flat-illustration-asset-parser

Name: flat-illustration-asset-parser
Availability: InStock
Author: steelan9199

Parse flat-style illustrations into structured Markdown tables for SAM3 segmentation.

9|3|Updated Feb 15, 2026

Search Tags:#prompt engineering #segmentation #illustration #sam3 #painting #asset parsing

Ready to run

npx skills add https://github.com/steelan9199/wechat-publisher --skill flat-illustration-asset-parser

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: flat-illustration-asset-parser
Source: https://github.com/steelan9199/wechat-publisher/tree/main/skills/flat-illustration-asset-parser
Command: npx skills add https://github.com/steelan9199/wechat-publisher --skill flat-illustration-asset-parser

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the manual labor of segmenting flat-style illustrations for AI-assisted image processing, ensuring precise asset extraction and logical painting sequences for educational or creative workflows.

Core Features & Use Cases

SAM3 Prompt Generation: Automatically generates isolated, high-precision English prompts for Segment Anything Model 3, avoiding common semantic errors.
Logical Painting Sequence: Provides a step-by-step drawing order based on physical occlusion, ideal for teaching children or manual illustration.
Color Mapping: Enforces a strict 10-color palette with intelligent collision resolution to ensure visual clarity in flat designs.

Quick Start

Upload a flat illustration image and ask the skill to generate the SAM3 extraction table and painting steps.

Frequently Asked Questions about flat-illustration-asset-parser

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

FAQPage Schema

How do I generate SAM3 prompts for segmenting flat illustrations?▼

To generate SAM3 prompts for flat illustrations, parse the image into a structured Markdown table that strips relative semantic descriptors and enforces strict color palette constraints. This produces isolated, high-precision English prompts for Segment Anything Model 3.

What is the logical painting sequence for flat design asset extraction?▼

Logical painting sequence for flat design asset extraction defines a step-by-step drawing order based on physical occlusion. It organizes visual assets into a layer-by-layer structure, which is ideal for teaching manual illustration or guiding educational painting workflows.

How do I enforce a strict color palette when parsing flat illustrations?▼

Enforcing a strict color palette when parsing flat illustrations involves mapping assets to a fixed 10-color range with intelligent collision resolution. This ensures visual clarity and prevents color overlap in the generated segmentation tables.

Can I use this asset parsing method for teaching children how to paint?▼

Yes, you can use this asset parsing method for teaching children how to paint. It provides a logical, step-by-step drawing order based on physical occlusion, making it easy to follow manual illustration workflows and educational creative tasks.

Why does SAM3 prompt generation fail with relative semantic descriptors?▼

SAM3 prompt generation fails with relative semantic descriptors because they introduce ambiguity in asset parsing. Stripping these descriptors and enforcing strict color palette constraints ensures high-precision prompt generation and accurate segmentation of flat-style illustrations.

Do I need any specific dependencies to convert flat illustrations into SAM3-ready tables?▼

No specific dependencies are required to convert flat illustrations into SAM3-ready tables. You simply upload your flat-style visual asset, and the parser outputs structured Markdown tables defining layer-by-layer drawing orders and color-mapped properties.