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Wx4 Skill

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claw-codes
wx4-skill

微信自动化操作 skill,用于帮助用户快速完成微信消息群发、文件发送、群管理、聊天记录获取、多群监听、AI 自动回复等自动化任务。当用户需要批量发送微信消息、管理微信群、获取聊天记录、监听群消息、或接入 AI 群聊机器人时使用此 skill。

Overview

Publisherclaw-codes
Repositorywx4py
Skill namewx4-skill
Stars
763
Forks
206
Bundled files
Instructions only
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by claw-codes on GitHub. Read the source before you install it.

Installation

Install the Wx4 Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/claw-codes/wx4py/tree/main/wx4-skill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/claw-codes/wx4py.git /tmp/wx4py
mkdir -p .claude/skills
cp -r /tmp/wx4py/wx4-skill .claude/skills/wx4-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wx4 Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wx4 Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wx4 Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

微信自动化操作 Skill

⚠️ 使用前必读:安装检查

在执行任何操作前,必须先确认 wx4py 库已安装。

步骤1:检查是否已安装

python
try:
    from wx4py import WeChatClient
    print("✅ wx4py 已安装")
except ImportError:
    print("❌ 未安装 wx4py")

步骤2:如果未安装,执行安装

bash
# 方法1:从 PyPI 安装(推荐)
pip install wx4py

# 方法2:从 GitHub 安装(最新版)
pip install git+https://github.com/claw-codes/wx4py.git

# 方法3:从本地安装
cd /path/to/wx4py
pip install .

步骤3:验证安装成功

python
from wx4py import WeChatClient

# 测试导入
print("✅ 安装成功!")

# 检查版本(可选)
import wx4py
print(f"版本: {wx4py.__version__}")

重要提示:

  • 如果用户提示 ModuleNotFoundError: No module named 'wx4py',说明未安装
  • 必须先安装库再执行后续操作
  • 安装只需要一次,后续无需重复

概述

本 skill 基于 wx4py 库,帮助用户通过 Python 代码自动化控制 Windows 微信客户端,完成重复性的消息发送、群管理等任务。

适用场景:

  • 批量群发通知到多个群或联系人
  • 定时发送文件、图片、消息
  • 获取和分析聊天记录
  • 管理群公告、群昵称、免打扰等设置
  • 监听多个群聊消息,并在被 @ 时自动回复
  • 接入 OpenAI 兼容接口或自定义 AI 回调,构建群聊机器人
  • 自动化日常微信操作

前置要求:

  • Windows 系统
  • 微信 PC 客户端已安装并登录(Qt 版本,已测试 4.1.7.59、4.1.8.29)
  • Python >=3.9
  • 终端、编辑器、脚本文件统一使用 UTF-8 编码环境,避免中文消息、群名、文件名出现乱码

编码环境建议:

  • Python 源码文件保存为 UTF-8
  • PowerShell / 终端优先使用 UTF-8 编码
  • 读写文本、JSON、Markdown 文件时显式指定 encoding='utf-8'
  • 如果要输出中文日志或生成中文文件内容,优先验证终端和编辑器都已切换到 UTF-8

核心功能

1. 消息发送

发送单条消息到联系人或群
python
from wx4py import WeChatClient

wx = WeChatClient()
wx.connect()

# 发送给联系人
wx.chat_window.send_to("文件传输助手", "测试消息")

# 发送到群
wx.chat_window.send_to("工作群", "下午3点开会", target_type='group')

wx.disconnect()
批量群发消息
python
wx = WeChatClient()
wx.connect()

# 批量发送到多个群
groups = ["群1", "群2", "群3"]
message = "重要通知:明天放假"
wx.chat_window.batch_send(groups, message)

# 批量发送到多个联系人
contacts = ["张三", "李四", "王五"]
wx.chat_window.batch_send(contacts, "周末聚餐通知", target_type='contact')

wx.disconnect()

2. 文件发送

发送单个文件
python
wx = WeChatClient()
wx.connect()

# 发送文件给联系人
wx.chat_window.send_file_to("文件传输助手", r"C:\reports\weekly.pdf")

# 发送文件到群
wx.chat_window.send_file_to("工作群", r"C:\data.xlsx", target_type='group', message='可以携带消息')

wx.disconnect()
批量发送多个文件
python
wx = WeChatClient()
wx.connect()

# 发送多个文件
files = [
    r"C:\images\photo1.png",
    r"C:\images\photo2.png",
    r"C:\documents\report.pdf"
]
wx.chat_window.send_file_to("文件传输助手", files)

wx.disconnect()

