muninn-memory-protocol

Name: muninn-memory-protocol
Availability: InStock
Author: likesjx

Enforce the Muninn memory protocol for session continuity across AI clients.

2|Updated Mar 5, 2026

Search Tags:#AI agent #protocol #memory #continuity #workflow standardization #client adapter

Ready to run

npx skills add https://github.com/likesjx/philotic-stack --skill muninn-memory-protocol

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: muninn-memory-protocol
Source: https://github.com/likesjx/philotic-stack/tree/main/skills/muninn-memory-protocol
Command: npx skills add https://github.com/likesjx/philotic-stack --skill muninn-memory-protocol

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires muninn_mcp.py, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of inconsistent memory handling across different AI clients, ensuring a unified and reliable approach to recalling and writing back information.

Core Features & Use Cases

Standardized Memory Workflow: Adopts the Muninn memory protocol for consistent session continuity.
Shared Helper Utilization: Leverages a common Python script (muninn_mcp.py) for memory operations, avoiding redundant implementations.
Atomic Memory Bursts: Encourages writing memory in small, meaningful segments to maintain clarity and manageability.
Use Case: When integrating a new cognitive client that needs to interact with a shared knowledge base, use this skill to ensure it correctly retrieves past context and saves new outcomes according to the established Muninn protocol.

Quick Start

Use the muninn-memory-protocol skill to bootstrap the client's memory.

Frequently Asked Questions about muninn-memory-protocol

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

FAQPage Schema

How do I standardize AI agent memory handling across different clients?▼

To standardize AI agent memory handling, you enforce a shared memory protocol that manages context retrieval before work and atomic memory write-back after outcomes. This ensures consistent session continuity across different cognitive clients using a shared Python helper.

What is atomic memory fragmentation for AI workflows?▼

Atomic memory fragmentation is the process of writing memory in small, meaningful segments to maintain clarity and manageability. It enforces size constraints for recall and decision entries, ensuring a unified and reliable approach to writing back session outcomes.

How do I maintain session continuity for an AI agent interacting with a shared knowledge base?▼

To maintain session continuity, retrieve past context before starting work and save new outcomes using a shared Python script. This workflow standardizes how an AI agent interacts with a shared knowledge base according to the established protocol.

Do I need a Python environment to manage memory protocol workflows?▼

Yes, you need a Python environment to manage memory protocol workflows because the skill leverages a common Python helper script, muninn_mcp.py, to execute memory operations and avoid redundant implementations across clients.

Why does my AI client fail to save memory consistently across sessions?▼

Your AI client fails to save memory consistently due to inconsistent memory handling across different clients. Adopting a standardized memory workflow with a shared helper script enforces atomic memory bursts and adherence to size constraints for reliable write-back.