hotel
Local-first hotel decision engine for trip stays, hotel comparison, shortlist creation, booking readiness, and accommodation planning. Use whenever the user mentions hotels, where to stay, comparing properties, nights, location tradeoffs, budget, amenities, booking decisions, or choosing the best stay for a trip. Captures hotel options, stores trip context, scores tradeoffs, and surfaces the best-fit hotel based on budget, location, amenities, and decision confidence.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/agistack/hotelWhat This Skill Does
The Hotel skill is a local-first decision engine designed to transform the often overwhelming process of selecting accommodation into a structured, rational, and frictionless experience. By leveraging local memory storage, it allows OpenClaw users to track multiple trips, log hotel candidates, and evaluate them against explicit preferences and budget constraints. Rather than relying on external booking aggregators that may prioritize paid placement, this skill focuses on the user's specific requirements—such as proximity to transit, cancellation flexibility, or specific amenities like reliable Wi-Fi—to score properties objectively. It serves as a personal travel coordinator that maintains the state of your travel research, allowing you to build shortlists and perform 'booking readiness' checks without leaving your local environment.
Installation
To integrate this skill into your OpenClaw environment, execute the following command in your terminal:
clawhub install openclaw/skills/skills/agistack/hotel
Ensure that you have Python 3 installed as python3 on your system path. Upon first use, the skill will initialize its necessary directory structure at ~/.openclaw/workspace/memory/hotel/ to handle your data securely and locally.
Use Cases
- Complex Itineraries: Managing stay details for multi-city trips where budgeting and location tradeoffs are critical.
- Objective Comparison: Comparing properties by creating a quantified scoring system based on your own priority values.
- Preference Tracking: Saving recurring requirements (e.g., 'always requires late check-in') so the agent can filter out unsuitable candidates automatically.
- Decision Anxiety Reduction: Using the shortlist feature to narrow down excessive options into a manageable list of top contenders.
Example Prompts
- 'I'm heading to Tokyo from April 10th to 13th with a $450 budget. Can you set up a new trip and remind me to add hotels?'
- 'Compare the two hotels I added for my Paris trip and tell me which one is better for walkability and budget.'
- 'I need a hotel with free breakfast and flexible cancellation. Can you list the hotels that meet these requirements?'
Tips & Limitations
This skill is strictly local and offline-first, meaning it does not sync with real-time booking APIs or check live inventory prices. Users must manually input hotel data. It is highly recommended to use the save_preference script to define your standard requirements, which helps the agent provide more relevant recommendations during the comparison phase. Since the data is stored in JSON format locally, you can back up your memory folder to ensure trip research is never lost.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-agistack-hotel": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-write, file-read, code-execution
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