A specialized decision-support skill designed to help users evaluate travel booking trade-offs on Trip.com and Ctrip platforms.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install trip
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 trip 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 Trip Booking Decision Support?
The trip skill for Openclaw Skills acts as a strategic advisor for navigating complex travel decisions on platforms like Trip.com and Ctrip. It is specifically engineered to analyze public travel-booking factors—such as price, flexibility, and timing—to help users make informed choices without requiring access to private account data. By focusing on the logic of travel commerce, it provides a structured framework for evaluating flights, hotels, and train journeys.
This skill is built for high-privacy, low-sensitivity interactions. It avoids the risks associated with browser automation or account-state actions, such as login or order retrieval, and instead focuses on providing the conceptual clarity needed to choose the right booking option. As part of the broader Openclaw Skills ecosystem, it ensures that users can objectively weigh the pros and cons of different travel products before proceeding to a final purchase.
Trip Booking Decision Support Use Cases
- Deciding between multiple flight options based on layover trade-offs and refund flexibility.
- Comparing hotel selections by evaluating public trust factors, location convenience, and fee structures.
- Understanding the logic behind train ticket classes and booking conditions on international travel platforms.
- Determining the best time to book based on public platform patterns and merchant reliability.
- Navigating complex booking scenarios where trade-offs between cost and convenience are unclear.
How Trip Booking Decision Support Works
- The AI identifies the specific travel booking or shopping need provided by the user.
- It analyzes the scenario against public decision-relevant factors like category fit, timing, and hidden fees.
- It cross-references internal guidance documents to apply consistent output patterns and travel logic.
- It evaluates trade-offs to determine the strongest choice based on the user's specific context.
- The skill generates a structured report highlighting the best option, the rationale, meaningful caveats, and practical next steps.
Trip Booking Decision Support Setup
To integrate this into your agent, place the skill definition in your designated skills directory and ensure the reference documents are available.
# Ensure the reference files exist in your local environment
ls references/booking-guide.md
ls references/output-patterns.md
# The skill can then be invoked by the agent when travel queries are detected.
Trip Booking Decision Support Data Schema & Taxonomy
The trip skill processes information through a structured logical flow rather than a database schema. It utilizes the following metadata taxonomy:
| Data Component | Description |
|---|---|
| Input Context | User scenarios, travel dates, destinations, or specific booking links. |
| Decision Factors | Price, flexibility, timing, fees, and merchant trust. |
| Output Structure | Fixed sections for Best Option, Why, Caveats, and Final Advice. |
| Reference Hooks | Links to booking-guide.md and output-patterns.md for logic consistency. |
| Privacy Policy | No local persistence, no cookie storage, and no account-state data retrieval. |
Trip Booking Decision Support Advanced Features
- Multi-platform logic support for both Trip.com and Ctrip booking environments.
- Contextual hand-off triggers that suggest when to switch to browser-based workflows for checkout.
- Risk-aware evaluation that prioritizes identifying booking caveats over superficial price comparisons.
- Standardized output patterns for consistent integration within multi-agent Openclaw Skills workflows.
- Conceptual decision support that functions independently of real-time account status or session cookies.
SKILL.md
Loading
Related Openclaw Skills
Fund Advisor
A comprehensive financial toolkit for fund investment simulations, Dollar Cost Averaging (DCA) calculations, and strategic asset allocation.
Nimble Scooter Integration
A comprehensive AI agent skill for interacting, diagnosing, and managing Nimble electric scooters via a unified interface.
ColorsCC
A high-performance, stateless color API and UI asset toolset designed for AI agents to generate placeholders, gradients, and themes without hallucination.
Filter
A dedicated rule-based filtering skill designed to eliminate information noise and preserve high-value signals in your workflows.
AI Potential Driver (PUA Breakthrough Mode)
A high-agency execution framework that drives OpenClaw agents to push past shallow results and achieve evidence-backed completion.
Kaito Content Search Primitive
A high-performance search primitive for AI agents to retrieve, filter, and synthesize real-time content from Twitter and global news sources.
Featured*
