wechat-automation

cacr92/wereply · updated May 23, 2026

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$npx skills add https://github.com/cacr92/wereply --skill wechat-automation
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summary

Expert guidance for WeChat monitoring and automation using wxauto (Windows) and Accessibility API (macOS).

skill.md

WeChat Automation Skill

Expert guidance for WeChat monitoring and automation using wxauto (Windows) and Accessibility API (macOS).

Overview

WeReply uses Platform-specific Agents to monitor WeChat conversations and control the input box:

  • Windows Agent: Python 3.12 + wxauto v4
  • macOS Agent: Swift + Accessibility API
  • Communication: JSON protocol via stdin/stdout with Rust Orchestrator

Architecture Pattern

微信窗口
   ↓ (UI Automation)
Platform Agent
   ├→ 监听消息(定时轮询)
   ├→ 提取消息内容
   ├→ 发送到 Orchestrator (JSON via stdout)
   └→ 接收命令 (JSON via stdin)
   控制输入框(写入建议)

Windows Agent - wxauto v4

Installation and Setup

# 安装依赖
pip install wxauto==4.0.0

# 确保微信已登录且窗口可见

Message Monitoring Pattern

import json
import time
import sys
from wxauto import WeChat

class WeChatMonitor:
    def __init__(self, interval_ms: int = 500):
        """
        初始化微信监听器

        Args:
            interval_ms: 监听间隔(毫秒),默认 500ms
        """
        self.wechat = WeChat()
        self.interval_ms = interval_ms
        self.last_message_id = None

    def start_monitoring(self):
        """开始监听微信消息"""
        try:
            while True:
                # 获取当前聊天窗口的最新消息
                messages = self.wechat.GetAllMessage()

                if messages and len(messages) > 0:
                    latest_message = messages[-1]

                    # 检查是否是新消息(避免重复处理)
                    message_id = self._generate_message_id(latest_message)
                    if message_id != self.last_message_id:
                        self.last_message_id = message_id
                        self._send_message_to_orchestrator(latest_message)

                # 间隔等待
                time.sleep(self.interval_ms / 1000.0)

        except KeyboardInterrupt:
            self._send_error("监听被用户中断")
        except Exception as e:
            self._send_error(f"监听错误: {str(e)}")

    def _generate_message_id(self, message) -> str:
        """生成消息唯一ID(用于去重)"""
        # 结合时间戳、发送者、内容生成ID
        content = message.get('content', '')
        sender = message.get('sender', '')
        timestamp = message.get('time', '')
        return f"{sender}:{timestamp}:{hash(content)}"

    def _send_message_to_orchestrator(self, message):
        """
        发送消息到 Rust Orchestrator

        格式:
        {
            "type": "MessageNew",
            "content": "消息内容",
            "sender": "发送者",
            "timestamp": "2024-01-23T10:30:00"
        }
        """
        payload = {
            "type": "MessageNew",
            "content": message.get('content', ''),
            "sender": message.get('sender', ''),
            "timestamp": message.get('time', '')
        }

        # 输出到 stdout(Rust 会读取)
        print(json.dumps(payload, ensure_ascii=False), flush=True)

    def _send_error(self, error_message: str):
        """发送错误信息到 Orchestrator"""
        payload = {
            "type": "Error",
            "message": error_message
        }
        print(json.dumps(payload, ensure_ascii=False), flush=True)

# 使用示例
if __name__ == '__main__':
    monitor = WeChatMonitor(interval_ms=500)
    monitor.start_monitoring()

Input Box Control Pattern

class WeChatInputWriter:
    def __init__(self):
        self.wechat = WeChat()

    def write_to_input(self, content: str) -> bool:
        """
        写入内容到微信输入框

        Args:
            content: 要写入的文本

        Returns:
            bool: 写入是否成功
        """
        try:
            # 使用 wxauto 写入输入框
            self.wechat.SendMsg(content)
            return True
        except Exception as e:
            self._send_error(f"写入失败: {str(e)}")
            return False

    def clear_input(self) -> bool:
        """清空输入框"""
        try:
            # wxauto v4 提供的清空方法
            self.wechat.ClearMsg()
            return True
        except Exception as e:
            self._send_error(f"清空失败: {str(e)}")
            return False

    def _send_error(self, error_message: str):
        """发送错误到 Orchestrator"""
        payload = {
            "type": "Error",
            "message": error_message
        }
        print(json.dumps(payload, ensure_ascii=False), flush=True)

Command Handling Pattern

how to use wechat-automation

How to use wechat-automation on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add wechat-automation
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/cacr92/wereply --skill wechat-automation

The skills CLI fetches wechat-automation from GitHub repository cacr92/wereply and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/wechat-automation

Reload or restart Cursor to activate wechat-automation. Access the skill through slash commands (e.g., /wechat-automation) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

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general reviews

Ratings

4.643 reviews
  • Kwame Brown· Dec 24, 2024

    Registry listing for wechat-automation matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Valentina Tandon· Dec 16, 2024

    We added wechat-automation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chaitanya Patil· Dec 4, 2024

    Useful defaults in wechat-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ama Gupta· Dec 4, 2024

    Useful defaults in wechat-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Advait Anderson· Dec 4, 2024

    Solid pick for teams standardizing on skills: wechat-automation is focused, and the summary matches what you get after install.

  • Piyush G· Nov 23, 2024

    wechat-automation has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Noah Khanna· Nov 23, 2024

    wechat-automation has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kwame Taylor· Nov 23, 2024

    I recommend wechat-automation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ama Wang· Nov 7, 2024

    Keeps context tight: wechat-automation is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ama Park· Oct 26, 2024

    wechat-automation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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