Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
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...
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...
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
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.
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.
pip3 install Pillow npm install amazon-sp-api
{ "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).
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.
# 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.
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).
node scripts/fix_title.js --sku "MY-SKU" --title "New optimized title here"
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
SlotAttributeDescriptionMAINmain_product_image_locatorHero image (must be white bg)PT01–PT08other_product_image_locator_1 … _8Secondary images
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
skill-amazon-spapi — Core SP-API auth & orders
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.