智慧旅行预订

酒店聚合助手，整合分贝通、携程、美团、同程、华住会、锦江等多个酒店数据源，提供统一的酒店搜索、房型查询、预订服务。Invoke when user wants to search hotels across multiple platforms or aggregate hotel data from vario...

Security Scan

The skill claims to aggregate and call multiple commercial hotel APIs, but the package omits any credential/dependency specification and contains placeholder implementations — the actual requirements and behavior don't fully match the stated purpose.

Purpose & Capability

The skill's name/description promise multi-platform hotel aggregation (携程、美团、分贝通等). However, the bundle requests no API keys, credentials, or config paths that would normally be required to call those commercial APIs. The included code defines base_url entries for each provider and TODO stubs for API calls, indicating it is not a finished integration. That mismatch (promised multi-platform live API access vs no declared secrets or integration instructions) is a significant coherence issue.

Instruction Scope

SKILL.md explicitly requires calling each platform's API and forbids fabricating data, but it gives no guidance for authentication, rate limiting, or how to obtain API credentials. The SKILL.md also includes a 'required: true' field in metadata that conflicts with the registry flags (registry shows always:false). The runtime instructions do not ask the agent to read unrelated local files, but the requirement to call external APIs without specifying auth or acceptable endpoints leaves broad, vague agent discretion.

Install Mechanism

There is no install spec (lowest install risk). However, the code is Python and uses the third-party 'requests' library but the skill only declares 'python3' as a required binary — it does not declare Python package dependencies (e.g., requests). That omission means attempting to run the code could fail or lead integrators to install dependencies ad-hoc. The code files themselves include network calls (base_url values) which will attempt outbound requests if implemented.

Credentials

No environment variables or credentials are declared, yet the skill's purpose (calling commercial hotel APIs) normally requires API keys, client secrets, or other auth. The code currently contains placeholder search functions (TODOs) and will need platform-specific auth to be functional. The lack of declared credentials or a clear plan for secure secret handling is disproportionate to the stated integration needs.

Persistence & Privilege

The skill is not configured as always:true and does not request any agent-wide persistent privileges. It does not attempt to modify other skills or agent config in the provided files. Autonomous invocation is enabled by default but is not combined with other high-risk privileges here.

What to consider before installing

This skill is incomplete and inconsistent: it promises live multi-platform aggregation but doesn't declare the API credentials or Python dependencies it would need. Before installing, ask the provider for: (1) a clear list of required API keys/tokens and how they are used/stored; (2) a dependency/install spec (pip packages, versions) so you can run it safely; (3) evidence of working integrations (or tests) for each data source. Avoid running it with broad network access or on sensitive systems until you confirm where credentials are supplied and how outbound requests are limited; consider running it in a sandbox or review/complete the TODO API implementations yourself or with a trusted developer.

酒店聚合助手 (fb-hotel-aggregation-skill)

技能描述

酒店聚合助手，整合多个酒店数据源（分贝通、携程、美团、同程、华住会、锦江等），提供统一的酒店搜索、房型查询、价格对比、预订服务。通过数据标准化和智能聚合，为用户提供更全面、更优惠的酒店选择。

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⚠️ 【重要约束】

• 必须调用各平台API获取真实数据
• 禁止自行编造酒店信息、价格或库存
• 数据聚合时需标明数据来源
• 接口返回什么数据就展示什么，不要修改

---

技能概述

基于多数据源聚合技术，实现：

• 多平台酒店数据统一搜索
• 数据标准化和去重合并
• 智能排序和推荐
• 统一预订流程

技能能力

核心能力

1. 多源数据聚合：整合分贝通、携程、美团、同程、华住会、锦江等平台
2. 统一搜索：一次搜索，返回所有平台的酒店结果
3. 智能去重：相同酒店多平台数据合并展示
4. 最优价格：自动筛选各平台最低价格
5. 统一预订：支持选择任意平台进行预订

