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ecommerce-material-studio

Generates e-commerce product images with category detection, scene compositing, text overlay, and multi-platform adaptation.

3.4k|487|Updated Jul 25, 2025
Search Tags:#image generation#pillow#compositing#taobao#product images#Batch processing#E-commerce
One-click install
npx skills add https://github.com/davepoon/buildwithclaude --skill ecommerce-material-studio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ecommerce-material-studio
Source: https://github.com/davepoon/buildwithclaude/tree/main/plugins/all-skills/skills/ecommerce-material-studio
Command: npx skills add https://github.com/davepoon/buildwithclaude --skill ecommerce-material-studio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Pillow, numpy, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Creating e-commerce product images (main images, detail images, scene composites) for platforms like Taobao, Kuaishou, Douyin, and JD requires repetitive manual design work across many sizes and styles. This Skill automates the full pipeline from a raw product photo to a packaged, platform-ready asset bundle.

Core Features & Use Cases

  • Category Detection & Style Matching: Analyzes a product image's colors, aspect ratio, and material with Pillow to identify its category and recommend scene templates from a 12-category template library.
  • Scene-Aware Compositing & Text Overlay: Composites product cutouts into lifestyle scenes with scale/perspective awareness, then renders brand logos, guarantee bars, and selling-point text with z-index and contrast handling.
  • Quality Check, Platform Adaptation & Delivery: Runs automated quality checks, resizes outputs for 7 Chinese e-commerce platforms, and packages everything into a zip with a usage guide and asset inventory.
  • Use Case: Given a white-background photo of a shaver, produce a complete set of main images and detail images adapted for Kuaishou and Taobao, with brand overlay, quality validation, and a delivery zip.

Quick Start

Ask the AI to generate a full set of e-commerce main and detail images for your product photo, adapted for your target platforms and packaged for delivery.

Frequently Asked Questions about ecommerce-material-studio

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

FAQPage Schema
How do I generate e-commerce main images from a product photo?▼

Run the pipeline starting with category_detector.py on your product image, then style_matcher.py to pick a template, composite into a scene with scene_aware_compositor.py, and render text with text_engine.py. Finish with quality_check.py and platform_adapter.py for platform-specific sizes.

What Python libraries are needed for e-commerce image generation?▼

The pipeline requires Pillow for image processing, plus numpy and scipy for the scene-aware compositing module. Install them once with pip install Pillow numpy scipy before running any scripts.

Which e-commerce platforms does the image adapter support?▼

The platform_adapter.py script supports seven platforms: Taobao, Kuaishou, Xiaohongshu, Douyin, Pinduoduo, JD, and WeChat Shop. It resizes, crops, and compresses images to each platform's required dimensions and file size limits.

Why does Chinese text rendering fail in the text engine?▼

text_engine.py requires a Chinese font file that may not exist at the default path on your system. Edit the FONT_PATHS_BOLD and FONT_PATHS_REGULAR constants to point to PingFang.ttc on macOS, NotoSansCJK on Linux, or msyh.ttc on Windows.

Can I batch process multiple products with resume support?▼

Yes, batch_processor.py accepts a JSON product list and tracks status in a state file. It supports resuming interrupted batches, retrying failed products, and generating a summary report of completed and failed items.

How do I add my own brand logo and colors to product images?▼

Copy references/brand_config_template.json to create your own brand profile with logo paths, color palette, and guarantee bar labels. Load it with brand_loader.py, which automatically selects the right logo variant for dark or light scenes.

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