A local-first decision engine for comparing hotels, managing trip stay tradeoffs, and reaching booking readiness without external APIs.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hotel
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install hotel using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Hotel Decision Engine?
The Hotel Decision Engine is a specialized tool within the Openclaw Skills ecosystem designed to eliminate the friction of accommodation planning. By focusing on a local-first architecture, it allows users to capture hotel options, store trip context, and score tradeoffs regarding budget, location, and amenities without requiring external sync or credentials.
This skill transforms vague stay planning into concrete decisions by making criteria explicit. It helps developers and travelers reduce booking regret by providing a structured framework to evaluate price, area convenience, and flexibility before committing to a reservation.
Hotel Decision Engine Use Cases
- Comparing multiple hotel candidates for a specific trip based on nightly price and location.
- Shortlisting the top 3 best-fit properties for a destination to simplify the final choice.
- Storing reusable preferences like walkability or flexible cancellation to apply across all future trips.
- Planning complex trip stays with specific budget constraints and date ranges.
- Assessing booking readiness by checking if a hotel meets all predefined trip requirements.
How Hotel Decision Engine Works
- Initialize the local storage environment to create the necessary JSON database structure on your machine.
- Create a Trip object by defining the destination, dates, and total budget constraints.
- Add Hotel candidates to the specific trip, including details like area, nightly price, and specific amenities.
- Save reusable Preferences that the engine uses to weight the importance of features like breakfast or quiet rooms.
- Run the comparison and shortlist scripts to evaluate tradeoffs and surface the highest-scoring accommodation options.
- Execute a booking check to verify if a property candidate is ready for final reservation.
Hotel Decision Engine Setup
To get started with this entry in the Openclaw Skills library, ensure you have Python 3 installed. No external packages are required.
# Initialize the local storage directory
python3 init_storage.py
# Create your first trip
python3 add_trip.py --destination "Tokyo" --check_in 2026-04-10 --check_out 2026-04-13 --budget_total 450
Hotel Decision Engine Data Schema & Taxonomy
The skill maintains data locally in the ~/.openclaw/workspace/memory/hotel/ directory using three primary JSON files:
| File | Description |
|---|---|
trips.json |
Stores destination, dates, budget, and trip purpose. |
hotels.json |
Contains property candidates, pricing, area notes, and refund policies. |
preferences.json |
Keeps track of global user preferences like 'breakfast: required'. |
Core Objects
- Trip: The primary container for stay planning (ID: TRP-XXXX).
- Hotel: Individual property entries linked to a Trip ID.
- Preference: Key-value pairs used to influence the scoring engine.
Hotel Decision Engine Advanced Features
- Local-first architecture ensuring total privacy with no external API dependencies or credentials.
- Custom scoring logic that evaluates location tradeoffs and budget alignment automatically.
- Multi-trip management allowing users to track stays for various destinations simultaneously.
- Booking Readiness Check which acts as a final validation gate before the user leaves the CLI to book.
- Seamless integration with the broader suite of Openclaw Skills for automated travel research.
SKILL.md
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