Uses computer vision to automatically remove watermarks and logos from images.
Install
mkdir -p .claude/skills/gemini-logo-remover && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/360" && unzip -o skill.zip -d .claude/skills/gemini-logo-remover && rm skill.zipInstalls to .claude/skills/gemini-logo-remover
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Remove Gemini logos, watermarks, or AI-generated image markers using OpenCV inpainting. Use this skill when the user asks to remove Gemini logo, AI watermark, or any logo/watermark from images.Key capabilities
- →Remove logos or watermarks using OpenCV inpainting
- →Target specific image regions via pixel coordinates
- →Identify and remove watermarks from image corners
- →Process images for professional presentations
- →Save cleaned images to designated output directories
How it works
The skill uses OpenCV's Telea inpainting algorithm to fill in masked regions based on surrounding pixel data. It allows users to define the removal area either by explicit coordinates or by relative corner ratios.
Inputs & outputs
When to use gemini-logo-remover
- →Removing AI watermarks
- →Deleting unwanted brand logos
- →Cleaning image background noise
- →Processing images for presentations
About this skill
Gemini Logo Remover
Remove Gemini logos and watermarks from AI-generated images using inpainting.
Setup
pip install opencv-python numpy pillow --break-system-packages
Usage
By Coordinates
import cv2
import numpy as np
def remove_region(input_path, output_path, x1, y1, x2, y2, radius=5):
"""Remove rectangular region using inpainting."""
img = cv2.imread(input_path)
h, w = img.shape[:2]
mask = np.zeros((h, w), dtype=np.uint8)
cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
result = cv2.inpaint(img, mask, radius, cv2.INPAINT_TELEA)
cv2.imwrite(output_path, result)
# Example: remove region at coordinates
remove_region('/mnt/user-data/uploads/img.png',
'/mnt/user-data/outputs/clean.png',
x1=700, y1=650, x2=800, y2=720)
By Corner
def remove_corner_logo(input_path, output_path, corner='bottom_right',
w_ratio=0.1, h_ratio=0.1, padding=10):
"""Remove logo from corner. corner: top_left, top_right, bottom_left, bottom_right"""
img = cv2.imread(input_path)
h, w = img.shape[:2]
lw, lh = int(w * w_ratio), int(h * h_ratio)
coords = {
'bottom_right': (w - lw - padding, h - lh - padding, w - padding, h - padding),
'bottom_left': (padding, h - lh - padding, lw + padding, h - padding),
'top_right': (w - lw - padding, padding, w - padding, lh + padding),
'top_left': (padding, padding, lw + padding, lh + padding)
}
x1, y1, x2, y2 = coords[corner]
mask = np.zeros((h, w), dtype=np.uint8)
cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
result = cv2.inpaint(img, mask, 5, cv2.INPAINT_TELEA)
cv2.imwrite(output_path, result)
# Example: remove bottom-right logo
remove_corner_logo('/mnt/user-data/uploads/img.png',
'/mnt/user-data/outputs/no_logo.png',
corner='bottom_right', w_ratio=0.08, h_ratio=0.08)
Find Coordinates
img = cv2.imread(input_path)
h, w = img.shape[:2]
print(f"Size: {w}x{h}")
# Gemini 별 로고는 보통 이미지 우하단 모서리에서 약간 안쪽에 위치
# 일반적인 좌표: x1=w-150, y1=h-100, x2=w-130, y2=h-55
# 정확한 위치는 이미지마다 다르므로 조정 필요
Output
Always save to /mnt/user-data/outputs/ and use present_files tool.
Notes
- Inpainting works best for small areas with uniform backgrounds
- Gemini logo is typically in bottom-right corner
- Adjust coordinates/ratios based on actual logo position and size
When not to use it
- →When the watermark covers a large or complex area of the image
- →When the image background is highly textured or non-uniform
Prerequisites
Limitations
- →Inpainting performance degrades on large or complex backgrounds
- →Requires manual adjustment of coordinates for precise logo removal
How it compares
It provides a programmatic, repeatable way to clean specific image regions compared to manual editing tools.
Compared to similar skills
gemini-logo-remover side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| gemini-logo-remover (this skill) | 9 | 8mo | Review | Beginner |
| milimo-storyboard-analyst | 0 | 5mo | No flags | Advanced |
| data-engineering | 13 | 7mo | Review | Advanced |
| crawl4ai | 21 | 8mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by bear2u
View all by bear2u →You might also like
milimo-storyboard-analyst
mainza-ai
Expertise in the Milimo Video Storyboard pipeline, from script parsing (Regex vs AI via Gemma) to generating concept art thumbnails and handling the Smart Element Matching engine. Use this when debugging storyboard extraction, prompt generation for chained video chunks, or modifying the scene/shot h
data-engineering
pluginagentmarketplace
ETL pipelines, Apache Spark, data warehousing, and big data processing. Use for building data pipelines, processing large datasets, or data infrastructure.
crawl4ai
basher83
This skill should be used when users need to scrape websites, extract structured data, handle JavaScript-heavy pages, crawl multiple URLs, or build automated web data pipelines. Includes optimized extraction patterns with schema generation for efficient, LLM-free extraction.
data-cleaning-pipeline
aj-geddes
Build robust processes for data cleaning, missing value imputation, outlier handling, and data transformation for data preprocessing, data quality, and data pipeline automation
pdf-processing-pro
davila7
Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environments, processing large volumes of PDFs, or requiring robust error handling and validation.
paddle-ocr-validation
jgtolentino
PaddleOCR-based receipt and BIR form extraction with validation