mian-hun

Recommend Taiwan ramen shops using decision-tree ranking from user preferences.

11|Updated Apr 15, 2026
One-click install
npx skills add https://github.com/voidful/mian-hun --skill mian-hun
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: mian-hun
Source: https://github.com/voidful/mian-hun/tree/main
Command: npx skills add https://github.com/voidful/mian-hun --skill mian-hun

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you stop guessing and quickly discover the best-fit Taiwan ramen shop and ramen type based on your city, flavor preference, budget, queue tolerance, and even weather.

Core Features & Use Cases

  • Personalized recommendations (客製化推薦): Uses a short quick-match flow to recommend the top ramen shop for your situation (including “no waiting” and late-night needs).
  • Ramen knowledge encyclopedia (知識百科): Answers ramen questions covering broth types, noodles, toppings, etiquette, and Taiwan ramen culture.
  • On-demand deep data: Loads reference files for ramen knowledge, shop details, and the recommendation decision logic.

Quick Start

Ask an AI to use MIAN-HUN by saying: "麵友我在台北,想吃濃厚系、預算200-300、可以排隊30分鐘,現在是冷天,幫我推薦一間最好吃的拉麵,並告訴我為什麼。"

Frequently Asked Questions about mian-hun

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get a personalized Taiwan ramen recommendation based on my city and budget?

To get a personalized Taiwan ramen recommendation, provide your city, flavor preference, budget, queue tolerance, and current weather. The system matches these inputs against a ramen shop database using decision-tree ranking logic to output the best fit.

What Taiwan ramen knowledge can I learn beyond just shop recommendations?

The Taiwan ramen knowledge encyclopedia covers broth types, noodle characteristics, toppings, eating etiquette, and Taiwan ramen culture. It also provides progression-based learning for users wanting to deepen their culinary understanding.

Does the ramen recommendation consider real-time weather conditions?

Yes, the ramen recommendation system considers current weather conditions as a matching factor. Inputting whether it is hot or cold helps the decision-tree logic select a suitable broth type and ramen shop for your environment.

Can I find Taiwan ramen shops that fit a no-waiting or late-night requirement?

You can find Taiwan ramen shops for no-waiting and late-night needs by specifying these constraints in your query. The shop database filters recommendations based on your queue tolerance and desired dining time.

How does the decision tree rank the best ramen shop for my specific preferences?

The decision tree ranks the best ramen shop by evaluating your flavor preference, budget, queue tolerance, and weather against loaded shop data. It then outputs a structured recommendation explaining the top choice.

What is the best way to ask for a ramen recommendation in Traditional Chinese?

The best way to ask is using Traditional Chinese with Taiwan usage, addressing the system as “麵友”. Include your city, desired broth richness, budget, queue tolerance, and weather to trigger the quick-match recommendation flow.