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.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install cnchenkai/daolv-hotel-bookingExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
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/priceCes informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
# 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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