# Send M2Wise to your agent
Use the source page and any available docs to guide the install because the item is currently unstable or timing out.
## Fast path
- Open the source page via Review source status.
- If you can obtain the package, extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the source page and extracted files.
## Suggested prompts
### New install

```text
I tried to install a skill package from Yavira, but the item is currently unstable or timing out. Inspect the source page and any extracted docs, then tell me what you can confirm and any manual steps still required.
```
### Upgrade existing

```text
I tried to upgrade a skill package from Yavira, but the item is currently unstable or timing out. Compare the source page and any extracted docs with my current installation, then summarize what changed and what manual follow-up I still need.
```
## Machine-readable fields
```json
{
  "schemaVersion": "1.0",
  "item": {
    "slug": "m2wise",
    "name": "M2Wise",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
    "sourceUrl": "https://clawhub.ai/zengyi-thinking/m2wise",
    "canonicalUrl": "https://clawhub.ai/zengyi-thinking/m2wise",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadUrl": "/downloads/m2wise",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=m2wise",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "packageFormat": "ZIP package",
    "primaryDoc": "SKILL.md",
    "includedAssets": [
      "EXAMPLES.md",
      "REFERENCE.md",
      "SKILL.md",
      "references/api.md",
      "references/memory.md",
      "references/wisdom.md"
    ],
    "downloadMode": "manual_only",
    "sourceHealth": {
      "source": "tencent",
      "slug": "m2wise",
      "status": "unstable",
      "reason": "timeout",
      "recommendedAction": "retry_later",
      "checkedAt": "2026-05-02T07:00:51.191Z",
      "expiresAt": "2026-05-02T19:00:51.191Z",
      "httpStatus": null,
      "finalUrl": null,
      "contentType": null,
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=m2wise",
        "error": "Timed out after 5000ms",
        "slug": "m2wise"
      },
      "scope": "item",
      "summary": "Item is unstable.",
      "detail": "This item is timing out or returning errors right now. Review the source page and try again later.",
      "primaryActionLabel": "Review source status",
      "primaryActionHref": "https://clawhub.ai/zengyi-thinking/m2wise"
    },
    "validation": {
      "installChecklist": [
        "Wait for the source to recover or retry later.",
        "Review SKILL.md only after the download returns a real package.",
        "Treat this source as transient until the upstream errors clear."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/m2wise",
    "downloadUrl": "https://openagent3.xyz/downloads/m2wise",
    "agentUrl": "https://openagent3.xyz/skills/m2wise/agent",
    "manifestUrl": "https://openagent3.xyz/skills/m2wise/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/m2wise/agent.md"
  }
}
```
## Documentation

### M2Wise - Memory-to-Wisdom Engine

This is a skill for the OpenClaw AI Agent to enable long-term memory and self-evolving wisdom.

### 🎯 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.

### 🚦 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.

### 🛠️ 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")

### 🧩 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.

### 🧠 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).

### 🚀 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.

### 🔗 Resources

GitHub Repository: https://github.com/zengyi-thinking/M2Wise.git
Installation via OpenClaw (ClawHub):
npx clawdhub@latest install m2wise
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: zengyi-thinking
- Version: 1.0.2
## Source health
- Status: unstable
- Item is unstable.
- This item is timing out or returning errors right now. Review the source page and try again later.
- Health scope: item
- Reason: timeout
- Checked at: 2026-05-02T07:00:51.191Z
- Expires at: 2026-05-02T19:00:51.191Z
- Recommended action: Review source status
## Links
- [Detail page](https://openagent3.xyz/skills/m2wise)
- [Send to Agent page](https://openagent3.xyz/skills/m2wise/agent)
- [JSON manifest](https://openagent3.xyz/skills/m2wise/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/m2wise/agent.md)
- [Download page](https://openagent3.xyz/downloads/m2wise)