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memory-manager

Encode, retrieve, and organize cognitive memories in conversational AI systems.

1|Updated Feb 8, 2026
Search Tags:#memory management#cognitive AI#knowledge retention#working memory#reminder system
One-click install
npx skills add https://github.com/framerslab/agentos-skills --skill memory-manager-framerslab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-manager
Source: https://github.com/framerslab/agentos-skills/tree/main/registry/curated/memory-manager
Command: npx skills add https://github.com/framerslab/agentos-skills --skill memory-manager-framerslab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the challenge of effectively managing and recalling knowledge, preferences, and workflows in a conversational AI environment.

Core Features & Use Cases

  • Cognitive Memory Management: Encode, recall, forget, and set reminders for long-term knowledge.
  • Memory Types: Handle episodic, semantic, procedural, and prospective memories.
  • Memory Scopes: Control access to memories at thread, user, persona, and organization levels.
  • Encoding & Retrieval: Improve memory encoding based on personality and mood, and retrieve information with precision.
  • Prospective Memory: Set reminders based on time, events, or context.
  • Working Memory: Prioritize and manage limited working memory capacity.

Quick Start

Encode the user's preferred format for reports into the semantic memory under the user scope using 'encode_memory report_format preferred'.

Frequently Asked Questions about memory-manager

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

FAQPage Schema
How does cognitive memory management work in a conversational AI system?▼

Memory encoding can be customized based on personality traits and mood states to improve recall precision. This approach tailors how knowledge and preferences are stored within the AI agent's cognitive framework.

How do I set reminders for an AI agent using prospective memory?▼

Working memory prioritization manages limited capacity by organizing and retrieving the most relevant information. This ensures the AI agent maintains focus on high-priority tasks without exceeding memory constraints.

Can I control memory access scopes for different users and organizations?▼

Reminders are set based on time, events, or context using prospective memory functions. This allows the AI agent to trigger notifications and recall tasks precisely when needed.

What are the limitations of working memory capacity in AI agents?▼

Working memory has a limited capacity that requires active prioritization and management. Overloading this memory scope can degrade the AI agent's ability to retrieve and process immediate tasks effectively.

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