A high-performance computer vision and image processing toolkit powered by OpenCV WebAssembly for AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install opencv
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 opencv 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 OpenCV Computer Vision?
The OpenCV Computer Vision skill provides a robust suite of image manipulation and analysis tools delivered through WebAssembly. By leveraging the openclaw-wasm-sandbox, this skill allows AI agents to perform complex visual tasks locally and securely. It is a critical component of the Openclaw Skills ecosystem for developers needing to bridge the gap between LLM reasoning and programmatic image understanding.
This skill enables a wide range of capabilities from basic transformations like resizing and blurring to advanced computer vision techniques such as feature extraction and deep learning-based object detection. It is designed to work seamlessly within your existing automation pipelines, providing the technical foundation for any vision-centric AI agent.
OpenCV Computer Vision Use Cases
- Automating image preprocessing such as resizing, cropping, and noise reduction for dataset preparation.
- Implementing real-time object detection using YOLO or Haar cascades within Openclaw Skills workflows.
- Extracting and matching image features for visual search or document verification.
- Creating panoramic views by stitching multiple images together automatically.
- Enhancing photo quality through sharpening, stylization, and detail enhancement filters.
- Performing camera calibration and pose estimation for spatial computing tasks.
- Executing batch image format conversions and statistical analysis across large directories.
How OpenCV Computer Vision Works
- The AI agent determines the need for image processing and identifies the relevant operation within the Openclaw Skills library.
- The system checks for the presence of the opencv-component.wasm file in the local skills directory.
- The agent invokes the wasm-sandbox-run tool, specifying the path to the OpenCV WebAssembly binary.
- Arguments defining the operation (e.g., resize, dnn, or panorama) and file paths are passed into the secure sandbox.
- The openclaw-wasm-sandbox executes the OpenCV logic on the local machine, ensuring high performance without external API calls.
- Processed image data or analysis results are returned to the agent to trigger the next step in the workflow.
OpenCV Computer Vision Setup
Prerequisites
This skill requires the openclaw-wasm-sandbox plugin to be installed and active in your environment.
Install WASM Component
You must download the specific OpenCV WebAssembly component to your local Openclaw Skills storage:
openclaw wasm-sandbox download \
"https://raw.githubusercontent.com/guyoung/wasm-sandbox-openclaw-skills/main/opencv/files/opencv-component.wasm" \
"~/.openclaw/skills/opencv/files/opencv-component.wasm"
Configuration
Ensure your agent has permission to access the working directories where images are stored by configuring the workDir or mapDir settings in the sandbox runner.
OpenCV Computer Vision Data Schema & Taxonomy
The skill organizes its operations into several functional categories, facilitating structured data processing within Openclaw Skills:
| Category | Key Operations | Description |
|---|---|---|
| Image Process | resize, blur, sharpen, crop | Basic pixel-level manipulations and statistics. |
| Detection | dnn (YOLO), cascade, contour | Object, shape, and edge detection routines. |
| Features | orb, sift, match, template | Feature point extraction and image matching. |
| ML | train-knn, train-svm, predict | Local machine learning training and inference. |
| Batch | batch-process, batch-convert | High-efficiency multi-threaded bulk operations. |
Output files are typically generated in the same directory as the input or a specified output path within the sandbox environment.
OpenCV Computer Vision Advanced Features
- Support for Deep Neural Networks (DNN) allowing for modern object detection models like YOLO.
- Multi-threaded batch processing capabilities for handling massive image libraries efficiently within Openclaw Skills.
- Advanced image stitching and panorama generation using sophisticated feature matching algorithms.
- Built-in machine learning modules for training KNN and SVM models directly on visual features.
- Comprehensive camera calibration tools including chessboard detection and undistortion for precision optics tasks.
SKILL.md
Loading
Related Openclaw Skills
Legal Compliance
A specialized AI agent skill designed to generate essential legal document templates and compliance frameworks for digital startups.
To-Agent Service Designer
A specialized framework for designing, analyzing, and documenting products where AI agents are the primary operators rather than humans.
Marketing Execution Skill
A comprehensive full-stack marketing engine that automates website audits, content generation, SEO, and multi-channel campaign execution.
Prompt Hardening
A systematic framework to harden AI agent prompts, ensuring LLMs strictly adhere to instructions and constraints.
Landing Page Conversion Specialist
A professional tool for crafting high-conversion landing pages through a combination of direct-response copywriting and modern web development.
Improve Codebase Architecture
A professional-grade skill for identifying architectural friction and generating GitHub-ready refactoring RFCs through multi-agent design exploration.
Featured*
