A comprehensive toolkit of pre-installed Python and C++ examples for AI inference, hardware control, and multimedia processing on the RDK X5 platform.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install rdk-x5-app
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 rdk-x5-app 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 RDK X5 App Pre-installed Demos?
The RDK X5 App skill provides a direct interface to the pre-installed example library located in the /app directory of the RDK X5 system. It serves as a foundational resource for developers to test BPU (Brain Processing Unit) capabilities, hardware peripherals, and multimedia pipelines right out of the box. By leveraging Openclaw Skills, users can quickly validate their hardware setup and explore optimized AI models without complex initial configuration.
This collection includes highly optimized Python AI demos, C++ multimedia workflows, and 40-pin GPIO interactions. Whether you are performing real-time object detection with YOLO or managing low-level hardware signals, this skill ensures you are using the correct system paths and dependencies for the RDK X5 architecture.
RDK X5 App Pre-installed Demos Use Cases
- Running pre-installed Python AI demos for image classification, semantic segmentation, and object detection.
- Testing 40-pin GPIO peripherals including I2C, SPI, PWM, and UART using Python scripts.
- Compiling and executing C++ multimedia samples for RTSP streaming, VIO capture, and BPU inference.
- Browsing and verifying the 34 pre-installed BPU models optimized for NV12 format.
- Setting up a local Web camera display server using Nginx for remote monitoring.
How RDK X5 App Pre-installed Demos Works
- Navigate to the specific demo directory within the /app path on the RDK X5 hardware.
- Ensure the execution environment uses the system Python 3.10 to maintain compatibility with pre-installed hardware libraries.
- Execute the target Python script for AI or GPIO tasks, or use the provided Makefiles to compile C++ source code.
- Access output data via terminal logs, web-based visual feeds on port 8080, or physical hardware interactions.
RDK X5 App Pre-installed Demos Setup
To use this skill effectively, you must operate within the RDK X5 system environment. Openclaw Skills integration depends on the hobot_dnn libraries found in the system Python path.
# Always use the system Python 3.10 to avoid ModuleNotFoundError
/usr/bin/python3.10 /app/pydev_demo/01_basic_sample/test_resnet18.py
# For C++ multimedia demos, ensure development headers are present
sudo apt install libhbm-dev
cd /app/cdev_demo/vio2display
make && ./vio2display
RDK X5 App Pre-installed Demos Data Schema & Taxonomy
The skill interacts with a structured directory hierarchy designed for RDK X5 optimization:
| Directory Path | Purpose | Key Components |
|---|---|---|
| /app/pydev_demo/ | Python AI Samples | 12 demos covering YOLO, SSD, and Centernet |
| /app/pydev_demo/models/ | BPU Model Repository | 34 optimized .bin models (Classification, Detection, Segmentation) |
| /app/40pin_samples/ | Hardware Interfacing | Python scripts for GPIO, PWM, and Serial communication |
| /app/cdev_demo/ | C++ Multimedia Demos | Compiled examples for RTSP, V4L2, and BPU inference |
| /app/multimedia_samples/ | Low-level C Examples | Underlying VIO and encoding hardware samples |
RDK X5 App Pre-installed Demos Advanced Features
- Multi-version YOLO support including optimized inference for YOLOv5 through YOLOv12n.
- Integrated Web UI deployment via Nginx for real-time camera stream visualization.
- High-performance RTSP stream decoding and display capabilities for network-based video processing.
- Direct BPU acceleration for semantic segmentation using models like DeepLabV3+ and FastSCNN.
- Comprehensive hardware abstraction samples for all 40-pin header functions, including SPI and I2C.
SKILL.md
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