How do I install the M2Wise skill?

Run npx skillmds add zengyi-thinking/m2wise in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is M2Wise safe to use?

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Which AI agents work with M2Wise?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is M2Wise free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published M2Wise?

zengyi-thinking (@zengyi-thinking) published this skill. Their other Agent Skills are listed on their SkillMD profile.

M2Wise by zengyi-thinking — Agent Skill

Name: M2Wise
Rating: 4.2 (5 reviews)
Author: zengyi-thinking

M2Wise

Memory-to-Wisdom Engine - Extract memories from conversations and generate actionable wisdom strategies for AI companion robots. Use when building AI assistants that need long-term memory and personalized wisdom evolution.

zengyi-thinking 08dbf88 25 files · 113.8 KB Updated Sep 10, 2026

File contents

M2Wise - Memory-to-Wisdom Engine

Enable AI companion robots to grow wisdom from experience

Overview

M2Wise is a Memory-to-Wisdom Engine for long-term memory and wisdom evolution in AI companion robots. It extracts memory from raw conversation data and further abstracts them into migratable, verifiable, and versionable wisdom strategies.

When to Use This Skill

Use M2Wise when you need:

Long-term memory management for AI companions
Extract user preferences, facts, and commitments from conversations
Generate actionable wisdom from accumulated memories
Track and evolve wisdom effectiveness over time

Core Features

1. Memory Subsystem

Preference Memory: Extract explicit user preferences (e.g., "I prefer concise Chinese technical answers")
Fact Memory: Record user's identity, background, and other factual information
Commitment Tracking: Track user commitments and promises
Affective Trace: Track user emotional changes over time

2. Wisdom Subsystem

Wisdom Generation: Automatically generate actionable wisdom from memory clusters
Counterexample Mining: Identify boundary cases where wisdom doesn't apply
Confidence Tracking: Automatically track wisdom effectiveness
Self-Evolution: Auto-adjust wisdom based on hit rates

3. Three-Phase Evolution Model

Online (Online Phase) → Sleep (Consolidation Phase) → Dream (Verification Phase)
       ↓                        ↓                           ↓
   Real-time Interaction   Memory Extraction + Clustering   Counterexample Mining + Verification & Publishing

Quick Start

from m2wise import M2Wise, M2WiseConfig

# Initialize
config = M2WiseConfig(data_dir="./m2wise_data")
engine = M2Wise(config=config)

# Add user conversation
engine.add(
    [{"role": "user", "content": "I prefer Chinese answers for technical questions"}],
    user_id="alice"
)

# Search memories and wisdom
bundle = engine.search("How to answer technical questions?", user_id="alice")
print(bundle.as_prompt())

# Generate wisdom (Sleep phase)
sleep_report = engine.sleep(user_id="alice")
print(f"Generated {sleep_report.drafts_created} wisdom drafts")

# Verify and publish (Dream phase)
dream_report = engine.dream(user_id="alice")
print(f"Published {dream_report.published} wisdoms")

Technical Features

Component	Features
Memory Extraction	Supports preference, fact, commitment, and more types
Similarity Calculation	Jaccard/Cosine/Levenshtein multi-strategy fusion
Confidence Evaluation	Time decay + evidence weight + hit rate weighting
Caching Strategy	LRU cache + TTL expiration, ~300x speedup

Adapter Support

Compatible with multiple external memory systems:

mem0 compatible
Letta compatible
Anthropic message format

Use Cases

AI companion robots
Personalized assistants
Long-term user profiling systems
Intelligent customer service

Installation

pip install m2wise

Version

Current Version: 1.0.0

License

Apache-2.0

---name: m2wisedescription: Memory-to-Wisdom Engine - Extract memories from conversations and generate actionable wisdom strategies for AI companion robots. Use when building AI assistants that need long-term memory and personalized wisdom evolution.---# M2Wise - Memory-to-Wisdom Engine> Enable AI companion robots to grow wisdom from experience## OverviewM2Wise is a **Memory-to-Wisdom Engine** for long-term memory and wisdom evolution in AI companion robots. It extracts memory from raw conversation data and further abstracts them into migratable, verifiable, and versionable wisdom strategies.## When to Use This SkillUse M2Wise when you need:- Long-term memory management for AI companions- Extract user preferences, facts, and commitments from conversations- Generate actionable wisdom from accumulated memories- Track and evolve wisdom effectiveness over time## Core Features### 1. Memory Subsystem- **Preference Memory**: Extract explicit user preferences (e.g., "I prefer concise Chinese technical answers")- **Fact Memory**: Record user's identity, background, and other factual information- **Commitment Tracking**: Track user commitments and promises- **Affective Trace**: Track user emotional changes over time### 2. Wisdom Subsystem- **Wisdom Generation**: Automatically generate actionable wisdom from memory clusters- **Counterexample Mining**: Identify boundary cases where wisdom doesn't apply- **Confidence Tracking**: Automatically track wisdom effectiveness- **Self-Evolution**: Auto-adjust wisdom based on hit rates### 3. Three-Phase Evolution Model```Online (Online Phase) → Sleep (Consolidation Phase) → Dream (Verification Phase) ↓ ↓ ↓ Real-time Interaction Memory Extraction + Clustering Counterexample Mining + Verification & Publishing```## Quick Start```pythonfrom m2wise import M2Wise, M2WiseConfig# Initializeconfig = M2WiseConfig(data_dir="./m2wise_data")engine = M2Wise(config=config)# Add user conversationengine.add( [{"role": "user", "content": "I prefer Chinese answers for technical questions"}], user_id="alice")# Search memories and wisdombundle = engine.search("How to answer technical questions?", user_id="alice")print(bundle.as_prompt())# Generate wisdom (Sleep phase)sleep_report = engine.sleep(user_id="alice")print(f"Generated {sleep_report.drafts_created} wisdom drafts")# Verify and publish (Dream phase)dream_report = engine.dream(user_id="alice")print(f"Published {dream_report.published} wisdoms")```## Technical Features| Component | Features ||-----------|----------|| Memory Extraction | Supports preference, fact, commitment, and more types || Similarity Calculation | Jaccard/Cosine/Levenshtein multi-strategy fusion || Confidence Evaluation | Time decay + evidence weight + hit rate weighting || Caching Strategy | LRU cache + TTL expiration, ~300x speedup |## Adapter SupportCompatible with multiple external memory systems:- mem0 compatible- Letta compatible- Anthropic message format## Use Cases- AI companion robots- Personalized assistants- Long-term user profiling systems- Intelligent customer service## Installation```bashpip install m2wise```## VersionCurrent Version: 1.0.0## LicenseApache-2.0

Frequently asked questions