Expert guidance for WeChat monitoring and automation using wxauto (Windows) and Accessibility API (macOS).
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionwechat-automationExecute the skills CLI command in your project's root directory to begin installation:
Fetches wechat-automation from cacr92/wereply and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate wechat-automation. Access via /wechat-automation in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Expert guidance for WeChat monitoring and automation using wxauto (Windows) and Accessibility API (macOS).
WeReply uses Platform-specific Agents to monitor WeChat conversations and control the input box:
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)
# 安装依赖
pip install wxauto==4.0.0
# 确保微信已登录且窗口可见
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()
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)
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
jezweb/claude-skills
Registry listing for wechat-automation matched our evaluation — installs cleanly and behaves as described in the markdown.
We added wechat-automation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in wechat-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in wechat-automation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: wechat-automation is focused, and the summary matches what you get after install.
wechat-automation has been reliable in day-to-day use. Documentation quality is above average for community skills.
wechat-automation has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend wechat-automation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: wechat-automation is the kind of skill you can hand to a new teammate without a long onboarding doc.
wechat-automation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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