According to AlphaMoat, as of 2026-09, Image Utils has 3,596 downloads and 5 stars — ranked #3,030 of 5,078 AI skills overall, and #278 of 512 in Design & Media.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 92 | 3.2K | 3 | - |
| SkillHub ↗ | 175 | 3.6K | 5 | — |
Platforms count differently and update at different times.
The documentation lists resizing, cropping, compositing, format conversion, watermarks, brightness and contrast adjustments, and web optimization. Processing changes image files, so confirm inputs, preserve originals, and inspect output dimensions and watermarks; load URLs only from trusted public sources.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 3.6K | — | 5 |
| 2026-08 | 3.2K | +6.9% | 5 |
| 2026-07 | 3.0K | +9.4% | 5 |
| 2026-06 | 2.8K | +6.9% | 4 |
| 2026-05 | 2.6K | — | 4 |
| 2026-03 | 1.9K | — | 3 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: image-utils description: Classic image manipulation with Python Pillow - resize, crop, composite, format conversion, watermarks, brightness/contrast adjustments, and web optimization. Use this skill when post-processing AI-generated images, preparing images for web delivery, batch processing image directories, creating responsive image variants, or performing any deterministic pixel-level image operation. Works standalone or alongside bria-ai for post-processing generated images. license: MIT metadata: author: Bria AI version: "1.3.4" --- # Image Utilities
Pillow-based utilities for deterministic pixel-level image operations. Use for resize, crop, composite, format conversion, watermarks, and other standard image processing tasks.
## When to Use This Skill
- **Post-processing AI-generated images**: Resize, crop, optimize for web after generation
- **Format conversion**: PNG ↔ JPEG ↔ WEBP with quality control
- **Compositing**: Overlay images, paste subjects onto backgrounds
- **Batch processing**: Resize to multiple sizes, add watermarks
- **Web optimization**: Compress and resize for fast delivery
- **Social media preparation**: Crop to platform-specific aspect ratios
## When NOT to Use This Skill — Use `bria-ai` Instead
This skill handles **deterministic pixel-level operations** only. For any **generative or AI-powered** image work, use the `bria-ai` skill instead:
- **Generating images from text prompts** → use `bria-ai`
- **AI background removal or replacement** → use `bria-ai`
- **AI image editing (inpainting, object removal/addition)** → use `bria-ai`
- **Style transfer or AI-driven visual effects** → use `bria-ai`
- **Creating product lifestyle shots with AI** → use `bria-ai`
- **Image upscaling with AI super-resolution** → use `bria-ai`
**Rule of thumb**: If the task requires *creating new visual content* or *understanding image semantics*, use `bria-ai`. If the task requires *transforming existing pixels* (resize, crop, format convert, watermark), use this skill.
If `bria-ai` is not available, install it with:
```bash
npx skills add bria-ai/bria-skill
```
## Quick Reference
| Operation | Method | Description |
|-----------|--------|-------------|
| **Loading** | `load(source)` | Load from URL, path, bytes, or base64 |
| | `load_from_url(url)` | Download image from URL |
| **Saving** | `save(image, path)` | Save with format auto-detection |
| | `to_bytes(image, format)` | Convert to bytes |
| | `to_base64(image, format)` | Convert to base64 string |
| **Resizing** | `resize(image, width, height)` | Resize to exact dimensions |
| | `scale(image, factor)` | Scale by factor (0.5 = half) |
| | `thumbnail(image, size)` | Fit within size, maintain aspect |
| **Cropping** | `crop(image, left, top, right, bottom)` | Crop to region |
