A CLI-powered skill for high-accuracy image background removal and transparent PNG generation using BiRefNet and Reve models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install background-removal
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 background-removal 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 Background Removal?
This skill provides a robust interface for removing image backgrounds using the high-accuracy BiRefNet model via the inference.sh CLI. It enables developers to automate the creation of transparent PNGs for e-commerce, marketing, and design purposes without needing complex local setups. By leveraging Openclaw Skills, users can integrate professional-grade image processing into their automated agents or terminal-based workflows.
Beyond simple removal, the skill supports the Reve model for prompt-based background manipulation, allowing users to not only clear backgrounds but also replace them with specific scenes like beaches or studio settings. This makes it a versatile tool for any developer looking to streamline visual asset production.
Background Removal Use Cases
- E-commerce: Generating clean, white-background or transparent product photos for online stores.
- Portraits: Creating professional headshots for social media and corporate directories.
- Marketing: Rapidly producing design assets and cutouts for digital advertisements.
- Design Workflows: Extracting subjects from complex images for use in composite digital art.
- Automated Pipelines: Integrating background removal into larger image generation and editing chains.
How Background Removal Works
- The user authenticates with the inference.sh service through the CLI.
- An image URL is provided as input to either the BiRefNet model for pure removal or the Reve model for prompt-based editing.
- The inference.sh engine processes the request using high-performance AI models to segment the foreground from the background.
- For background removal, the skill generates a high-quality PNG with an alpha channel.
- The final asset URL is returned, which can then be used in subsequent steps or downloaded directly.
Background Removal Setup
First, install the required CLI and authenticate your account:
curl -fsSL https://cli.inference.sh | sh && infsh login
To add this capability to your agent, use the Openclaw Skills installer:
npx skills add inference-sh/skills@background-removal
Run a basic background removal command:
infsh app run infsh/birefnet --input '{"image_url": "https://your-photo.jpg"}'
Background Removal Data Schema & Taxonomy
The skill utilizes a structured JSON input schema to process image requests:
| Field | Type | Description |
|---|---|---|
image_url |
String | The public URL of the source image to be processed. |
prompt |
String | (Optional) Used with Reve to describe background changes (e.g., 'make it transparent' or 'beach background'). |
output |
JSON | The command returns a JSON object containing the URL to the processed PNG file. |
Background Removal Advanced Features
- Prompt-based Editing: Use natural language prompts via the Reve model to intelligently modify or replace backgrounds instead of just removing them.
- Workflow Integration: Chain this skill with FLUX generation skills to create an image and immediately remove its background for a seamless asset creation pipeline.
- High-Accuracy Segmentation: Utilizes the BiRefNet model, specifically optimized for high-precision edge detection in complex portraits and product shots.
- Mass Processing: Leverages the lightweight nature of Openclaw Skills to handle batch image processing via simple shell scripts.
SKILL.md
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