An automated pipeline to audit Amazon listings for non-square images, pad them to 2000x2000 white backgrounds, and re-upload via SP-API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-listing-optimizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install skill-listing-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Amazon Listing Image Optimizer?
This Openclaw Skills package addresses the critical need for Amazon sellers to maintain perfectly square product images. Amazon frequently penalizes listings that use landscape or portrait aspect ratios. This tool provides a professional-grade solution by combining Node.js for API interaction and Python with the Pillow library for high-fidelity image processing.
By leveraging this skill, developers and sellers can eliminate manual Seller Central image uploads. It programmatically detects non-conforming visual assets, applies white padding to reach a standard 1:1 ratio, and manages the temporary hosting required for Amazon's SP-API to crawl and update the live listings. This ensures consistent listing quality across entire catalogs with minimal effort.
Amazon Listing Image Optimizer Use Cases
- Automatically audit all FBA listings for non-square or low-resolution images.
- Batch process product photos to meet the 2000x2000 pixel white background requirement.
- Bulk upload corrected images to multiple Amazon marketplaces simultaneously.
- Patch listing titles and visual assets programmatically without manual data entry.
How Amazon Listing Image Optimizer Works
- Run the audit script to identify SKUs with images that do not meet the 1:1 aspect ratio.
- Download the non-conforming images to a local processing directory.
- Execute the Python image processor which uses LANCZOS resampling to pad images to a 2000x2000 white background.
- Initialize a temporary local HTTP server to make the processed images accessible to Amazon's crawlers.
- Send a feed submission to the Amazon SP-API containing the URLs of the optimized images.
- Monitor the processing report until Amazon confirms the images have been updated on the live site.
Amazon Listing Image Optimizer Setup
Prerequisites
Ensure you have Node.js and Python 3 installed. This Openclaw Skills utility requires the Pillow library and the Amazon SP-API node wrapper.
pip3 install Pillow
npm install amazon-sp-api
Authentication
Create a file named amazon-sp-api.json in your root directory:
{
\"lwaClientId\": \"YOUR_CLIENT_ID\",
\"lwaClientSecret\": \"YOUR_CLIENT_SECRET\",
\"refreshToken\": \"YOUR_REFRESH_TOKEN\",
\"region\": \"eu\",
\"marketplace\": \"YOUR_MARKETPLACE_ID\",
\"sellerId\": \"YOUR_SELLER_ID\"
}
Amazon Listing Image Optimizer Data Schema & Taxonomy
The skill manages data across local directories and JSON reports to track optimization progress:
| Data Component | Format | Purpose |
|---|---|---|
| image_fix/ | Directory | Local storage for original and fixed (padded) JPEG files. |
| report.json | JSON | Audit output listing SKUs, non-square slots, and current dimensions. |
| SP-API Credentials | JSON | Secure storage for LWA tokens and marketplace identifiers. |
| Image Slots | Metadata | Mapping for MAIN, PT01-PT08 attributes in the Amazon catalog. |
Amazon Listing Image Optimizer Advanced Features
- Professional LANCZOS resampling for superior image quality during resizing.
- Automated HTTP server lifecycle management with an auto-kill timer for security.
- Full pipeline execution through a single command sequence for mass catalog optimization.
- Integrated support for updating product titles alongside image improvements using Openclaw Skills scripts.
- Cross-marketplace compatibility with support for EU, US, and other SP-API regions.
SKILL.md
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