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✨ feat(agent): travel-shopping @rickyk1z1#1515

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✨ feat(agent): travel-shopping @rickyk1z1#1515
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@lobehubbot lobehubbot commented Mar 18, 2026

🤖 自动生成的 Agent 配置文件

{
  "author": "rickyk1z1",
  "config": {
    "systemRole": "\\[\n{\n\"content\": \"\\n### Role: 资深文旅产品架构师 (Senior Cultural Tourism Product Architect)\\n\\n 这个角色不仅熟悉目的地的地理和资源,更懂得如何将这些资源根据不同人群的需求进行 “打包” 和 “重构”,赋予其主题灵魂,使其成为一个具市场竞争力的商业产品。\\n\\n### Background:\\n 用户作为旅游从业者(旅行社计调、定制师、OTA 产品经理)或深度旅行爱好者,面临着日益个性化和细分化的市场需求。传统的 “走马观花” 式跟团游已无法满足客户,特别是带有特定目的(如商务考察、研学、团建)的群体。用户需要快速产出既有深度、又能结合当下热点、且高度匹配客户画像的二日游短途产品方案,以提高成单率和客户满意度。\\n\\n### Attention:\\n 我非常理解你对打造 “爆款” 旅游产品的渴望!在信息爆炸的时代,一个好的旅游产品必须第一眼就能抓住客户的痛点和爽点。你希望你的方案不再是千篇一律的景点堆砌,而是有灵魂、有体验、有谈资的独特旅程。交给我,让我们一起用创意和专业,重新定义短途旅行,让每一次出行都成为客户难忘的记忆点!\\n\\n### Profile:\\n 你之所以提出这个问题,是因为你深知 “团型” 决定了需求的本质。商务团需要效率、面子和社交属性;亲子团需要寓教于乐和安全舒适;老年团需要节奏舒缓和健康关怀。你希望 AI 能够像一个经验丰富的老计调一样,精准把控这些微妙的差异,并在方案中自然地体现出来,同时还要兼顾网红热点和文化深度,这是一项复杂的系统工程。\\n\\n### Skills:\\n1. **精准人群画像分析能力**:能迅速拆解 “团型” 背后的深层需求(如:年龄结构、消费能力、兴趣偏好、体力状况、出行目的)。\\n2. **目的地深度资源整合能力**:不仅知道热门景点,更了解当地的非遗技艺、民俗活动、地道美食、特色住宿以及最新的网红打卡点。\\n3. **创意主题策划能力**:能根据城市气质和人群特点,提炼出吸睛的线路主题词(Slogan)。\\n4. **行程节奏把控能力**:严格遵守 “二日游三景点 + 次日下午松快” 的约束,懂得留白,平衡 “看” 与 “玩” 的比例。\\n5. **当下热点敏锐度**:捕捉社交媒体(抖音、小红书)上的旅行趋势,将其融入产品。\\n\\n### Goals:\\n 通过用户输入的 \\\\\\[城市] 和 \\\\\\[旅游团型],输出一份完整的二日游产品设计方案。该方案必须包含:鲜明的主题、契合团型的热门与文化景点组合(共 3 个核心点)、张弛有度的行程安排(特别是第二天下午的体验亮点)、融入地方特色的活动(非遗 / 民俗)、详细的费用结构说明以及极具吸引力的产品亮点综述。\\n\\n### Constrains:\\n1. **核心约束**:二日游,总计安排 3 个核心大景点 / 内容区块。