| | `crop_center(image, width, height)` | Crop from center |
| | `crop_to_aspect(image, ratio)` | Crop to aspect ratio |
| **Compositing** | `paste(bg, fg, position)` | Overlay at coordinates |
| | `composite(bg, fg, mask)` | Alpha composite |
| | `fit_to_canvas(image, w, h)` | Fit onto canvas size |
| **Borders** | `add_border(image, width, color)` | Add solid border |
| | `add_padding(image, padding)` | Add whitespace padding |
| **Transforms** | `rotate(image, angle)` | Rotate by degrees |
| | `flip_horizontal(image)` | Mirror horizontally |
| | `flip_vertical(image)` | Flip vertically |
| **Watermarks** | `add_text_watermark(image, text)` | Add text overlay |
| | `add_image_watermark(image, logo)` | Add logo watermark |
| **Adjustments** | `adjust_brightness(image, factor)` | Lighten/darken |
| | `adjust_contrast(image, factor)` | Adjust contrast |
| | `adjust_saturation(image, factor)` | Adjust color saturation |
| | `blur(image, radius)` | Apply Gaussian blur |
| **Web** | `optimize_for_web(image, max_size)` | Optimize for delivery |
| **Info** | `get_info(image)` | Get dimensions, format, mode |
## Requirements
```bash
pip install Pillow requests
```
## Basic Usage
```python
from image_utils import ImageUtils
# Load from URL
image = ImageUtils.load_from_url("https://example.com/image.jpg")
# Or load from various sources
image = ImageUtils.load("/path/to/image.png") # File path
image = ImageUtils.load(image_bytes) # Bytes
image = ImageUtils.load("data:image/png;base64,...") # Base64
# Resize and save
resized = ImageUtils.resize(image, width=800, height=600)
ImageUtils.save(resized, "output.webp", quality=90)
# Get image info
info = ImageUtils.get_info(image)
print(f"{info['width']}x{info['height']} {info['mode']}")
```
## Resizing & Scaling
```python
# Resize to exact dimensions
resized = ImageUtils.resize(image, width=800, height=600)
# Resize maintaining aspect ratio (fit within bounds)
fitted = ImageUtils.resize(image, width=800, height=600, maintain_aspect=True)
# Resize by width only (height auto-calculated)
resized = ImageUtils.resize(image, width=800)
# Scale by factor
half = ImageUtils.scale(image, 0.5) # 50% size
double = ImageUtils.scale(image, 2.0) # 200% size
# Create thumbnail
thumb = ImageUtils.thumbnail(image, (150, 150))
```
## Cropping
```python
# Crop to specific region
cropped = ImageUtils.crop(image, left=100, top=50, right=500, bottom=350)
# Crop from center
center = ImageUtils.crop_center(image, width=400, height=400)
# Crop to aspect ratio (for social media)
square = ImageUtils.crop_to_aspect(image, "1:1") # Instagram
wide = ImageUtils.crop_to_aspect(image, "16:9") # YouTube thumbnail
story = ImageUtils.crop_to_aspect(image, "9:16") # Stories/Reels
# Control crop anchor
top_crop = ImageUtils.crop_to_aspect(image, "16:9", anchor="top")
bottom_crop = ImageUtils.crop_to_aspect(image, "16:9", anchor="bottom")
```
## Compositing
```python
# Paste foreground onto background
result = ImageUtils.paste(background, foreground, position=(100, 50))
# Alpha composite (foreground must have transparency)
result = ImageUtils.composite(background, foreground)
# Fit image onto canvas with letterboxing
canvas = ImageUtils.fit_to_canvas(
image,
width=1200,
height=800,
background_color=(255, 255, 255, 255), # White
position="center" # or "top", "bottom"
)
```
## Format Conversion
```python
# Convert to different formats
png_bytes = ImageUtils.to_bytes(image, "PNG")
jpeg_bytes = ImageUtils.to_bytes(image, "JPEG", quality=85)
webp_bytes = ImageUtils.to_bytes(image, "WEBP", quality=90)
# Get base64 for data URLs
base64_str = ImageUtils.to_base64(image, "PNG")
data_url = ImageUtils.to_base64(image, "PNG", include_data_url=True)
