memory

Store long-term facts in MEMORY.md and searchable HISTORY.md event logs.

37|1|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/saolalab/clawforce --skill memory-saolalab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory
Source: https://github.com/saolalab/clawforce/tree/main/clawbot/skills/memory
Command: npx skills add https://github.com/saolalab/clawforce --skill memory-saolalab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Agents often lose context over time, making it hard to remember long‑term facts or retrieve past events without manual searching.

Core Features & Use Cases

  • Two‑layer memory: Persistent MEMORY.md stores always‑loaded facts, while HISTORY.md records an append‑only event log.
  • Grep‑based search: Quickly locate relevant past events using standard grep commands through the exec tool.
  • Auto‑consolidation: Conversations are summarized into HISTORY.md and distilled into MEMORY.md automatically, requiring no manual maintenance.
  • Easy updates: Add new facts instantly with edit_file or write_file to keep the long‑term memory current.

Quick Start

Ask the memory skill to find all mentions of “deadline” in recent events.

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 to an autonomous agent workflow?▼

Long-term memory for autonomous agents is managed through a two-layer architecture using MEMORY.md for always-loaded facts and HISTORY.md for append-only event logs, providing persistent context without manual maintenance.

How can I search past event logs for an AI agent?▼

Past event logs are searchable using standard grep commands executed through the exec tool, allowing you to quickly locate specific mentions or events within the HISTORY.md append-only log file.

Does agent memory auto-consolidation require manual summarization?▼

Auto-consolidation requires no manual maintenance, as conversations are automatically summarized into HISTORY.md and distilled into MEMORY.md, ensuring long-term facts and event logs stay current automatically.

What is the best way to store persistent context for AI agents?▼

Storing persistent context is best handled by a two-layer memory architecture using MEMORY.md for always-loaded facts and HISTORY.md for searchable logs, which automatically consolidates conversations to retain context.

Can I update long-term facts in MEMORY.md manually?▼

Long-term facts in MEMORY.md can be updated instantly using edit_file or write_file commands; while auto-consolidation handles automatic distillation, direct file edits keep the memory current.