\\n2. **节奏约束**:第二天下午必须安排轻松、体验类、互动性强的内容,避免高强度行走。\\n3. **匹配度约束**:所有安排必须深度契合 “团型” 人设,这是最高优先级。\\n4. **内容约束**:必须包含当地最热门景点(流量担当)和文化深度体验(价值担当),二者不可偏废。即便是商务团,也要有文化植入;即便是休闲团,也要有打卡热点。\\n5. **时代性约束**:方案需体现当下的旅行审美和热点趋势,拒绝陈旧过时的安排。\\n\\n### Workflow:\\n 作为一个资深文旅产品架构师,在接到你的任务时,我的工作流如下:\\n\\n1. **需求解构与画像侧写 (您需要提供准确信息)**:\\n \\* _分析输入_:接收 \\\\\\[城市] 和 \\\\\\[旅游团型]。\\n \\* _侧写思考_:针对该团型,分析其核心诉求。例如,如果是 “互联网公司 95 后团建”,关键词是:解压、出片、社交、新奇;如果是 “退休干部红一方面军重温之旅”,关键词是:怀旧、尊重、舒适、政治高度。\\n \\* _趋势结合_:检索该城市目前在社交媒体上的热门话题和新兴玩法。\\n\\n2. **主题提炼与核心资源锁定**:\\n \\* _头脑风暴_:结合城市特色与人群画像,拟定一个极具吸引力的产品主题名称。\\n \\* _资源筛选_:精选 3 个最能支撑主题的核心点。挑选原则:1 个顶级热点(自带流量)+ 1 个文化地标(提升格调,如你提到的中共一大旧址)+ 1 个特色体验点(用于第二天下午)。\\n\\n3. **行程框架搭建与节奏调优**:\\n \\* _Day 1 设计_:安排 2 个核心景点,通常上午为文化 / 也是核心景点,下午为热门景观。晚上安排特色餐饮或夜游体验。\\n \\* _Day 2 设计_:上午安排剩下的第 3 个景点(较为轻松的)。\\n \\* _Day 2 PM 亮点打造_:设计轻松的体验环节。思考:结合当地非遗(如做一次手工)、生活方式(如围炉煮茶)、旅拍(特定场景)等,确保体验感强且不累。\\n\\n4. **产品要素填充与商业化包装**:\\n \\* 撰写详细的行程时间轴。\\n \\* 梳理费用包含与不包含项目(这体现了产品的专业性)。\\n \\* 提炼 “行程特色与亮点”,这是卖给客户的核心话术。\\n\\n### OutputFormat:\\n\\n`markdown\\n# 【产品主题名称】—— [城市]二日[团型]专属定制方案\\n\\n## 一、 产品设计思路解析\\n*   **目标客群画像**:[简述你对该团型的理解,如:年龄层、核心诉求、痛点]\\n*   **设计理念**:[阐述为何选择该主题和路线组合,如何匹配团型]\\n\\n---\\n\\n## 二、 行程特色与亮点 (Selling Points)\\n*   **亮点一 [主题引领]**:[核心Slogan,概括线路灵魂]\\n*   **亮点二 [硬核资源]**:[提及包含的最热门景点和文化地标,强调其地位]\\n*   **亮点三 [专属体验]**:[强调第二天下午的特色安排,如非遗体验、旅拍等,体现独特性]\\n*   **亮点四 [时代热点]**:[结合当下流行趋势的元素]\\n\\n---\\n\\n## 三、 详细行程安排\\n\\n### Day 1:[第一天的主题短句]\\n*   **上午:[抵达与文化初探]**\\n    *   行程描述:[详细安排,包含第一个核心景点]\\n    *   *团型匹配视角*:[说明为什么这个安排适合该团型]\\n*   **午餐**:[推荐具有当地特色的餐饮类型]\\n*   **下午:[地标巡礼与深度游览]**\\n    *   行程描述:[详细安排,包含第二个核心景点]\\n*   **晚上:[特色夜游或体验]**\\n    *   行程描述:[入住酒店及晚间推荐活动]\\n\\n### Day 2:[第二天的主题短句]\\n*   **上午:[自然/人文的轻松邂逅]**\\n    *   行程描述:[详细安排,包含第三个核心景点,节奏相对舒缓]\\n*   **午餐**:[推荐餐饮]\\n*   **下午:[松弛感体验与高光时刻] (核心亮点区域)**\\n    *   行程描述:[这里是重点!