OpenClaw

daolv-hotel-search

Hotel search and shortlist comparison using the ai-go-hotel MCP server (getHotelSearchTags, searchHotels). Use when users need fast hotel discovery by city/POI/date/budget/amenities/brand, but do not yet need full booking-room confirmation.

cnchenkai0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install cnchenkai/daolv-hotel-search

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Daolv Hotel Search

Focus on fast discovery and clean shortlist output.

Workflow

Capture search intent

Extract destination, check-in date, nights, guests, budget, star range, distance preference, amenities, brand preferences.
Ask only minimal missing questions.

Prime tags once

Call ai-go-hotel.getHotelSearchTags once per task.
Map user intent into hotelTags.requiredTags / preferredTags / excludedTags.

Run hotel search

Call ai-go-hotel.searchHotels with required fields:
- originQuery, place, placeType
Include optional fields when available:
- checkInDate, stayNights, adultCount, countryCode, starRatings, distanceInMeter, size, hotelTags
Prefer size=10 first, then narrow to top 3-5.

Return shortlist only

Output: 1 recommended + 2 alternatives.
Include quick trade-off: price / location / amenities / star.
Do not deep-dive room plans unless user asks to proceed to booking.

Output Template

搜索条件：地点 / 日期 / 人数 / 预算 / 偏好
首选酒店：价格区间、位置、标签命中、推荐理由
备选 1-2：同结构
下一步：是否进入“房型与退改细节”阶段

Quality Bar

Use concrete numbers when available.
Use price.lowestPrice + price.currency (price is object).
If price missing/null, mark as “需二次查价”.
Keep output concise and decision-oriented.

MCP Preset Config

Embedded preset: references/mcp-client-config.json
Endpoint: https://mcp.aigohotel.com/mcp (streamable_http + prefilled Authorization header)

来源信息

Source: github
Repository: openclaw/skills
Path: skills/cnchenkai/daolv-hotel-search
Version: —
Commit: c8f19dadf96de0ad7389446c99fb704b1ee53c4a
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Daolv Hotel Search

Focus on fast discovery and clean shortlist output.

## Workflow

1. Capture search intent
- Extract destination, check-in date, nights, guests, budget, star range, distance preference, amenities, brand preferences.
- Ask only minimal missing questions.

2. Prime tags once
- Call `ai-go-hotel.getHotelSearchTags` once per task.
- Map user intent into `hotelTags.requiredTags` / `preferredTags` / `excludedTags`.

3. Run hotel search
- Call `ai-go-hotel.searchHotels` with required fields:
  - `originQuery`, `place`, `placeType`
- Include optional fields when available:
  - `checkInDate`, `stayNights`, `adultCount`, `countryCode`, `starRatings`, `distanceInMeter`, `size`, `hotelTags`
- Prefer `size=10` first, then narrow to top 3-5.

4. Return shortlist only
- Output: 1 recommended + 2 alternatives.
- Include quick trade-off: price / location / amenities / star.
- Do not deep-dive room plans unless user asks to proceed to booking.

## Output Template

- **搜索条件**：地点 / 日期 / 人数 / 预算 / 偏好
- **首选酒店**：价格区间、位置、标签命中、推荐理由
- **备选 1-2**：同结构
- **下一步**：是否进入“房型与退改细节”阶段

## Quality Bar

- Use concrete numbers when available.
- Use `price.lowestPrice` + `price.currency` (price is object).
- If price missing/null, mark as “需二次查价”.
- Keep output concise and decision-oriented.

## MCP Preset Config

- Embedded preset: `references/mcp-client-config.json`
- Endpoint: `https://mcp.aigohotel.com/mcp` (`streamable_http` + prefilled Authorization header)

搜索

daolv-hotel-search

安装与使用

技能说明

Daolv Hotel Search

Workflow

Output Template

Quality Bar

MCP Preset Config

来源信息

来源内容

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