An intelligent decision-support tool for evaluating Qunar travel bookings by weighing public trade-offs, pricing, and flexibility.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install qunar
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 qunar 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 Qunar Decision Support?
The Qunar skill for AI agents is a specialized decision-support framework designed to help users navigate the complexities of travel booking on the Qunar platform. It functions as a strategic advisor that analyzes public data to compare flights, hotels, and travel packages. By focusing on public decision-relevant factors rather than account-specific data, it provides an objective layer of analysis for Openclaw Skills users.
This skill is built for transparency and reliability, ensuring users understand the relationship between price, flexibility, and service quality. It explicitly avoids sensitive operations like account logins or cookie handling, making it a secure choice for high-level travel planning and sourcing guidance.
Qunar Decision Support Use Cases
- Comparing budget flight options against premium carriers to determine if cost savings justify potential flexibility risks.
- Evaluating hotel booking conditions on Qunar to find the best balance between cancellation policies and price.
- Deciding between different booking providers on the platform based on public trust factors and fee structures.
- Sourcing high-level travel advice for complex multi-leg journeys before moving to a checkout phase.
How Qunar Decision Support Works
- The skill identifies the specific shopping or booking scenario provided by the user, such as a flight route or hotel stay.
- It analyzes public factors including category fit, timing, fees, and conditions to assess the value proposition.
- It synthesizes a trade-off analysis, explaining the logic behind the strongest current choice.
- It generates a structured report highlighting the best option, supporting reasons, potential caveats, and practical verification steps.
Qunar Decision Support Setup
To integrate this skill into your environment, ensure you have the Openclaw Skills framework installed and follow these steps:
# Navigate to your skills directory
cd openclaw-skills/library
# Clone or add the qunar skill definition
# Ensure references/booking-guide.md and references/output-patterns.md are present
No API keys or login credentials are required as this skill operates exclusively on public decision-support logic.
Qunar Decision Support Data Schema & Taxonomy
The skill utilizes a structured output format and references internal documentation to maintain quality standards:
| Component | Description |
|---|---|
references/ |
Contains booking-guide.md and output-patterns.md for logic consistency. |
Decision Logic |
Focuses on public metadata (fees, trust, timing) rather than local DB persistence. |
Output Structure |
Standardized headers: Best Option, Why, Caveats, and Final Advice. |
State Management |
Stateless; does not store cookies, session data, or account-linked info. |
Qunar Decision Support Advanced Features
- Multi-agent compatibility for broader travel research workflows.
- Integration with specialized booking references to refine decision-making algorithms.
- Automated clarification loops that trigger if a user's travel request is too broad for a precise recommendation.
- Support for Openclaw Skills standardized output patterns to ensure consistent formatting across different travel agents.
SKILL.md
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