Skill Amazon Listing Optimizer

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name: skill-listing-image-optimizer description: "Audit Amazon product listing images for non-square dimensions, auto-pad them to 2000×2000 white background, and push corrected images to live listings via SP-API. Works with any marketplace and seller account." metadata: openclaw: requires: { bins: ["node", "python3"] }

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Amazon Listing Image Optimizer

Automatically fix non-square product images on Amazon listings — download, pad to 2000×2000 white background, and push back to live listings via SP-API. No manual Seller Central work required.

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Why This Exists

Amazon penalizes listings with non-square images (aspect ratio != 1:1). Common offenders:

Landscape 16:9 or 4:3 product shots

Portrait hero images

Tiny low-resolution images

This skill detects, fixes, and re-uploads — all automatically.

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Setup

1. Install dependencies

pip3 install Pillow
npm install amazon-sp-api

2. Create SP-API credentials file

{
  "lwaClientId": "amzn1.application-oa2-client.YOUR_CLIENT_ID",
  "lwaClientSecret": "YOUR_CLIENT_SECRET",
  "refreshToken": "Atzr|YOUR_REFRESH_TOKEN",
  "region": "eu",
  "marketplace": "YOUR_MARKETPLACE_ID",
  "sellerId": "YOUR_SELLER_ID"
}

Set AMAZON_SPAPI_PATH env var to point to it (default: ./amazon-sp-api.json).

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Scripts

audit.js — Detect non-square images

node scripts/audit.js --sku "MY-SKU"          # audit single SKU
node scripts/audit.js --all                    # audit all FBA SKUs
node scripts/audit.js --all --out report.json  # save report

Outputs: list of non-conforming image slots with dimensions.

pad_to_square.py — Fix images locally

# After audit.js downloads originals to ./image_fix/
python3 scripts/pad_to_square.py ./image_fix/

Pads all *_orig.jpg files to 2000×2000 white background, outputs *_fixed.jpg.

push_images.js — Upload fixed images to Amazon

node scripts/push_images.js --dir ./image_fix/ --sku "MY-SKU" --slots PT03,PT05

Spins up a local HTTP server on a public port, submits image URLs to SP-API, then auto-kills the server after 15 minutes (time for Amazon to crawl).

fix_title.js — Patch listing title

node scripts/fix_title.js --sku "MY-SKU" --title "New optimized title here"

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Full Pipeline (one command)

node scripts/audit.js --all --out report.json
python3 scripts/pad_to_square.py ./image_fix/
node scripts/push_images.js --dir ./image_fix/ --from-report report.json

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Image Slot Reference

| Slot | Attribute | Description | |------|-----------|-------------| | MAIN | main_product_image_locator | Hero image (must be white bg) | | PT01–PT08 | other_product_image_locator_1 … _8 | Secondary images |

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Notes

Amazon processes image updates within 15–30 mins of ACCEPTED response

VPS must have a publicly accessible IP/port for the temp HTTP server (or use S3/Cloudflare)

PIL uses LANCZOS resampling for best quality when resizing

Keep images under 10MB; target 2000×2000px @ 95% JPEG quality

Related

skill-amazon-spapi — Core SP-API auth & orders

1. Install dependencies

pip3 install Pillow
npm install amazon-sp-api

2. Create SP-API credentials file

{
  "lwaClientId": "amzn1.application-oa2-client.YOUR_CLIENT_ID",
  "lwaClientSecret": "YOUR_CLIENT_SECRET",
  "refreshToken": "Atzr|YOUR_REFRESH_TOKEN",
  "region": "eu",
  "marketplace": "YOUR_MARKETPLACE_ID",
  "sellerId": "YOUR_SELLER_ID"
}

Set AMAZON_SPAPI_PATH env var to point to it (default: ./amazon-sp-api.json).

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Amazon processes image updates within 15–30 mins of ACCEPTED response

VPS must have a publicly accessible IP/port for the temp HTTP server (or use S3/Cloudflare)

PIL uses LANCZOS resampling for best quality when resizing

Keep images under 10MB; target 2000×2000px @ 95% JPEG quality