based retrieval on a two-layer memory system. This allows quick search across append-only logs and consolidated long-term facts to locate required context.

Can I use this two-layer memory architecture across multiple project contexts?▼

Yes, the two-layer memory architecture applies across multiple project contexts. It organizes memory to support memory-driven workflows requiring recall of past information within various interactive sessions.

Why do I need separate files for short-term context and long-term facts?▼

Separating short-term context and long-term facts optimizes context management by isolating append-only logs from persistent facts. This prevents history bloat while ensuring quick retrieval of consolidated, relevant information.