Ecommerce Selling Point Images
When to Use
Use this skill when the user wants marketplace-ready ecommerce selling point images from a real product photo. Typical requests include:
- Create Amazon, Taobao, Tmall, Temu, Shopee, TikTok Shop, Xiaohongshu, Pinduoduo, JD, or cross-border product feature images.
- Turn one original product photo into a visual selling point image with text overlays, benefit callouts, usage scenes, or platform styling.
- Use optional reference images as style, layout, scene, or competitor inspiration while preserving the user’s actual product.
- Generate localized product feature images in Chinese, English, Japanese, Korean, or another requested target language.
The workflow is optimized for one image per request or a small set of variations. For a full Tmall/Taobao listing set with main images and detail pages, prefer the broader tmall-product-images skill. Use $gpt-image-2 as the image generation/editing engine for actual files.
- Required: one original product image. Preserve the product’s shape, color, material, logo placement, packaging, proportions, and distinctive details.
- Optional: one or more reference images. Use them as style, layout, scene, lighting, typography, or competitor-analysis references while preserving the original product.
$gpt-image-2 image editing supports multiple images through images; keep the original product image first and then reference images in the order the user provided or in the order most relevant to the requested output.
- Optional: selling point description. If absent, infer only visible product facts and ask one focused question when the missing selling point would materially change the image.
- Optional: target marketplace or platform. Default to a general clean ecommerce style if unspecified.
- Optional: target language for on-image text. Default to the user’s conversation language.
- Optional: aspect ratio or output size. Default to square
1024x1024; use 1536x1024 for landscape and 1024x1536 for portrait unless the user asks for a supported smaller draft size.
- Optional: output count. Default to 1. Pass
n only when the user asks for multiple variations.
- Optional: output format and quality. Default to PNG and
quality: "high" for deliverable ecommerce images.
Execution
Use $gpt-image-2 for the actual image operation. This skill provides the ecommerce strategy, prompt structure, input ordering, and compliance constraints; $gpt-image-2 provides the execution path.
- Follow
$gpt-image-2 image editing mode.
Because this workflow always starts from an original product image, use GPT Image Editing through fusion-api.openai_image_edit_async_submit, as documented by $gpt-image-2. Do not run new oo capability discovery during normal use.
- Upload local images that must be passed to
$gpt-image-2:
oo file upload "<product-image-path>" --json
oo file upload "<reference-image-path>" --json
Pass each returned downloadUrl as an image reference in images, using the $gpt-image-2 shape:
{"image_url":"<downloadUrl>"}
Put the original product image first, followed by reference images.
- Build a concise prompt with this structure:
Create a marketplace-ready ecommerce selling point image.
Primary product: preserve the product from image 1 exactly, including shape, proportions, color, material, packaging, labels, and logo placement.
Reference usage: use images 2+ only for style, layout, background, lighting, composition, typography, or scene inspiration; do not copy unrelated products, brands, watermarks, or text.
Marketplace: <platform or general ecommerce>.
Target language for visible text: <language>.
Selling point: <user-provided selling point, or conservative visible/inferred benefit>.
Composition: product-first, clean commercial layout, readable mobile-safe typography, clear visual hierarchy, enough whitespace, no fake platform badges.
Avoid: QR codes, phone numbers, social handles, external URLs, competitor marks, fake certificates, fake discounts, unsupported absolute claims, distorted product details, unreadable text, watermark.
When the user did not provide a selling point, avoid inventing proof-dependent claims. Use visible, low-risk benefits such as material appearance, compactness, storage, comfort, organization, portability, or usage context only when supported by the image.
- Execute with
$gpt-image-2.
The underlying command shape is:
oo connector run "fusion-api" \
--action "openai_image_edit_async_submit" \
--data @payload.json \
--json
Example payload:
{
"model": "gpt-image-2",
"prompt": "Create a marketplace-ready ecommerce selling point image...",
"images": [
{"image_url": "<product-downloadUrl>"},
{"image_url": "<optional-reference-downloadUrl>"}
],
"output_format": "png",
"quality": "high",
"size": "1024x1024"
}
Use the exact field names from $gpt-image-2: output_format, quality, size, and optional n. If using a JSON file, write valid JSON with those field names. oo connector run --json usually waits internally and returns completed image results directly; if it returns a handle, follow $gpt-image-2 polling guidance.
