nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Hotel discovery, shortlist comparison, and booking handoff using the ai-go-hotel MCP server (getHotelSearchTags, searchHotels, getHotelDetail). Use when users ask to find hotels, compare options by budget/location/amenities, plan city stays, family or business lodging, or complete hotel booking decisions with clear next actions.
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-booking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。
Provide reliable hotel planning and booking support with structured MCP calls and decision-ready outputs.
ai-go-hotel.getHotelSearchTags once.hotelTags.requiredTags, preferredTags, excludedTags, and optional budget constraints.ai-go-hotel.searchHotels with:
place
placeType
originQuery
checkInDate, stayNights, adultCount, size, starRatings, hotelTags, countryCode, distanceInMeter, withHotelAmenities, language
size=8-12 for first pass; narrow to top 3-5 in final output.checkInDate invalid/past/empty may fallback to tomorrowprice is an object (use price.lowestPrice + price.currency)placeType can be normalized from user language:
ai-go-hotel.getHotelDetail (prefer hotelId when available).checkInDate / checkOutDate format YYYY-MM-DD.roomRatePlans can be very large; render only top rows by relevance/price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。
# Daolv Hotel Booking Provide reliable hotel planning and booking support with structured MCP calls and decision-ready outputs. ## Workflow 1. Capture booking intent before calling tools - Extract: destination, check-in date, nights, adults/children, room count, budget, purpose (business/family/leisure), required amenities, preferred/avoided brands. - If key constraints are missing, ask only the minimum follow-up questions. 2. Prime tags once per task - Call `ai-go-hotel.getHotelSearchTags` once. - Cache returned tags for the rest of the conversation. - Use those tags to build `hotelTags.requiredTags`, `preferredTags`, `excludedTags`, and optional budget constraints. 3. Search hotels with normalized parameters - Call `ai-go-hotel.searchHotels` with: - `place` - `placeType` - `originQuery` - optional `checkInDate`, `stayNights`, `adultCount`, `size`, `starRatings`, `hotelTags`, `countryCode`, `distanceInMeter`, `withHotelAmenities`, `language` - Prefer `size=8-12` for first pass; narrow to top 3-5 in final output. - Respect live schema behavior: - `checkInDate` invalid/past/empty may fallback to tomorrow - `price` is an object (use `price.lowestPrice` + `price.currency`) - some fields can be null or missing - `placeType` can be normalized from user language: - 城市/city → 城市 - 机场/airport → 机场 - 景点/attraction → 景点 - 火车站/railway station → 火车站 - 地铁站/metro → 地铁站 - 酒店/hotel → 酒店 4. Enrich finalists with room-level details - For each shortlisted option, call `ai-go-hotel.getHotelDetail` (prefer `hotelId` when available). - Pass dates with `checkInDate` / `checkOutDate` format `YYYY-MM-DD`. - Handle fallback and edge behavior: - invalid/empty dates may auto-correct - failures may return plain text (not structured JSON) - `roomRatePlans` can be very large; render only top rows by relevance/price - Extract actionable room/price data, cancellation policy, breakfast inclusion, and important constraints. 5. Return...
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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