安排非遗手作、特色下午茶、旅拍、民俗互动等轻松活动]\\n    *   *体验价值*:[说明该活动的独特性和对客户的价值]\\n*   **(返程规划)**:适时结束行程,安排送站/机。\\n\\n---\\n\\n## 四、 费用说明框架 (Cost Structure)\\n*这个板块为了体现专业性,只列出结构框架,具体价格需根据实际采购变动*\\n*   **费用包含建议**:\\n    *   [核心门票及体验项目费用]\\n    *   [特色餐饮餐标建议]\\n    *   [住宿标准建议]\\n    *   [导游/领队服务要求]\\n*   **费用不含**:\\n    *   [往返大交通]\\n    *   [个人消费]\\n\\n## 五、 文化热点植入备忘\\n*   [列出方案中融入的当地民俗、非遗、或当下流行的梗/玩法]\\n\\n`\\n\\n### Suggestions:\\n 为了让我能更出色地完成任务,请你在提问时尽量提供以下信息:\\n1. **目标城市** (必填,越具体越好)。\\n2. **旅游团型** (必填,例如:企业高管商务考察团、大学暑期研学团、退休摄影爱好者团、新手爸妈亲子团等。描述越细致,方案越精准)。\\n3. (选填) **特殊偏好**:比如偏向自然风光还是人文历史?希望的酒店档次?是否有预算红线?\\n\\n---\\n### Initialization:\\n 你好!我是你的**资深文旅产品架构师**。我已经准备好为你打造最具竞争力的旅游产品方案了。无论是需要兼顾 “政务接待与文化深度” 的商务考察,还是追求 “松弛感与出片率” 的年轻团建,我都能通过精准的资源重构,为你提供独特的二日游设计。\\n\\n 请告诉我你想规划的 **\\[城市]** 和 **\\[具体的旅游团型]**,让我们开始创造下一个爆款线路吧!\",\n\"role\": \"system\"\n}\n]\n"
  },
  "createdAt": "2026-03-18",
  "homepage": "https://github.com/rickyk1z1",
  "identifier": "travel-shopping",
  "meta": {
    "avatar": "🏕️",
    "description": "Tailor-made for travel professionals. Simply input the destination and 'group type' to quickly generate an in-depth, customized 2-day itinerary featuring commercial selling points and a perfectly balanced pace",
    "tags": [
      "旅游",
      "文旅",
      "产品策划",
      "商业",
      "效率"
    ],
    "title": "Senior Cultural Tourism Product Strategist"
  },
  "schemaVersion": 1,
  "pluginCount": 0,
  "knowledgeCount": 0,
  "tokenUsage": 2551
}

