WenXiaoWendy

WenXiaoWendy / dianping-queue-skill

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用于让 agent 在大众点评自动取号,定时取号,符合条件后触发取号,以及定时获取排号情况等,全流程自动化脚本控制,无需ai手动执行

31
0
69% credibility
Found May 27, 2026 at 31 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This is a personal automation tool that helps your AI assistant get queue numbers from Dianping (a popular Chinese restaurant and lifestyle platform) on your behalf. When you ask your assistant to get a queue number for a restaurant, it automatically controls a browser to navigate the Dianping website, logs into your account, and follows the same steps you would take manually. The tool can also monitor queue status and alert you at the right time to leave for the restaurant. It is designed strictly for personal use with your own account and explicitly prohibits commercial or batch operations.

How It Works

1
😤 You discover the tool when you're tired of missing your turn

You've been waiting at restaurants only to find out you missed your queue number, and you hear about a tool that can help your AI assistant manage this for you.

2
📦 You install the skill on your computer

You run a simple setup command that prepares everything your computer needs to help your AI assistant handle queue numbers.

3
📱 You tell your assistant your phone number and city

During first-time setup, you share your phone number so the assistant can log into your account and your city so it knows which restaurants to search.

4
🤖 Your AI assistant springs into action

When you ask your assistant to get a queue number, it automatically opens a browser, navigates to Dianping, logs you in, and follows the same steps you would do manually.

5
You can choose how you want to use it
⏰
Get your number at a specific time

Tell your assistant when you want to arrive, and it will grab your queue number automatically at the right moment.

📊
Wait until fewer people are ahead

Set a threshold like 'only get the number when there are fewer than 5 tables ahead,' and your assistant watches and waits until the condition is met.

🔔
Get alerted when it's almost your turn

After getting your number, your assistant keeps watching and notifies you when you should start heading to the restaurant.

🎉 You arrive just in time, every time

No more arriving too early and waiting, no more missing your turn. Your assistant handles the timing while you go about your day.

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AI-Generated Review

What is dianping-queue-skill?

This is a Python-based automation tool that lets AI agents handle restaurant queue management on Dianping, China's largest restaurant review platform. Instead of manually opening the app or website to grab a queue number, you tell your agent what you need and it handles the entire flow: fetching queue status, grabbing numbers at specific times, or waiting until conditions are met before taking action. It uses Playwright under the hood to simulate real browser interactions, making it look like a genuine user to the platform.

Why is it gaining traction?

The hook here is hands-off restaurant queue management. Chinese urban dining culture involves significant waiting times, and this tool solves the "sit at home until your number is called" problem. You can set thresholds like "grab a number when there are fewer than 5 tables ahead" or "notify me when I should leave." For developers building agent workflows, this demonstrates practical browser automation integrated with AI decision-making. The skill architecture means it slots into existing agent setups without custom code.

Who should use this?

Chinese-speaking developers building agent workflows who frequently dine at popular restaurants with long queues. If you live in Shanghai, Beijing, or any city where Dianping queue management is part of daily life, this saves real friction. Not useful if you do not use Dianping or if your agent stack does not support skill-based extensions.

Verdict

This is a niche utility solving a specific real-world problem for a Chinese audience. With only 31 stars and a credibility score of 0.699999988079071%, it is extremely early-stage and unproven at scale. The closed-source helper component means you are trusting a binary without seeing what it does. Approach with caution: test thoroughly before relying on it for anything important.

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