3. 聊天记录获取

获取指定时间范围的聊天记录
python
wx = WeChatClient()
wx.connect()

# 获取今天的聊天记录
messages = wx.chat_window.get_chat_history(
    target="工作群",
    target_type='group',
    since='today'  # 'today' | 'yesterday' | 'week' | 'all'
)

# 处理消息
for msg in messages:
    print(f"[{msg['time']}] {msg['content']}")

wx.disconnect()

返回格式:

python
{
    "type": "text",      # text / link / system
    "content": "消息内容",
    "time": "今天 15:30"
}

4. 群管理

获取群成员列表
python
wx = WeChatClient()
wx.connect()

members = wx.group_manager.get_group_members("工作群")
print(f"群成员数量:{len(members)}")
for member in members:
    print(member)

wx.disconnect()
修改群昵称
python
wx = WeChatClient()
wx.connect()

wx.group_manager.set_group_nickname("工作群", "张三的小号")

wx.disconnect()
获取群昵称
python
wx = WeChatClient()
wx.connect()

nickname = wx.group_manager.get_group_nickname("工作群")
print(f"我在本群的昵称:{nickname}")

wx.disconnect()
设置消息免打扰
python
wx = WeChatClient()
wx.connect()

# 开启免打扰
wx.group_manager.set_do_not_disturb("工作群", enable=True)

# 关闭免打扰
wx.group_manager.set_do_not_disturb("工作群", enable=False)

wx.disconnect()
置顶/取消置顶聊天
python
wx = WeChatClient()
wx.connect()

# 置顶聊天
wx.group_manager.set_pin_chat("工作群", enable=True)

# 取消置顶
wx.group_manager.set_pin_chat("工作群", enable=False)

wx.disconnect()
修改群公告(如果修改不成功,很大概率是因为不是管理员的原因)
python
wx = WeChatClient()
wx.connect()

# 优先使用这种方式,从 Markdown 文件设置公告(支持表格、列表等格式)
wx.group_manager.set_announcement_from_markdown("工作群", "markdown文件路径")

# 直接设置文本公告
wx.group_manager.modify_announcement_simple("工作群", "本群禁止发广告,违者移出")

wx.disconnect()

5. 群聊监听与 AI 自动回复

监听多个群聊消息
python
from wx4py import CallbackHandler, MessageEvent, WeChatClient

groups = ["工作群1", "工作群2", "工作群3"]

def on_message(event: MessageEvent):
    print(f"[{event.group}] {event.content}")
    return ""  # 只监听,不自动回复

with WeChatClient(auto_connect=True) as wx:
    wx.process_groups(
        groups,
        [CallbackHandler(on_message)],
        block=True,
    )
只在被 @ 时自动回复
python
from wx4py import AsyncCallbackHandler, MessageEvent, WeChatClient

groups = ["工作群1", "工作群2"]

def reply(event: MessageEvent):
    if not event.is_at_me:
        return ""
    return f"收到:{event.content}"

with WeChatClient(auto_connect=True) as wx:
    wx.process_groups(
        groups,
        [AsyncCallbackHandler(reply, auto_reply=True, reply_on_at=True)],
        block=True,
    )
监听指定群消息并转发给指定联系人或群
python
from wx4py import ForwardRuleHandler, GroupForwardRule, WeChatClient

rules = [
    GroupForwardRule(
        source_group="告警群",
        targets=["值班同学"],
        target_type="contact",
        prefix_template="[告警群] ",
    ),
    GroupForwardRule(
        source_group="项目群A",
        targets=["项目群B"],
        target_type="group",
        prefix_template="[项目群A] ",
    ),
]

with WeChatClient(auto_connect=True) as wx:
    wx.process_groups(
        ["告警群", "项目群A"],
        [ForwardRuleHandler(rules)],
        block=True,
    )

适用场景:

  • 监听告警群,把消息转发给值班联系人
  • 监听业务群,把消息同步到另一个群
  • 针对不同来源群,配置不同的联系人或群作为目标
使用内置 AIClient 接入 OpenAI 兼容接口
python
import os
from wx4py import AIClient, AIConfig, AIResponder, AsyncCallbackHandler, WeChatClient

ai = AIClient(
    AIConfig(
        base_url="https://api.example.com/v1",
        api_format="completions",
        model="your-model-id",
        api_key=os.environ["AI_API_KEY"],
        enable_thinking=False,
    )
)

with WeChatClient(auto_connect=True) as wx:
    wx.process_groups(
        ["工作群"],
        [
            AsyncCallbackHandler(
                AIResponder(ai, context_size=8, reply_on_at=True),
                auto_reply=True,
            )
        ],
        block=True,
    )
使用自定义 AI 或业务系统
python
from wx4py import AsyncCallbackHandler, MessageEvent, WeChatClient

def custom_reply(event: MessageEvent):
    if not event.is_at_me:
        return ""