Result Handling
Read GPT Image 2 results according to $gpt-image-2:
- Image URLs are usually in
.data.data[].url.
- Revised prompts may be in
.data.data[].revised_prompt.
- Returned metadata may include
.data.size, .data.quality, .data.output_format, .data.model, and .data.usage.
Download each returned HTTP image URL with oo file download "<url>" "<output-dir>" --name "<fileNameWithoutExtension>" --ext "<png|jpeg|webp|jpg>". oo file download prints Saved to: <path> and does not support --json.
- Use a clear local output directory such as
/Users/yunshi/Downloads/ecommerce-selling-point-images/<short-product-name>-<timestamp>/.
- Name files predictably, for example
selling-point-01.png, selling-point-02.png, etc.
- Preview the generated image when practical, or return the local file path and a short note describing platform, language, aspect ratio, and selling point used.
- If the task returns structured result data instead of direct URLs, report the actual returned fields and do not invent file URLs.
Failure Handling
- Missing original product image: stop and ask for one product image.
- Too many reference images:
$gpt-image-2 supports multiple edit images, but if the set is noisy, redundant, or too large for a useful prompt, select the most relevant references and summarize the rest in the prompt.
- Unsupported aspect ratio, format, size, quality, or count: use the nearest
$gpt-image-2 supported value and mention the adjustment.
- Missing selling point: either ask one focused question or use a conservative visible benefit; do not invent certifications, performance numbers, regulated claims, awards, warranties, or safety/health claims.
- Upload failure: report the exact file that failed and retry only after the path or network issue is resolved.
- Connector handle or timeout: follow
$gpt-image-2 result/polling guidance before rerunning. Do not start a duplicate task only because a wait window ended.
- GPT Image 2 connector failure or billing/auth blocker: report the exact blocker from oo output and the next useful action.
---
name: ecommerce-selling-point-images
description: 'Generate ecommerce selling point images from a product photo with optional reference images and a selling point description. Use when the user asks for Amazon, Taobao, Temu, Shopee, or marketplace-ready feature images with localized text and platform styling.'
metadata:
icon: "\U0001F5BC️"
title: Ecommerce Selling Point Images
companionSkill: gpt-image-2
packageName: '@zjxuyunshi/ecommerce-selling-point-images'
version: 0.0.1
---
# Ecommerce Selling Point Images
## When to Use
Use this skill when the user wants marketplace-ready ecommerce selling point images from a real product photo. Typical requests include:
- Create Amazon, Taobao, Tmall, Temu, Shopee, TikTok Shop, Xiaohongshu, Pinduoduo, JD, or cross-border product feature images.
- Turn one original product photo into a visual selling point image with text overlays, benefit callouts, usage scenes, or platform styling.
- Use optional reference images as style, layout, scene, or competitor inspiration while preserving the user's actual product.
- Generate localized product feature images in Chinese, English, Japanese, Korean, or another requested target language.
The workflow is optimized for one image per request or a small set of variations. For a full Tmall/Taobao listing set with main images and detail pages, prefer the broader `tmall-product-images` skill. Use `$gpt-image-2` as the image generation/editing engine for actual files.
## Inputs
- Required: one original product image. Preserve the product's shape, color, material, logo placement, packaging, proportions, and distinctive details.
- Optional: one or more reference images. Use them as style, layout, scene, lighting, typography, or competitor-analysis references while preserving the original product. `$gpt-image-2` image editing supports multiple images through `images`; keep the original product image first and then reference images in the order the user provided or in the order most relevant to the requested output.
- Optional: selling point description. If absent, infer only visible product facts and ask one focused question when the missing selling point would materially change the image.
- Optional: target marketplace or platform. Default to a general clean ecommerce style if unspecified.
- Optional: target language for on-image text. Default to the user's conversation language.
- Optional: aspect ratio or output size. Default to square `1024x1024`; use `1536x1024` for landscape and `1024x1536` for portrait unless the user asks for a supported smaller draft size.
- Optional: output count. Default to 1. Pass `n` only when the user asks for multiple variations.
- Optional: output format and quality. Default to PNG and `quality: "high"` for deliverable ecommerce images.
