food-travel — Eat-First Travel Planner#

One-liner: Input a dish, a craving, or a city — get a complete travel plan built around eating.

This skill solves the full "eat → where → go → stay → route" chain for food lovers.

Scenario Detection#

Identify which scenario the user wants, then follow the corresponding workflow:

Trigger patternScenarioExampleA dish/cuisine + no cityA: Pick a destination"我想吃烤鸭" "想吃海鲜去哪"A city + food intentB: City food map"成都有什么好吃的" "杭州美食攻略"A city + duration + food intentC: Full itinerary"3天吃遍西安" "周末广州美食游"

If unclear, ask the user to clarify.

---

Scenario A: Pick a Destination for Food#

Input: a dish, cuisine, or flavor preference Output: best city recommendation + food list + travel logistics

Steps#

1. Web search: "{dish/cuisine} 最正宗 去哪个城市吃" to identify the top 2-3 cities.

2. For each city, web search: "{city} 必吃 {dish} 餐厅推荐" to get restaurant data.

3. Search flights (if user provides origin): bash

   flyai search-flight --origin "{origin}" --destination "{city}" --dep-date {date}
   
   

4. Search hotels: bash

   flyai search-hotel --dest-name "{city}" --check-in-date {date} --check-out-date {date}
   
   

Output format#

text

# 为了{dish}，去{city}！

## 为什么选{city}
（One-paragraph reason）

## 必吃清单

| 餐厅 | 招牌菜 | 人均 | 地址 | 推荐理由 |
|------|--------|------|------|----------|
| ...  | ...    | ...  | ...  | ...      |

## 怎么去
（Flight options table with booking links）

## 住哪里
（Hotel options near food districts, with booking links）

---

Scenario B: City Food Map#

Input: a city name Output: meal-by-meal restaurant map organized by time of day

Steps#

1. Web search: "{city} 必吃餐厅推荐" + "{city} 特色小吃 推荐" + "{city} 夜宵 推荐".

2. Organize results into 4 time slots: 早餐, 午餐, 晚餐, 夜宵/下午茶.

3. keyword-search supplement: bash

   flyai keyword-search --query "{city} 美食券 餐厅"
   
   

   Filter for food-related items only.

Output format#

text

# {city}美食地图

## 🌅 早餐
| 餐厅 | 推荐 | 人均 | 地址 |
|------|------|------|------|

## ☀️ 午餐
...

## 🌆 晚餐
...

## 🌙 夜宵
...

## 可预订美食产品
（Filtered keyword-search results with images and booking links）

> 餐厅数据来自网络搜索，美食券来自 fly.ai 实时结果。

---

Scenario C: Full Food-Driven Itinerary#

Input: city + duration (e.g. "3天吃遍西安") Output: day-by-day schedule with every meal planned + attractions between meals + transport + hotel

Steps#

1. Web search: "{city} {N}天美食攻略" + "{city} 必吃餐厅推荐".

2. Search hotels: bash

   flyai search-hotel --dest-name "{city}" --check-in-date {date} --check-out-date {date}
   
   

3. Search flights (if origin provided): bash

   flyai search-flight --origin "{origin}" --destination "{city}" --dep-date {date}
   
   

4. Search attractions to fill between-meal time: bash

   flyai search-poi --city-name "{city}"
   
   

5. Organize into a day-by-day plan where every meal is the anchor.

Output format#

text

# {N}天吃遍{city}

## Day 1
### 🌅 早餐 — {restaurant}
- 推荐：{dishes}｜人均：{price}｜地址：{addr}

### ☀️ 上午 — {attraction}（吃完溜达消食）
（POI info with booking link）

### 🍜 午餐 — {restaurant}
- 推荐：{dishes}｜人均：{price}｜地址：{addr}

### 🌆 下午 — {attraction/activity}

### 🔥 晚餐 — {restaurant}
- 推荐：{dishes}｜人均：{price}｜地址：{addr}

### 🌙 夜宵 — {restaurant}
- 推荐：{dishes}

## Day 2
...

## 交通
（Flight options with booking links）

## 住宿
（Hotel options with booking links, prefer hotels near Day 1 dinner area）

## 预算估算
| 项目 | 预估费用 |
|------|----------|
| 机票 | ¥xxx |
| 住宿 | ¥xxx |
| 餐饮 | ¥xxx |
| 门票 | ¥xxx |
| **合计** | **¥xxx** |

> 餐厅数据来自网络搜索，机票酒店来自 fly.ai 实时结果。

---

General Rules#

• Food comes first — every itinerary section starts with a meal, attractions fill the gaps.
• Web search for restaurants — flyai has no restaurant database; always use web search for dining data.
• flyai for logistics — use search-flight, search-hotel, search-poi, keyword-search for transport, accommodation, attractions, and bookable dining products.
• Always include booking links — for every flight, hotel, and POI result, show [Click to book]({url}).
• Always include images — show ![]({picUrl}) or ![]({mainPic}) when available.
• Practical details — include price, address, opening hours when available.
• Source attribution — "餐厅数据来自网络搜索，机票酒店来自 fly.ai 实时结果。"