    # 这里可以调用公司内部接口、其他 AI SDK 或业务系统。
    return f"自定义回复:{event.content}"

with WeChatClient(auto_connect=True) as wx:
    wx.process_groups(
        ["工作群"],
        [AsyncCallbackHandler(custom_reply, auto_reply=True, reply_on_at=True)],
        block=True,
    )

实现说明:

  • 每个群优先打开独立聊天窗口,适合同时监听多个群。
  • 自动回复通过发送队列串行执行,避免多个群同时回复时抢占窗口。
  • 本库发送的消息会自动记录,避免机器人监听到自己的回复后再次触发。

使用模式

推荐:使用上下文管理器

python
from wx4py import WeChatClient

with WeChatClient() as wx:
    # 自动连接和断开
    wx.chat_window.send_to("文件传输助手", "Hello!")

手动连接管理

python
wx = WeChatClient()
wx.connect()

try:
    # 执行操作
    wx.chat_window.send_to("文件传输助手", "Hello!")
finally:
    wx.disconnect()

实用脚本示例

示例1:定时群发通知

python
from wx4py import WeChatClient
import schedule
import time

def send_daily_notification():
    with WeChatClient() as wx:
        groups = ["工作群1", "工作群2", "工作群3"]
        message = "早安!今日工作提醒..."
        wx.chat_window.batch_send(groups, message, target_type='group')
        print("通知已发送")

# 每天早上9点发送
schedule.every().day.at("09:00").do(send_daily_notification)

while True:
    schedule.run_pending()
    time.sleep(60)

示例2:收集昨天的聊天记录并保存

python
from wx4py import WeChatClient
import json
from datetime import datetime

with WeChatClient() as wx:
    # 获取昨天的聊天记录
    messages = wx.chat_window.get_chat_history(
        target="工作群",
        target_type='group',
        since='yesterday'
    )

    # 保存到文件
    filename = f"chat_history_{datetime.now().strftime('%Y%m%d')}.json"
    with open(filename, 'w', encoding='utf-8') as f:
        json.dump(messages, f, ensure_ascii=False, indent=2)

    print(f"已保存 {len(messages)} 条消息到 {filename}")

示例3:批量发送文件到多个群

python
from wx4py import WeChatClient
import os

# 准备要发送的文件
files_dir = r"C:\weekly_reports"
files = [os.path.join(files_dir, f) for f in os.listdir(files_dir) if f.endswith('.pdf')]

groups = ["管理群", "开发群", "产品群"]

with WeChatClient() as wx:
    for group in groups:
        print(f"正在向 {group} 发送文件...")
        wx.chat_window.send_file_to(group, files, target_type='group')
        print(f"已完成 {group}")

注意事项

操作限制

  • 仅支持 Windows 系统
  • 需要微信客户端已登录
  • 操作期间不要手动操作微信窗口
  • 受微信 UIA 限制,聊天记录无法获取发送者姓名

最佳实践

  • 使用上下文管理器确保连接正确释放
  • 批量操作时添加适当延迟,避免操作过快
  • 重要操作前先测试小范围验证
  • 群聊机器人优先使用 reply_on_at=True,避免普通群消息触发自动回复
  • AI 回调耗时会影响监听延迟,大规模群监听时建议使用响应更快的模型或业务接口
  • 捕获异常并记录日志
  • 需要系统查看方法说明时,优先参考 docs/guide/API_GUIDE.md

异常处理

python
from wx4py import WeChatClient, WeChatNotFoundError, ControlNotFoundError

try:
    with WeChatClient() as wx:
        wx.chat_window.send_to("目标", "消息")
except WeChatNotFoundError:
    print("错误:未找到微信窗口,请确保微信已打开并登录")
except ControlNotFoundError as e:
    print(f"错误:未找到控件 - {e}")
except Exception as e:
    print(f"未知错误:{e}")

工作流程指导

当用户请求微信自动化任务时,按以下步骤操作:

  1. 理解需求

    • 确认目标(联系人/群组)
    • 确认操作类型(发消息/发文件/获取记录/管理设置)
    • 确认是否批量操作
  2. 编写脚本

    • 使用上述示例作为模板
    • 添加必要的错误处理
    • 考虑操作延迟和频率限制
  3. 验证和测试

    • 先用小范围测试(如"文件传输助手")
    • 确认脚本正确运行后再扩大范围
  4. 执行和反馈

    • 运行脚本并观察结果
    • 提供清晰的执行反馈
    • 必要时保存日志

快速参考

操作方法示例
发消息给联系人chat_window.send_to(target, message)wx.chat_window.send_to("张三", "Hi")
发消息到群chat_window.send_to(target, message, target_type='group')wx.chat_window.send_to("工作群", "Hi", target_type='group')
批量群发chat_window.batch_send(targets, message, target_type)wx.chat_window.batch_send(["群1", "群2"], "Hi", target_type='group')
发送文件chat_window.send_file_to(target, file_path)wx.chat_window.send_file_to("张三", r"C:\file.pdf")
获取聊天记录chat_window.get_chat_history(target, target_type, since)wx.chat_window.get_chat_history("工作群", 'group', 'today')
获取群成员group_manager.get_group_members(group_name)wx.group_manager.get_group_members("工作群")
获取群昵称group_manager.get_group_nickname(group_name)wx.group_manager.get_group_nickname("工作群")
设置群昵称group_manager.set_group_nickname(group_name, nickname)wx.group_manager.set_group_nickname("工作群", "小张")
消息免打扰group_manager.set_do_not_disturb(group_name, enable)wx.group_manager.set_do_not_disturb("工作群", True)
置顶聊天group_manager.set_pin_chat(group_name, enable)wx.group_manager.set_pin_chat("工作群", True)
修改群公告group_manager.modify_announcement_simple(group_name, content)wx.group_manager.modify_announcement_simple("工作群", "新公告")
监听/处理多个群聊process_groups(groups, handlers, block=True)wx.process_groups(["群1"], [handler], block=True)
监听群消息并转发process_groups(groups, [ForwardRuleHandler(rules)], block=True)wx.process_groups(["群1"], [ForwardRuleHandler(rules)], block=True)
自动回复群聊AsyncCallbackHandler(reply_func, auto_reply=True)AsyncCallbackHandler(reply, auto_reply=True, reply_on_at=True)
AI 自动回复AIResponder(ai_client, context_size=8)AsyncCallbackHandler(AIResponder(ai, context_size=8, reply_on_at=True), auto_reply=True)

常见问题

Q: 脚本运行时提示找不到微信窗口? A: 确保微信已打开并登录,窗口不要最小化。

Q: 批量发送时部分失败? A: 检查目标名称是否正确,建议在每次发送间添加延迟。

Q: 中文消息、群名或导出文件出现乱码? A: 先确认终端、编辑器、Python 源文件和读写文件逻辑都使用 UTF-8;写文件时显式传入 encoding='utf-8',避免依赖系统默认编码。

Q: 可以获取图片和语音消息吗? A: 目前主要支持文本消息,图片和语音的完整获取受限于微信 UIA。

Q: AI 自动回复会回复所有群消息吗? A: 不会,推荐传入 reply_on_at=True。普通消息只监听,只有群消息 @ 当前群昵称时才会触发回复。

Q: 可以不用内置 AIClient,接入自己的 AI 服务吗? A: 可以。process_groups() 接收任意 handler;最简单的方式是使用 AsyncCallbackHandler 包装你自己的回调,回调返回字符串即可入队发送。

Q: 多个群同时触发回复会抢占窗口吗? A: 自动回复会进入发送队列串行执行,避免多个独立聊天窗口同时发送导致焦点抢占。

Q: 脚本可以在后台运行吗? A: 微信窗口需要在前台才能进行 UI 自动化操作。

Frequently asked questions

What does the Wx4 Skill AI skill do?

微信自动化操作 skill,用于帮助用户快速完成微信消息群发、文件发送、群管理、聊天记录获取、多群监听、AI 自动回复等自动化任务。当用户需要批量发送微信消息、管理微信群、获取聊天记录、监听群消息、或接入 AI 群聊机器人时使用此 skill。

Why use Wx4 Skill on TypingMind?

Because you install it once and use it with any model. Wx4 Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wx4 Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/claw-codes/wx4py/tree/main/wx4-skill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wx4 Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wx4 Skill?

As many as you like. As long as a model supports skills, you can use Wx4 Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wx4 Skill AI skill free?

Yes. It is published on GitHub by claw-codes under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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