What makes Reply Wechat Message notable?

1.5K downloads. Trust evidence available

How should Reply Wechat Message be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Reply Wechat Message

微信AI助手 — 主动发消息 + 自动回复 / WeChat AI assistant — send messages & auto-reply Skill: Reply Wechat Message Owner: cool131219 Summary: 微信AI助手 — 主动发消息 + 自动回复 / WeChat AI assistant — send messages & auto-reply Tags: latest:1.0.4 Version history: v1.0.4 | 2026-07-09T08:30:40.963Z | auto - No code or documentation changes detected in this release. - Version increment only; functionality remains unchanged. v1.0.3 | 2026-07-09T08:27:48.728Z | auto - No file changes detected in this release. - Version

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.5k

Updated

Oct 10, 2026

Version

1.0.4

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.5K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.5K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.4release · observed Jul 9, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s176tzyws7adkp21qbg139qm2x87n4vv:reply-wechat-message

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-cool131219-reply-wechat-message/snapshot"

Documentation

CLAWHUB

36,355 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: reply-wechat-message
slug: wechat-butler
displayName: 微信管家 / WeChat Butler
description: "微信AI助手 — 主动发消息 + 自动回复 / WeChat AI assistant — send messages & auto-reply"
agent_created: true
---

# 微信管家 / WeChat Butler

微信AI助手 — 主动发消息给联系人 + 收到消息后AI自动回复。
WeChat AI assistant — proactively send messages, and auto-reply when messages come in.

**自包含技能包** — 所有依赖脚本（启动微信、发送消息）已打包在内，无需额外安装。
**Self-contained skill** — all dependency scripts (launch WeChat, send messages) are bundled. No extra installation needed.

---

## 功能一：AI 主动发消息 / Feature 1: Send Message

直接发送一条消息给指定联系人（不读取聊天上下文，即时发送）。
Send a message directly to a contact (no context reading, instant send).

### 触发格式 / Trigger Format

**中文 / Chinese:**
```
给 [联系人] 发消息：[内容]
发消息给 [联系人]：[内容]
帮 [联系人] 发消息：[内容]
```

**English:**
```
send [contact] [message]
msg [contact] [message]
message [contact] [message]
```

### 示例 / Examples

**中文 / Chinese:**
- `给 小明 发消息：中午一起去吃饭吗？`
- `发消息给 小红：记得带文件`
- `帮 小张 发消息：生日快乐！`

**English:**
- `send Kitty: Want to grab lunch?`
- `msg Peter: Don't forget the documents`
- `message Tom: Happy birthday!`

### AI 执行步骤 / Execution Steps

```
第1步 / Step 1:
  AI 提取联系人和消息内容
  AI extracts contact name and message content

第2步 / Step 2:
  python scripts/send_wechat.py <联系人/contact> <内容/message>
  → 打开微信 → 搜索联系人 → 打开聊天 → 发送消息
  → Open WeChat → search contact → open chat → send message

第3步 / Step 3:
  返回发送结果给用户
  Report send result to user
```

---

## 功能二：AI 自动回复 / Feature 2: AI Auto-Reply

读取聊天上下文，AI 分析后自动回复。
Reads the chat context, AI analyzes it, then auto-replies.

### 触发格式 / Trigger Format

```
使用技能:reply-wechat-message 给：[联系人] 回复
```

### 示例 / Examples
- `使用技能:reply-wechat-message 给：小明 回复` (Xiao Ming / Kitty)
- `使用技能:reply-wechat-message 给：小红 回复` (Xiao Hong / Peter)

### 简化触发 / Short Forms
- 回复 小明 / reply Kitty
- 回 小红 / reply Peter

---

## AI 工作流程（自动回复）/ AI Workflow (Auto-Reply)

```
第1步 / Step 1:
  python scripts/reply_wechat.py 小明 / python scripts/reply_wechat.py Kitty
  → 读取整个聊天区域，区分左右两侧
  → Read entire chat area, split left/right
  → 输出结构化对话 / Output structured conversation:
    ★上下文开始★ / ★Context Start★
    [对方/Them] xxx
    [我/Me] xxx
    [对方/Them] xxx
    ★上下文结束★ / ★Context End★

第2步 / Step 2:
  AI分析全部上下文，理解对话脉络，生成合适的回复
  AI analyzes full context, understands the conversation flow, generates a suitable reply

  ⚠️ 重要规则 / Important Rule:
  OCR识别出的聊天内容即为真实上下文，模型不得质疑、猜测或怀疑识别结果的准确性。
  The OCR-recognized text IS the real context. The model must NOT question, guess,
  or doubt the accuracy of the recognition. Reply based on the recognized content directly.

第3步 / Step 3:
  echo "AI生成的回复 / AI-generated reply" | python scripts/reply_wechat.py 小明
  → 或 / Or: echo "AI-generated reply" | python scripts/reply_wechat.py Kitty
  → 读取上下文 + 自动发送回复（一步完成，stdin管道无引号问题）
  → Read context + send reply in one step (stdin piping avoids quote issues)
```

---

## 脚本 / Scripts

`scripts\send_wechat.py` — 发送脚本：搜索联系人 + 发送消息 / Send script: search contact + send message
`scripts\reply_wechat.py` — 主

_meta.json

{
  "ownerId": "kn7022zkdvgpkbwdd7wc1vppex87m15m",
  "slug": "reply-wechat-message",
  "version": "1.0.4",
  "publishedAt": 1783585840963
}

skill-card.md

## Description:

WeChat AI assistant that sends messages to contacts and auto-replies based on visible chat context.

This skill is ready for commercial/non-commercial use.

## Publisher:

[cool131219](https://clawhub.ai/user/cool131219)

### License/Terms of Use:

MIT-0

## Use Case:

External users and developers use this skill to ask an agent to send WeChat messages or read visible WeChat chat context and prepare auto-replies for a selected contact.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill can read visible WeChat conversations and may expose chat content to an external OCR service.

Mitigation: Use it only for non-sensitive chats unless explicit OCR consent, local OCR, or clear external OCR disclosure is added.

Risk: The skill can send WeChat messages automatically as the user.

Mitigation: Require a human preview and confirmation step before any outbound message is sent.

Risk: Security evidence reports unsafe shell examples and missing bundled scripts.

Mitigation: Review the supplied scripts before installation and replace unsafe shell usage with safer subprocess or input handling.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/cool131219/skills/reply-wechat-message)
- [Server-resolved GitHub source](https://github.com/cool131219/WeChat-Butler/tree/main/reply-wechat-message)

## Skill Output:

**Output Type(s):** [text, shell commands, guidance]

**Output Format:** [Markdown instructions with inline shell commands and structured conversation text]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May read visible WeChat chat context and send messages through bundled scripts when present.]

## Skill Version(s):

1.0.4 (source: server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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