Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
M2Wise
Memory-to-Wisdom Engine for AI agents. Use this skill to give yourself long-term memory, extract user preferences/facts from conversations, and track wisdom...
skill openclawclawhub Free
Memory-to-Wisdom Engine for AI agents. Use this skill to give yourself long-term memory, extract user preferences/facts from conversations, and track wisdom...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- EXAMPLES.md, REFERENCE.md, SKILL.md, references/api.md, references/memory.md, references/wisdom.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- zengyi-thinking
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
M2Wise - Memory-to-Wisdom Engine
This is a skill for the OpenClaw AI Agent to enable long-term memory and self-evolving wisdom.
π― Purpose
As an AI agent, you inherently lack persistent memory across distinct sessions. The M2Wise skill bridges this gap. It allows you to: Extract and store memories (facts, preferences, commitments) from user conversations. Retrieve contextual memories and aggregated wisdom before answering. Consolidate raw memories into overarching "Wisdom" (rules, schemas, and skills) during idle time.
π¦ When to Use This Skill
You should proactively invoke this skill in the following scenarios: User expresses a preference: e.g., "I prefer short answers", "Don't use emojis." User shares a personal fact: e.g., "I work as a Python developer", "I live in Beijing." User asks you to remember: e.g., "Remember that I hate spam." Before answering complex questions: To recall the user's past preferences or facts to personalize your response.
π οΈ How to Use (Agent Instructions)
You can interact with M2Wise via its Python SDK. Use your shell/execution environment to run these scripts snippet by snippet.
1. Installation Requirements
Make sure the environment has M2Wise installed before calling its Python API: pip install m2wise[all]
2. Saving Memories (Online Phase)
When you detect a fact or preference in the conversation, run a quick python script to save it: from m2wise_sdk import M2WiseSDK sdk = M2WiseSDK() # Extract and save the user's message sdk.add_message("current_user_id", "I prefer concise Chinese answers for technical questions")
3. Retrieving Context (Online Phase)
Before fulfilling a user's request, fetch their relevant memories: from m2wise_sdk import M2WiseSDK sdk = M2WiseSDK() context = sdk.get_context("current_user_id", "How should I answer this technical question?") print("Retrieved Context:", context) Action: Read the output of this script and adapt your final response to the user based on the retrieved context.
4. Background Processing (Sleep & Dream)
It is a good practice to trigger memory consolidation periodically (e.g., at the end of a long task). from m2wise_sdk import M2WiseSDK sdk = M2WiseSDK() # Sleep: Extracts memories and groups them into Wisdom Drafts sdk.trigger_sleep("current_user_id") # Dream: Verifies drafts against counterexamples and publishes them sdk.trigger_dream("current_user_id")
𧩠MCP Server Alternative
If your OpenClaw runtime supports MCP (Model Context Protocol), you can start the M2Wise MCP server and use its native tools instead of writing Python scripts: # Start the MCP server m2wise-mcp --data-dir ./data Available MCP Tools: m2wise_add: Add memory from conversation. m2wise_search: Search memories and wisdom. m2wise_sleep: Generate wisdom drafts. m2wise_dream: Verify and publish wisdom.
π§ Memory and Wisdom Types You Will Encounter
Memories: preference (likes/dislikes), fact (states/attributes), commitment (future actions). Wisdoms: principle (interaction guidelines), schema (behavioral patterns), skill (operational tactics).
π Best Practices
Be Proactive: Don't wait for the user to explicitly say "remember this". If they state a strong preference, save it using sdk.add_message(). Context First: For ambiguous requests, always query the memory bank first. Consolidate Often: Run trigger_sleep() and trigger_dream() after completing a major task to ensure your wisdom evolves and stays clean.
π Resources
GitHub Repository: https://github.com/zengyi-thinking/M2Wise.git Installation via OpenClaw (ClawHub): npx clawdhub@latest install m2wise
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- EXAMPLES.md
- REFERENCE.md
- references/api.md
- references/memory.md
- references/wisdom.md