An AI-driven inspection tool that uses Ezviz camera captures and intelligent agents to monitor restaurant hygiene, fire safety, and staff compliance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hsa-test
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 hsa-test 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 Ezviz Restaurant Inspection?
The Ezviz Restaurant Inspection skill is a sophisticated automation tool designed for the food and beverage industry. By integrating directly with Ezviz hardware and AI analysis engines, it allows business owners to conduct remote, automated audits of their premises. This skill addresses critical operational challenges such as maintaining hygiene standards, ensuring fire safety, and verifying staff adherence to health protocols like mask-wearing.
Built to leverage Openclaw Skills, this tool automates the entire lifecycle of a digital inspection—from fetching secure access tokens and triggering camera snapshots to processing those images through advanced AI models. It provides a structured risk assessment, categorizing findings into high, medium, and low risks, ensuring that managers can prioritize urgent safety issues like unattended open flames in the kitchen.
Ezviz Restaurant Inspection Use Cases
- Monitoring kitchen fire safety by detecting unattended open flames.
- Verifying floor hygiene to prevent slip-and-fall accidents caused by spills or debris.
- Ensuring staff compliance with health regulations, such as proper mask-wearing.
- Standardizing quality control across multiple chain restaurant locations remotely.
- Automating garbage management by checking if waste bins are properly sealed.
How Ezviz Restaurant Inspection Works
- The system automatically exchanges your appKey and appSecret for a secure Ezviz accessToken.
- It sends a capture command to specified device serial numbers to grab real-time images from the restaurant.
- The captured image URLs are passed to the Ezviz AI Intelligent Agent along with custom analysis prompts.
- The AI analyzes the visual data based on predefined safety and hygiene criteria.
- A comprehensive JSON report and console summary are generated, detailing risk levels and specific findings for each device.
Ezviz Restaurant Inspection Setup
First, ensure you have the required Python library installed:
pip install requests
Configure your environment variables with your Ezviz developer credentials:
export EZVIZ_APP_KEY="your_app_key"
export EZVIZ_APP_SECRET="your_app_secret"
export EZVIZ_DEVICE_SERIAL="dev1,dev2"
export EZVIZ_AGENT_ID="your_agent_id"
Run the inspection script:
python3 scripts/restaurant_inspection.py
Ezviz Restaurant Inspection Data Schema & Taxonomy
The skill organizes its inspection data into a structured format for easy integration into management dashboards.
| Data Point | Description | Format |
|---|---|---|
| Inspection Result | Categorized list of high-risk, general, and compliant items. | JSON Object |
| Risk Level | Assessment of severity (High, Medium, Low). | String |
| Image URL | Temporary link to the captured evidence. | URL (String) |
| Device Metadata | Serial number and channel information. | String |
Ezviz Restaurant Inspection Advanced Features
- Multi-device support: Inspect dozens of cameras across different locations in a single execution.
- Custom AI Prompts: Tailor the inspection focus (e.g., focus on 'HACCP standards' or 'Dining Area Cleanliness') via environment variables.
- Automatic Token Management: Features built-in logic to refresh 7-day access tokens without manual intervention.
- Per-Channel Targeting: Ability to specify unique camera channels for multi-lens hardware (e.g.,
dev1:1, dev2:2). - High-Risk Alerting: Specifically flags 'unattended fire' scenarios as high-priority warnings in the output summary.
SKILL.md
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