| slug | muapi-amazon-product-listing |
| name | muapi-amazon-product-listing |
| version | 1.0.0 |
| description | Generate a complete Amazon product listing image set — hero image, lifestyle shot, infographic with features, and comparison/detail closeups optimized for Amazon standards. |
| acceptLicenseTerms | true |
Amazon Product Listing Pack
Generate a complete Amazon product listing image set — hero image, lifestyle shot, infographic with features, and comparison/detail closeups optimized for Amazon standards.
Inputs
| Name | Type | Required | Default | Description |
|---|
product_name | text | yes | — | The product name (e.g. "Stainless Steel Water Bottle 32oz"). |
product_category | text | yes | — | Amazon category (e.g. "Kitchen & Dining", "Sports & Outdoors", "Electronics"). |
key_features | text | yes | — | Comma-separated top features to highlight (e.g. "leak-proof lid, BPA-free, keeps cold 24h, fits cupholder"). |
target_buyer | text | no | general consumer | Who buys this (e.g. "athletes", "busy moms", "office workers aged 25-45"). |
product_image | image_url | no | — | Optional existing product photo to use as base reference. |
Steps
Submit a SINGLE the plan with all steps running in parallel.
All 4 Images (Parallel)
-
Hero image (white background) — muapi image generate (model=ai-product-photography) if {{product_image}} provided, else muapi image generate (model=gpt4o-text-to-image):
- Prompt:
Professional Amazon main listing hero image of {{product_name}}. Pure white background #FFFFFF. Product centered, perfectly lit with soft studio lighting, no shadows. High resolution, commercial product photography, sharp focus on all details, 2000x2000px equivalent quality.
- Aspect ratio: 1:1
-
Lifestyle/context shot — muapi image generate (model=ai-product-shot) if {{product_image}} provided, else muapi image generate (model=nano-banana-pro):
- Prompt:
Amazon lifestyle image of {{product_name}} being used by {{target_buyer}} in a natural setting. {{product_category}} product in real-life use context. Warm natural lighting, aspirational but relatable, slight bokeh background. Commercial lifestyle photography, professional quality.
- Aspect ratio: 1:1
-
Feature infographic — muapi image generate (model=gpt4o-text-to-image):
- Prompt:
Amazon product detail page infographic for {{product_name}}. Shows product with 4-5 callout arrows highlighting these key features: {{key_features}}. Clean white or light grey background, professional typography, bold feature labels with icons. Amazon A+ content style, feature benefit layout, commercial design.
- Aspect ratio: 1:1
-
Closeup detail shot — muapi image generate (model=nano-banana-pro):
- Prompt:
Extreme closeup macro product detail shot of {{product_name}} — focus on premium materials, texture, quality craftsmanship. Studio lighting, white background, ultra sharp focus, demonstrates quality. Amazon product detail image showing materials/finish.
- Aspect ratio: 1:1
After generation:
- Present all 4 images in order (main > lifestyle > infographic > detail)
- Suggest uploading to canvas to arrange as a listing mockup
- Offer to generate 3 additional A+ content module images
Notes
- Amazon requires main image on pure white background — enforce this strictly.
- Key features should be visually distinct and scannable in the infographic.
- For electronics, add "showing ports, buttons, and connections clearly" to the detail shot.
Trigger Keywords
amazon listing, amazon product, product listing, ecommerce listing, amazon images, product photography amazon, listing images
Notes for the Executing Agent
- This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call
muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
- For model IDs without a CLI alias yet, fall back to the raw endpoint via
curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
- Substitute
{{input_name}} placeholders with the user's actual inputs before issuing each call.