# Returns: "data:image/png;base64,..."
# Save with format auto-detected from extension
ImageUtils.save(image, "output.png")
ImageUtils.save(image, "output.jpg", quality=85)
ImageUtils.save(image, "output.webp", quality=90)
```
## Watermarks
```python
# Text watermark
watermarked = ImageUtils.add_text_watermark(
image,
text="© 2024 My Company",
position="bottom-right", # bottom-left, top-right, top-left, center
font_size=24,
color=(255, 255, 255, 128), # Semi-transparent white
margin=20
)
# Logo/image watermark
logo = ImageUtils.load("logo.png")
watermarked = ImageUtils.add_image_watermark(
image,
watermark=logo,
position="bottom-right",
opacity=0.5,
scale=0.15, # 15% of image width
margin=20
)
```
## Adjustments
```python
# Brightness (1.0 = original, <1 darker, >1 lighter)
bright = ImageUtils.adjust_brightness(image, 1.3)
dark = ImageUtils.adjust_brightness(image, 0.7)
# Contrast (1.0 = original)
high_contrast = ImageUtils.adjust_contrast(image, 1.5)
# Saturation (0 = grayscale, 1.0 = original, >1 more vivid)
vivid = ImageUtils.adjust_saturation(image, 1.3)
grayscale = ImageUtils.adjust_saturation(image, 0)
# Sharpness
sharp = ImageUtils.adjust_sharpness(image, 2.0)
# Blur
blurred = ImageUtils.blur(image, radius=5)
```
## Transforms
```python
# Rotate (counter-clockwise, degrees)
rotated = ImageUtils.rotate(image, 45)
rotated = ImageUtils.rotate(image, 90, expand=False) # Don't expand canvas
# Flip
mirrored = ImageUtils.flip_horizontal(image)
flipped = ImageUtils.flip_vertical(image)
```
## Borders & Padding
```python
# Add solid border
bordered = ImageUtils.add_border(image, width=5, color=(0, 0, 0))
# Add padding (whitespace)
padded = ImageUtils.add_padding(image, padding=20) # Uniform
padded = ImageUtils.add_padding(image, padding=(10, 20, 10, 20)) # left, top, right, bottom
```
## Web Optimization
```python
# Optimize for web delivery
optimized_bytes = ImageUtils.optimize_for_web(
image,
max_dimension=1920, # Resize if larger
format="WEBP", # Best compression
quality=85
)
# Save optimized
with open("optimized.webp", "wb") as f:
f.write(optimized_bytes)
```
## Integration with Bria AI
Use alongside the **[bria-ai skill](https://clawhub.ai/galbria/bria-ai)** to post-process AI-generated images. Generate or edit images with Bria's API, then use image-utils for resizing, cropping, watermarking, and web optimization.
```python
import requests
from image_utils import ImageUtils
# Generate with Bria AI (see bria-ai skill for full API reference)
response = requests.post(
"https://engine.prod.bria-api.com/v2/image/generate",
headers={"api_token": BRIA_API_KEY, "Content-Type": "application/json"},
json={"prompt": "product photo of headphones", "aspect_ratio": "1:1", "sync": True}
)
image_url = response.json()["result"]["image_url"]
# Download and post-process
image = ImageUtils.load_from_url(image_url)
# Create multiple sizes for responsive images
sizes = {
"large": ImageUtils.resize(image, width=1200),
"medium": ImageUtils.resize(image, width=600),
"thumb": ImageUtils.thumbnail(image, (150, 150))
}
# Save all as optimized WebP
for name, img in sizes.items():
ImageUtils.save(img, f"product_{name}.webp", quality=85)
```
## Batch Processing Example
```python
from pathlib import Path
from image_utils import ImageUtils
def process_catalog(input_dir, output_dir):
"""Process all images in a directory."""
output_path = Path(output_dir)
output_path.mkdir(exist_ok=True)
for image_file in Path(input_dir).glob("*.{jpg,png,webp}"):
image = ImageUtils.load(image_file)
# Crop to square
square = ImageUtils.crop_to_aspect(image, "1:1")
# Resize to standard size
resized = ImageUtils.resize(square, width=800, height=800)
# Add watermark
final = ImageUtils.add_text_watermark(resized, "© My Brand")
# Save optimized
output_file = output_path / f"{image_file.stem}.webp"
ImageUtils.save(final, output_file, quality=85)
process_catalog("./raw_images", "./processed")
```
## API Reference
See [image_utils.py](./references/code-examples/image_utils.py) for complete implementation with docstrings.
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.