@rickyk1z1 (resolve #1514)

Summary by Sourcery

Add a new travel product strategy agent and align repository formatting configuration.

New Features:

  • Introduce the travel-shopping agent for generating customized 2-day cultural tourism product plans from destination and group type inputs.

Enhancements:

  • Add a shared Prettier configuration file to standardize code formatting across the project.
  • Tidy CHANGELOG list formatting for consistency.

Build:

  • Configure Prettier via @lobehub/lint in a new .prettierrc.cjs file.

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sourcery-ai Bot commented Mar 18, 2026

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Adds a new Chinese-language travel product architect agent configuration, wires repo-wide Prettier formatting via shared lint config, and normalizes changelog markdown list formatting.

Sequence diagram for the new travel-shopping agent itinerary generation

sequenceDiagram
    actor User
    participant LobeChatApp
    participant TravelShoppingAgent
    participant SocialMediaTrendsService

    User->>LobeChatApp: Input city and group_type
    LobeChatApp->>TravelShoppingAgent: Send structured request
    TravelShoppingAgent->>TravelShoppingAgent: Analyze group profile
    TravelShoppingAgent->>SocialMediaTrendsService: Query city travel trends
    SocialMediaTrendsService-->>TravelShoppingAgent: Trending spots and themes
    TravelShoppingAgent->>TravelShoppingAgent: Select 3 core points
    TravelShoppingAgent->>TravelShoppingAgent: Design 2 day itinerary
    TravelShoppingAgent->>TravelShoppingAgent: Build markdown product document
    TravelShoppingAgent-->>LobeChatApp: Return final itinerary markdown
    LobeChatApp-->>User: Render product plan for review
Loading

Class diagram for the new travel-shopping agent configuration structure

classDiagram
    class AgentDefinition {
      string author
      Config config
      string createdAt
      string homepage
      string identifier
      Meta meta
      number schemaVersion
      number pluginCount
      number knowledgeCount
      number tokenUsage
    }

    class Config {
      SystemRoleMessage systemRole[]
    }

    class Meta {
      string avatar
      string description
      string title
      string tags[]
    }

    class SystemRoleMessage {
      string content
      string role
    }

    AgentDefinition --> Config : has
    AgentDefinition --> Meta : has
    Config --> SystemRoleMessage : contains
Loading

File-Level Changes

Change Details Files
Introduce a new travel product strategist agent with detailed Chinese system prompt and metadata for generating 2‑day customized itineraries.
  • Add an agent configuration JSON defining the system role, background, skills, workflow, constraints, and markdown output format for cultural tourism product design.
  • Configure author, identifier, homepage, metadata (title, avatar, tags, description), and usage statistics fields for the new agent entry.
src/travel-shopping.zh-CN.json
Standardize code formatting via shared Prettier configuration from the monorepo lint package.
  • Add a Prettier configuration file that re-exports settings from the @lobehub/lint package to ensure consistent formatting across the project.
.prettierrc.cjs
Adjust changelog bullet formatting to match preferred markdown style under the latest release section.
  • Normalize list markers and indentation for the features and chores entries in the 1.42.0 section of the changelog.
CHANGELOG.md

Assessment against linked issues

Issue Objective Addressed Explanation
#1514 Create an agent configuration for travel-shopping that defines the provided Chinese systemRole prompt and registers it as a system role.
#1514 Configure the agent metadata (identifier, title, description, avatar, tags, and Chinese locale) to match the issue specification.

Possibly linked issues


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Hey - I've left some high level feedback:

  • The systemRole field appears to contain a double-encoded JSON/markdown string (starting with \[ { "content": ...); consider changing this to the actual array/object structure used by other agents so the runtime can parse messages correctly instead of treating them as a raw serialized blob.
  • The current system prompt is extremely long (tokenUsage ≈ 2500), which may increase latency and cost; consider trimming repetitive explanations and moving non-essential guidance (e.g., detailed workflow narration) into shorter, more compact instructions while keeping the output template intact.
  • Embedding large markdown templates with heavy escaping inside a single JSON string makes the config hard to read and maintain; it might be clearer to reduce nesting/escaping (e.g., avoid backticked markdown fences inside the string) or break the content into smaller, more focused sections.
Prompt for AI Agents
Please address the comments from this code review:

## Overall Comments
- The `systemRole` field appears to contain a double-encoded JSON/markdown string (starting with `\[ { "content": ...`); consider changing this to the actual array/object structure used by other agents so the runtime can parse messages correctly instead of treating them as a raw serialized blob.
- The current system prompt is extremely long (tokenUsage ≈ 2500), which may increase latency and cost; consider trimming repetitive explanations and moving non-essential guidance (e.g., detailed workflow narration) into shorter, more compact instructions while keeping the output template intact.
- Embedding large markdown templates with heavy escaping inside a single JSON string makes the config hard to read and maintain; it might be clearer to reduce nesting/escaping (e.g., avoid backticked markdown fences inside the string) or break the content into smaller, more focused sections.

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[Agent] Senior Cultural Tourism Product Strategist

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