A comprehensive AI image processing toolset for automated background removal, facial clarity enhancement, and AI-generated background replacement.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tukeli-visual-api
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install tukeli-visual-api using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Tukeli Visual API skill provides advanced computer vision capabilities directly to your AI agent environment. By leveraging the Tukeli REST API, this skill enables seamless image matting (removing backgrounds from portraits or objects), face enhancement for restoring low-quality photos, and sophisticated AI-driven background generation based on text prompts. As part of the broader ecosystem of Openclaw Skills, it empowers developers to build complex visual workflows without needing deep expertise in neural networks or GPU management.
This skill supports both local file uploads and image URLs, returning results as binary streams or Base64 encoded strings. It is designed for high-precision tasks such as hair-level portrait segmentation and professional-grade image restoration, making it an essential tool for automated media production.
To get started with this skill, follow these installation steps:
TUKELI_API_KEY=your_secret_api_key
pip install -r scripts/requirements.txt
python scripts/tukeli.py --api matting --image your_photo.jpg --output result.png
The skill organizes processed assets and tracking information as follows:
| Data Type | Specification |
|---|---|
| Supported Inputs | PNG, JPG, JPEG, BMP, GIF, WEBP |
| Output Directory | data/outputs/ |
| File Naming | {api_type}_{timestamp}.png |
| Metadata Storage | .meta.json file for every processed image |
The metadata includes critical taxonomy such as API endpoint used, processing duration, original file size, and the specific parameters applied during the transformation.
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