A powerful ALPR integration for AI agents to identify license plates, vehicle makes, and models through automated workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install plate-recognizer
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 plate-recognizer 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 Plate Recognizer?
Plate Recognizer is a sophisticated cloud-based automatic license plate recognition (ALPR) service designed for high-accuracy identification of vehicles. By utilizing this integration within the Openclaw Skills ecosystem, developers can seamlessly process image and video data to extract license plate numbers, vehicle makes, and models. This skill bridges the gap between raw visual data and actionable intelligence for automated systems.
The integration is powered by Membrane, which simplifies the developer experience by handling the complex authentication plumbing and credential refreshing automatically. This allows AI agents to interact with Plate Recognizer data without managing local secrets or manually tracking API tokens, ensuring a secure and efficient connection to ALPR services.
Plate Recognizer Use Cases
- Automating parking lot entry and exit by recognizing authorized license plates.
- Enhancing security systems with real-time alerts for specific vehicle makes or models.
- Monitoring traffic patterns and vehicle flow in smart city applications.
- Integrating vehicle data into existing CRM or property management workflows.
How Plate Recognizer Works
- The AI agent initializes a connection to the Plate Recognizer service via the Openclaw Skills framework and Membrane CLI.
- User authentication is performed once through a secure browser-based flow, establishing a persistent connection ID.
- The agent searches for specific actions (like plate recognition or vehicle identification) using natural language intent discovery.
- When an image or video is processed, the agent executes the selected action, passing parameters through a standardized JSON interface.
- Plate Recognizer processes the media and returns structured data including plate text, confidence scores, and vehicle attributes back to the agent.
Plate Recognizer Setup
First, install the Membrane CLI to enable Openclaw Skills functionality:
npm install -g @membranehq/cli@latest
Next, authenticate your agent (replace <agentType> with openclaw):
membrane login --tenant --clientName=openclaw
Create a connection to the Plate Recognizer service:
membrane connection ensure "https://platerecognizer.com/" --json
If the state is BUILDING, wait for it to become READY:
npx @membranehq/cli connection get <id> --wait --json
Plate Recognizer Data Schema & Taxonomy
The skill organizes interaction through connections and structured JSON schemas. Data is primarily managed via the following taxonomy:
| Object | Description |
|---|---|
| Connection | Stores the authentication state and base URL for the Plate Recognizer API. |
| Action | Defines specific tasks like recognize-plate with defined inputSchema and outputSchema. |
| Proxy Request | Allows for raw API access to endpoints like /statistics/ or /cameras/ via the Membrane proxy. |
| Metadata | Includes vehicle make, model, color, and license plate region codes. |
Plate Recognizer Advanced Features
- Natural language action discovery to find the best tools for specific recognition tasks.
- Support for headless environments with manual authorization code completion.
- Transparent credential refreshing for long-running automated surveillance tasks.
- Direct API proxying with automatic header injection for custom endpoints not covered by standard actions.
- Multi-agent support compatible with Openclaw Skills, Claude, and other AI IDEs.
SKILL.md
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