Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Generate professional e-commerce product photography and videos using EachLabs AI models. Product shots, background replacement, lifestyle scenes, and 360-degree views. Use when the user needs product images for e-commerce or marketing.
Generate professional e-commerce product photography and videos using EachLabs AI models. Product shots, background replacement, lifestyle scenes, and 360-degree views. Use when the user needs product images for e-commerce or marketing.
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. 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. Summarize what changed and any follow-up checks I should run.
Generate professional product photography, e-commerce visuals, and product videos using EachLabs AI models.
Header: X-API-Key: <your-api-key> Set the EACHLABS_API_KEY environment variable. Get your key at eachlabs.ai.
TaskModelSlugProduct photoshootProduct to Photoshootproduct-to-photoshootProduct on modelProduct Photo to Modelshootproduct-photo-to-modelshootColor variationsProduct Colorsproduct-colorsFood photographyFood Photosfood-photosBackground removalProduct Background Removerproduct-backround-removerImage upscalingProduct Photo Upscalerproduct-photo-upscalerHome scene placementProduct Home Viewproduct-home-viewProduct shotBria Product Shotbria-product-shotProduct shootProduct Shootproduct-shootProduct arc shotEachlabs Product Arc Shoteachlabs-product-arc-shot-v1Product zoom inEachlabs Product Zoom Ineachlabs-product-zoom-in-v1
TaskModelSlugProduct photographyGPT Image v1.5gpt-image-v1-5-text-to-imageBackground replacementGPT Image v1.5 Editgpt-image-v1-5-editProduct editingFlux 2 Turbo Editflux-2-turbo-editMulti-angle viewsQwen Image Editqwen-image-edit-2511-multiple-anglesBackground removalRembg Enhancerembg-enhanceBackground removalEachlabs BG Removereachlabs-bg-remover-v1Image upscalingEachlabs Upscaler Proeachlabs-image-upscaler-pro-v1Ad inpaintingSDXL Ad Inpaintsdxl-ad-inpaintCustom product styleZ Image Trainerz-image-trainerProduct videoPixverse v5.6 Image-to-Videopixverse-v5-6-image-to-video
Check model GET https://api.eachlabs.ai/v1/model?slug=<slug> β validates the model exists and returns the request_schema with exact input parameters. Always do this before creating a prediction to ensure correct inputs. POST https://api.eachlabs.ai/v1/prediction with model slug, version "0.0.1", and input matching the schema Poll GET https://api.eachlabs.ai/v1/prediction/{id} until status is "success" or "failed" Extract output URL from response
curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "gpt-image-v1-5-edit", "version": "0.0.1", "input": { "prompt": "Place this product on a clean white background with soft studio lighting and subtle shadows", "image_urls": ["https://example.com/product.jpg"], "background": "opaque", "quality": "high" } }'
curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "gpt-image-v1-5-edit", "version": "0.0.1", "input": { "prompt": "Place this coffee mug on a cozy wooden desk in a modern home office with warm morning light, lifestyle photography", "image_urls": ["https://example.com/mug.jpg"], "quality": "high" } }'
curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "qwen-image-edit-2511-multiple-angles", "version": "0.0.1", "input": { "image_urls": ["https://example.com/product.jpg"], "horizontal_angle": 45, "vertical_angle": 15, "zoom": 5 } }' Generate multiple angles by running separate predictions with different horizontal_angle values (0, 45, 90, 135, 180, 225, 270, 315 for a full 360).
curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "gpt-image-v1-5-edit", "version": "0.0.1", "input": { "prompt": "Remove the background from this product image", "image_urls": ["https://example.com/product.jpg"], "background": "transparent", "output_format": "png" } }'
curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "pixverse-v5-6-image-to-video", "version": "0.0.1", "input": { "image_url": "https://example.com/product-studio.jpg", "prompt": "Slow cinematic camera rotation around the product with dramatic studio lighting", "duration": "5", "resolution": "1080p" } }'
Specify lighting: "soft studio lighting", "dramatic side lighting", "natural window light" Mention surface: "marble surface", "wooden table", "clean white background" Include shadows: "soft shadows", "reflection on surface" Add context: "lifestyle setting", "in-use shot", "flat lay arrangement" For batch catalog shots, maintain consistency with similar prompts
For catalog-scale processing, create multiple predictions in parallel by sending separate POST requests for each product. Poll each prediction independently.
See the eachlabs-image-generation and eachlabs-video-generation references for complete model parameters.
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.