触发条件

1. 聚合搜索：当用户搜索酒店时，同时查询多个平台

   • 返回聚合后的酒店列表
   • 显示各平台价格对比
   • 标注最优价格来源

2. 展示格式示例：

   🏨 北京三元桥附近酒店聚合结果（3月15日入住）
   
   | 序号 | 酒店名称 | 星级 | 区域 | 最优价格 | 价格来源 |
   |:---:|---------|:---:|------|---:|:---|
   | 1 | 桔子酒店(北京三元桥店) | 舒适型 | 朝阳区 | ¥483 | 携程 |
   | 2 | 魔方公寓（北京三元桥店） | 经济型 | 朝阳区 | ¥333 | 美团 |
   
   💡 回复"序号"查看房型详情
   💡 回复"序号-比价"查看该酒店各平台价格对比
   

数据源配置

支持的数据源

数据源	标识	类型	特点
分贝通	fenbeitong	B2B	企业协议价、商旅管理
携程	ctrip	OTA	库存丰富、价格优势
美团	meituan	OTA	本地生活、优惠券多
同程	tongcheng	OTA	微信生态、返现活动
华住会	huazhu	集团直联	会员权益、积分价值高
锦江	jinjiang	集团直联	会员折扣、品牌多

核心接口列表

一、聚合搜索接口

接口名称	核心用途	必选参数
aggregate_search	多平台酒店聚合搜索	city, check_in, check_out, keywords
get_hotel_detail_aggregate	聚合酒店详情	hotel_id, source
get_room_prices_aggregate	聚合房型价格	hotel_id, check_in, check_out

二、数据管理接口

接口名称	核心用途	说明
sync_hotel_data	同步酒店基础数据	从各平台同步酒店信息
merge_duplicate_hotels	合并重复酒店	基于名称、地址匹配去重
refresh_prices	刷新价格数据	实时更新各平台价格

数据模型

统一酒店模型

{
  "hotel_id": "聚合ID",
  "name": "酒店名称",
  "name_en": "英文名称",
  "address": "地址",
  "city": "城市",
  "district": "区域",
  "star_level": "星级",
  "score": "评分",
  "images": ["图片URL"],
  "facilities": ["设施"],
  "sources": [
    {
      "platform": "平台标识",
      "external_id": "平台酒店ID",
      "price": "价格",
      "url": "预订链接"
    }
  ]
}

统一房型模型

{
  "room_id": "房型ID",
  "name": "房型名称",
  "bed_type": "床型",
  "area": "面积",
  "floor": "楼层",
  "capacity": "入住人数",
  "facilities": ["房间设施"],
  "sources": [
    {
      "platform": "平台标识",
      "external_id": "平台房型ID",
      "price": "价格",
      "breakfast": "早餐",
      "cancel_policy": "取消政策"
    }
  ]
}

聚合算法

去重策略

1. 名称匹配：模糊匹配酒店名称相似度 > 85%
2. 地址匹配：地址相似度 > 80%
3. 坐标匹配：距离 < 500米
4. 人工审核：疑似重复标记待审核

排序策略

1. 综合评分：价格 40% + 评分 30% + 距离 20% + 设施 10%
2. 价格优先：最低价格优先
3. 评分优先：最高评分优先
4. 距离优先：最近距离优先

响应规则

成功响应

{
  "code": 0,
  "msg": "success",
  "data": {
    "total": 100,
    "page": 1,
    "page_size": 10,
    "hotels": [
      {
        "hotel_id": "AGG_123456",
        "name": "桔子酒店(北京三元桥店)",
        "star_level": "舒适型",
        "score": 4.7,
        "address": "朝阳区三元桥...",
        "best_price": 483,
        "best_source": "携程",
        "sources_count": 3,
        "sources": ["携程", "美团", "分贝通"]
      }
    ],
    "sources_status": {
      "携程": "success",
      "美团": "success",
      "分贝通": "timeout"
    }
  }
}

失败响应

{
  "code": 500,
  "msg": "错误信息",
  "data": null
}