A smart decision-making assistant for Dianping that prioritizes practical store vetting and review authenticity over simple star ratings.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install dianping
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 dianping 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 Dianping AI Skill?
The Dianping skill for Openclaw Skills transforms how users navigate China's leading local life platform. Instead of blindly trusting inflated star scores, this skill implements a sophisticated vetting process that analyzes review quality, identifies recent complaints, and evaluates the authenticity of high-rated stores. It is designed to help users find restaurants, cafes, and local services that truly match their specific use cases, such as business meetings, quiet solo dining, or photogenic social gatherings.
By integrating a specialized browser workflow, the skill can inspect live store pages and group-buying deals to provide objective conclusions. It filters out marketing noise to identify hidden risks like long queues, pushy sales tactics, or restrictive coupon conditions, ensuring users spend their time and money at merchants that offer genuine value.
Dianping AI Skill Use Cases
- Vetting high-rated restaurants to distinguish between genuine quality and paid reviews.
- Comparing multiple dining or service options to find the best fit for specific social scenarios.
- Evaluating group-buying packages (Tuan-gou) to identify hidden restrictions or poor value.
- Identifying suitable work environments by filtering cafes for noise levels and power outlet availability.
- Spotting red flags in service-oriented businesses like KTVs, massage parlors, or hair salons.
How Dianping AI Skill Works
- The agent identifies the specific user intent, such as finding a restaurant, service, or analyzing a deal.
- It triggers a browser-based workflow to capture real-time data from public store or deal pages.
- The skill prioritizes reading recent 1-2 star reviews and photo reviews to establish a realistic baseline of the merchant.
- It compares the captured data against category baselines (e.g., understanding that a 4.5 in hotpot differs from a 4.5 in a boutique cafe).
- It generates a structured decision report including a final conclusion, supporting reasons, and specific risk alerts.
Dianping AI Skill Setup
To integrate this capability into your agent, ensure your environment supports browser-based interactions and add the Dianping configuration to your skill manifest:
# Install the skill into your Openclaw environment
openclaw install skill-dianping
# Configure the browser commerce base for live inspection
openclaw configure browser-commerce-base --platform dianping
Dianping AI Skill Data Schema & Taxonomy
The skill organizes findings into a structured decision-making taxonomy to avoid information overload:
| Data Point | Description |
|---|---|
| Conclusion | A definitive recommendation: Worth visiting, Proceed with caution, or Not recommended. |
| Rationale | 2-4 key facts derived from deep review analysis. |
| Risk Alerts | Identification of friction points like queues, hidden fees, or appointment requirements. |
| Recommendation Rank | A prioritized list when comparing multiple candidates (e.g., A > B > C). |
| Category Signal | Classification of the store as "Social Media/Photogenic" vs. "Repeat Visit/Practical." |
Dianping AI Skill Advanced Features
- Review Trap Detection: Specialized logic to identify review manipulation and inflated scores.
- Multi-Agent Comparison: Capability to rank several store candidates simultaneously based on user-defined criteria.
- Deal Constraint Analysis: Automatically parses coupon fine print for holiday restrictions, reservation needs, and dine-in only rules.
- Contextual Matching: Weights signals differently depending on the goal (e.g., prioritizing "quiet" for work or "lighting" for photography).
SKILL.md
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