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food-travel美食旅行

Agent Skill

food-travel 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,806

周安装

157

GitHub Stars

公开资料未说明

下载量

1,243
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:food-travel(美食旅行)
来源仓库:https://github.com/zzzilin/food-travel
安装命令:
openclaw skills install food-travel
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install food-travel

简介

推荐以美食为导向的城市目的地与逐餐餐厅路线。

  • 建立社区分享与个性化口味匹配功能。
  • 适用于旅行规划与饮食探索爱好者。food-travel 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 需输入预算与时间范围以优化建议。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 部分餐厅可能需要会员或预约权限。

SKILL.md

name
food-travel
description
Plan food-driven travel experiences — recommend best cities for a dish or cuisine, generate city food maps with meal-by-meal restaurant routes, and build complete food-centric itineraries with flights, hotels, and dining schedules. Use when the user asks about food travel, food trips, eating tours, food guides, must-eat dishes, restaurant recommendations for travel, or phrases like "我想吃烤鸭去哪", "成都美食攻略", "3天吃遍西安", "周末广州美食游", "为了吃去旅行", "plan a food trip".

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 patternScenarioExample
A 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):
   flyai search-flight --origin "{origin}" --destination "{city}" --dep-date {date}
  1. Search hotels:
   flyai search-hotel --dest-name "{city}" --check-in-date {date} --check-out-date {date}

Output format

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

Filter for food-related items only.

Output format

# {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:
   flyai search-hotel --dest-name "{city}" --check-in-date {date} --check-out-date {date}
  1. Search flights (if origin provided):
   flyai search-flight --origin "{origin}" --destination "{city}" --dep-date {date}
  1. Search attractions to fill between-meal time:
   flyai search-poi --city-name "{city}"
  1. Organize into a day-by-day plan where every meal is the anchor.

Output format

# {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。"

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

89.84%
按下载量换算1,117

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可疑

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只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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