Use when you have a written implementation plan to execute in a separate session with review checkpoints
npx skills add michaelboeding/skills --skill "background-remove"
Install specific skill from multi-skill repository
# Description
>
# SKILL.md
name: background-remove
description: >
Remove backgrounds from images using AI. Triggers include:
"remove background", "transparent background", "cut out", "isolate subject",
"remove bg", "make transparent", "extract subject", "background removal"
Creates PNG or WebP images with transparent backgrounds.
Background Remove Skill
Remove backgrounds from images using AI (rembg/U2-Net) or built-in methods.
Output: PNG or WebP with transparent background.
Quick Examples
| User Says | What Happens |
|---|---|
| "Remove the background from this photo" | AI removes background, outputs PNG |
| "Make this image transparent" | Removes background, preserves subject |
| "Cut out the product from this image" | Isolates subject with clean edges |
| "Remove backgrounds from all images in /photos" | Batch processes multiple images |
| "Quick background removal, white background" | Uses fast built-in method |
Prerequisites
-
rembg- AI-based background removal (recommended)
bash pip install rembg # Or with GPU acceleration (faster, requires CUDA) pip install rembg[gpu] -
Pillow- Required for image processing
bash pip install Pillow
The first run will download the U2-Net model (~170MB) which is cached for future use.
Methods
| Method | Description | Best For |
|---|---|---|
| rembg | AI-based using U2-Net model | Complex images, photos, products (default) |
| builtin | White-to-transparent conversion | Icons, graphics with clean white backgrounds |
Workflow
Step 1: Gather Requirements (REQUIRED)
Use the AskUserQuestion tool for each question. Ask ONE question at a time.
Q1: Image Source
"Which image(s) should I remove the background from?
Please provide the file path or paste the image."
Wait for response.
Q2: Method (Optional)
"Which removal method?
- AI (rembg) - Best quality, works on any image (default)
- Built-in - Faster, best for white backgrounds"
Wait for response. Default to AI if user doesn't specify.
Q3: Output Location (Optional)
"Where should I save the result?
- Same location with
_nobgsuffix (default)- Custom path"
Wait for response.
Step 2: Execute Background Removal
Single image:
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i "/path/to/image.jpg" \
-o "/path/to/output.png"
Batch processing:
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i "/path/to/img1.jpg" "/path/to/img2.png" "/path/to/img3.webp" \
-o "/path/to/output_folder"
Using built-in method (faster for white backgrounds):
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i "/path/to/icon.png" \
-m builtin
Step 3: Deliver Result
- Show the result to the user
- Confirm the background was removed successfully
- Offer to:
- Process additional images
- Try a different method if quality isn't satisfactory
- Adjust output format (PNG vs WebP)
Script Parameters
| Parameter | Short | Description | Default |
|---|---|---|---|
--input |
-i |
Input image path(s) | Required |
--output |
-o |
Output path or directory | Auto-generated with _nobg suffix |
--method |
-m |
Removal method (rembg, builtin) | rembg |
Output Formats
The output format is determined by the file extension:
| Extension | Format | Notes |
|---|---|---|
.png |
PNG | Best quality, larger file (default) |
.webp |
WebP | Good compression, modern format |
Integration with Other Skills
This skill can be called by other skills that need background removal:
From Python (import)
import sys
sys.path.insert(0, "${SKILL_PATH}/skills/background-remove/scripts")
from background_remove import remove_background
result = remove_background("/path/to/image.png", "/path/to/output.png", method="rembg")
if result.get("success"):
print(f"Saved to: {result['file']}")
else:
print(f"Error: {result['error']}")
From Command Line
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i "/path/to/image.png" \
-o "/path/to/output.png" \
-m rembg
Error Handling
rembg not installed:
rembg not installed. Install with: pip install rembg[gpu] (or pip install rembg for CPU-only)
The script will automatically fall back to the built-in method.
Image not found:
Image not found: /path/to/image.png
Processing failed:
- Try a different method
- Check if the image file is corrupted
- Ensure sufficient memory for large images
Tips for Best Results
- Use rembg for photos - AI handles complex edges (hair, fur, transparent objects)
- Use builtin for graphics - Faster for icons/logos with clean white backgrounds
- Check edges - If edges are rough, the AI method usually gives better results
- Batch process - Process multiple images at once for efficiency
- GPU acceleration - Install
rembg[gpu]for faster processing on NVIDIA GPUs
Examples
Remove background from a photo
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i "product_photo.jpg" \
-o "product_transparent.png"
Batch process a folder
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i photos/*.jpg \
-o "transparent_photos/"
Fast removal for icons (white background)
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i "icon.png" \
-m builtin
Output as WebP (smaller file size)
python3 ${SKILL_PATH}/skills/background-remove/scripts/background_remove.py \
-i "photo.jpg" \
-o "result.webp"
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.