An intelligent travel companion that automates rental car discovery and real-time inventory querying through the Fliggy Mtop API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install rent-skill
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 rent-skill 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 Fliggy Rental Car Assistant?
This Openclaw Skills integration serves as a specialized AI agent for the Fliggy Rental Car ecosystem. It streamlines the vehicle search process by interpreting natural language travel requirements—such as destination, duration, and passenger count—and translating them into precise API queries. By bridging the gap between user intent and real-time inventory, the skill provides a seamless, developer-friendly way to browse and book rental cars within an AI-driven workflow.
The skill excels at handling complex parameters like city code mapping, timestamp conversion, and multi-dimensional filtering. Whether you are looking for specific luxury brands or cost-effective fuel-efficient models, this assistant optimizes the search results to ensure users find the best match for their specific itinerary.
Fliggy Rental Car Assistant Use Cases
- Finding 7-seat MPVs for large family vacations in major cities like Beijing or Shanghai.
- Automated price comparison for electric vehicles versus gasoline cars for business trips.
- Real-time inventory monitoring for specific brands like Tesla or BYD during peak travel seasons.
- Integrating car rental discovery into broader travel-planning AI agents using Openclaw Skills.
How Fliggy Rental Car Assistant Works
- Collects travel metadata including destination city, pickup/return dates, and number of passengers via interactive dialogue.
- Converts user-provided city names into standardized city codes (e.g., Beijing to P_V1_487161).
- Logic-based seat capacity calculation where passenger counts are mapped to vehicle categories (e.g., 5-6 people trigger 7-seat recommendations).
- Executes a call to the mtop.trip.tfrcs.rentcar.queryItems API with structured parameters and filter tags.
- Filters and ranks the API response based on total price, provider score, and brand preferences.
- Generates a Markdown-formatted recommendation list with deep-links to the Fliggy OTA booking pages.
Fliggy Rental Car Assistant Setup
To deploy this skill, you must configure your AI agent to communicate with the Fliggy Mtop endpoint.
# Example command to register the skill in an Openclaw Skills environment
claw skill add fliggy-rental-car --version 1.0.0
Ensure that the environment has access to the city mapping database and the necessary API credentials to sign Mtop requests. The skill expects the user to provide inputs that can be parsed into timestamps and city codes.
Fliggy Rental Car Assistant Data Schema & Taxonomy
The skill utilizes a structured data format for interacting with the rental car inventory. Below are the key data points handled:
| Attribute | Type | Description |
|---|---|---|
carUseDate |
Integer | Pickup time as a millisecond timestamp |
searFilterTags |
Array | JSON string array for filtering (e.g., fuelGroup_1, seat_5) |
price.single |
Number | Daily rental rate in CNY |
price.finalSummary |
Number | Total estimated cost including fees and insurance |
otaKey |
String | Unique identifier used to generate the final booking URL |
Fliggy Rental Car Assistant Advanced Features
- Intelligent brand priority: Automatically boosts preferred brands (e.g., NIO, Tesla) to the top of the recommendation list.
- Dynamic filter tag generation: Supports complex filtering for vehicle age, energy types (EV/Hybrid), and specific car features like 360-degree cameras.
- Inventory fallback logic: Suggests alternative cities or vehicle classes if the primary search returns no results.
- Automated OTA link construction: Builds complex deep-links with context persistence to ensure the user lands on the correct merchant selection page.
SKILL.md
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