memory-manager

Organize agent memory into episodic, semantic, and procedural structures.

1|Updated Apr 3, 2026
One-click install
npx skills add https://github.com/bobvarkey/openclaw-workspace --skill memory-manager-bobvarkey
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: memory-manager
Source: https://github.com/bobvarkey/openclaw-workspace/tree/main/skills/memory-manager
Command: npx skills add https://github.com/bobvarkey/openclaw-workspace --skill memory-manager-bobvarkey

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps agents avoid context loss by organizing memory into clear structures, monitoring when memory is getting too full, and preserving important state before it is overwritten or compressed.

Core Features & Use Cases

  • Memory initialization: Creates a three-tier layout for episodic, semantic, and procedural memory, plus snapshots and legacy storage.
  • Compression monitoring: Checks overall memory usage and flags safe, warning, or critical thresholds before context becomes fragile.
  • Organization and recovery: Migrates flat memory files into the right buckets, supports manual categorization, and saves snapshots for recovery.
  • Search and stats: Searches memory by type and reports breakdowns, growth, and recent activity for ongoing maintenance.
  • Use case: An agent that has accumulated many notes, decisions, and workflows can use this Skill to classify them properly, find relevant history quickly, and keep a backup trail before memory pressure rises.

Quick Start

Ask the agent to initialize the memory structure, check compression risk, and organize existing memory files into episodic, semantic, and procedural categories.

Frequently Asked Questions about memory-manager

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

FAQPage Schema
How do I organize agent memory to prevent context loss in long workflows?

Agent memory organization uses a three-tier layout for episodic, semantic, and procedural structures to prevent context loss. It monitors compression thresholds and preserves snapshots, ensuring historical recall and recovery before memory is overwritten or compressed.

What is the best way to monitor agent memory compression risk?

Monitoring agent memory compression risk requires checking overall memory usage against safe, warning, and critical thresholds. This flags when context becomes fragile, allowing you to preserve important state and create backups before memory pressure rises and causes data loss.

How do I categorize flat agent memory files into structured types?

Categorizing flat agent memory files involves migrating them into episodic, semantic, and procedural memory buckets. This process supports manual classification of accumulated notes, decisions, and workflows, enabling agents to find relevant history quickly and maintain organized knowledge structures.

Can I search agent memory by type and report usage statistics?

Searching agent memory by type allows you to query specific episodic, semantic, or procedural categories. The system reports breakdowns, growth metrics, and recent activity, providing ongoing maintenance statistics to track memory distribution and usage patterns across the workspace.

Do I need a local workspace directory to initialize agent memory structures?

Agent memory initialization requires file-based setup across local workspace directories. You need a local environment to create the three-tier episodic, semantic, and procedural memory layout, plus dedicated directories for snapshots and legacy storage to enable backup and recovery operations.

When should I preserve agent memory snapshots before context is overwritten?

Preserving agent memory snapshots is necessary when compression monitoring flags warning or critical thresholds. This saves important state before context becomes fragile or memory pressure rises, creating a recovery trail for agents with accumulated notes, decisions, and workflows in context-heavy environments.