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Tattoo Color Analyzer Skill

Analyze tattoo images to estimate laser removal sessions and difficulty.

2|2|Updated Jan 3, 2026
Search Tags:#ai analysis#image analysis#tattoo removal#color classification#session prediction#laser treatment
One-click install
npx skills add https://github.com/mebsites88s/tattoo-color-analyzer-v2-ai --skill tattoo-color-analyzer-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Tattoo Color Analyzer Skill
Source: https://github.com/mebsites88s/tattoo-color-analyzer-v2-ai/tree/main
Command: npx skills add https://github.com/mebsites88s/tattoo-color-analyzer-v2-ai --skill tattoo-color-analyzer-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires opencv-python, numpy, scikit-learn, Pillow, torch, torchvision, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides AI-powered analysis of tattoo images to assess laser removal difficulty, estimate the number of sessions required, and identify optimal treatment parameters.

Core Features & Use Cases

  • AI-Powered Analysis: Utilizes deep learning for tattoo segmentation and session prediction.
  • Color Extraction & Difficulty Scoring: Identifies dominant ink colors and assigns a removal difficulty score (1-10).
  • Session Estimation: Predicts a range of sessions needed for removal based on color, skin type, and tattoo characteristics.
  • Use Case: A tattoo removal clinic can use this Skill to quickly provide clients with an estimated treatment plan and cost, improving client consultation efficiency.

Quick Start

Analyze the tattoo image located at /path/to/tattoo.jpg using the analyze_tattoo tool.

Frequently Asked Questions about Tattoo Color Analyzer Skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does AI tattoo analysis estimate laser removal sessions?▼

AI tattoo analysis estimates laser removal sessions by segmenting tattoo regions with deep learning, extracting dominant ink colors, and predicting treatment counts based on color composition, Fitzpatrick skin type, and tattoo characteristics.

How do I analyze a tattoo image to assess removal difficulty in Python?▼

To assess tattoo removal difficulty, use the analyze_tattoo tool in Python to process your tattoo image, which automatically segments the tattoo and assigns a removal difficulty score from 1 to 10.

Do I need PyTorch and OpenCV to run AI tattoo image analysis?▼

Yes, you need Python with PyTorch and OpenCV installed, along with numpy, scikit-learn, and Pillow, to perform AI tattoo image analysis, color classification, and session prediction.

What is the best way to classify tattoo ink colors for laser treatment prediction?▼

The best way to classify tattoo ink colors for laser treatment is using AI image analysis that extracts dominant colors, classifies them, and uses the color composition to predict the required removal sessions.

Can Fitzpatrick skin type affect tattoo removal session prediction?▼

Yes, Fitzpatrick skin type directly affects tattoo removal session prediction because the AI analysis incorporates skin type alongside ink color classification and tattoo characteristics to estimate the total treatment sessions.

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