## Execution
Use `$gpt-image-2` for the actual image operation. This skill provides the ecommerce strategy, prompt structure, input ordering, and compliance constraints; `$gpt-image-2` provides the execution path.
1. Follow `$gpt-image-2` image editing mode.
Because this workflow always starts from an original product image, use GPT Image Editing through `fusion-api.openai_image_edit_async_submit`, as documented by `$gpt-image-2`. Do not run new oo capability discovery during normal use.
2. Upload local images that must be passed to `$gpt-image-2`:
```bash
oo file upload "<product-image-path>" --json
oo file upload "<reference-image-path>" --json
```
Pass each returned `downloadUrl` as an image reference in `images`, using the `$gpt-image-2` shape:
```json
{"image_url":"<downloadUrl>"}
```
Put the original product image first, followed by reference images.
3. Build a concise prompt with this structure:
```text
Create a marketplace-ready ecommerce selling point image.
Primary product: preserve the product from image 1 exactly, including shape, proportions, color, material, packaging, labels, and logo placement.
Reference usage: use images 2+ only for style, layout, background, lighting, composition, typography, or scene inspiration; do not copy unrelated products, brands, watermarks, or text.
Marketplace: <platform or general ecommerce>.
Target language for visible text: <language>.
Selling point: <user-provided selling point, or conservative visible/inferred benefit>.
Composition: product-first, clean commercial layout, readable mobile-safe typography, clear visual hierarchy, enough whitespace, no fake platform badges.
Avoid: QR codes, phone numbers, social handles, external URLs, competitor marks, fake certificates, fake discounts, unsupported absolute claims, distorted product details, unreadable text, watermark.
```
When the user did not provide a selling point, avoid inventing proof-dependent claims. Use visible, low-risk benefits such as material appearance, compactness, storage, comfort, organization, portability, or usage context only when supported by the image.
4. Execute with `$gpt-image-2`.
The underlying command shape is:
```bash
oo connector run "fusion-api" \
--action "openai_image_edit_async_submit" \
--data @payload.json \
--json
```
Example payload:
```json
{
"model": "gpt-image-2",
"prompt": "Create a marketplace-ready ecommerce selling point image...",
"images": [
{"image_url": "<product-downloadUrl>"},
{"image_url": "<optional-reference-downloadUrl>"}
],
"output_format": "png",
"quality": "high",
"size": "1024x1024"
}
```
Use the exact field names from `$gpt-image-2`: `output_format`, `quality`, `size`, and optional `n`. If using a JSON file, write valid JSON with those field names. `oo connector run --json` usually waits internally and returns completed image results directly; if it returns a handle, follow `$gpt-image-2` polling guidance.
## Result Handling
Read GPT Image 2 results according to `$gpt-image-2`:
- Image URLs are usually in `.data.data[].url`.
- Revised prompts may be in `.data.data[].revised_prompt`.
- Returned metadata may include `.data.size`, `.data.quality`, `.data.output_format`, `.data.model`, and `.data.usage`.
Download each returned HTTP image URL with `oo file download "<url>" "<output-dir>" --name "<fileNameWithoutExtension>" --ext "<png|jpeg|webp|jpg>"`. `oo file download` prints `Saved to: <path>` and does not support `--json`.
- Use a clear local output directory such as `/Users/yunshi/Downloads/ecommerce-selling-point-images/<short-product-name>-<timestamp>/`.
- Name files predictably, for example `selling-point-01.png`, `selling-point-02.png`, etc.
- Preview the generated image when practical, or return the local file path and a short note describing platform, language, aspect ratio, and selling point used.
- If the task returns structured result data instead of direct URLs, report the actual returned fields and do not invent file URLs.
## Failure Handling
- Missing original product image: stop and ask for one product image.
- Too many reference images: `$gpt-image-2` supports multiple edit images, but if the set is noisy, redundant, or too large for a useful prompt, select the most relevant references and summarize the rest in the prompt.
- Unsupported aspect ratio, format, size, quality, or count: use the nearest `$gpt-image-2` supported value and mention the adjustment.
- Missing selling point: either ask one focused question or use a conservative visible benefit; do not invent certifications, performance numbers, regulated claims, awards, warranties, or safety/health claims.
- Upload failure: report the exact file that failed and retry only after the path or network issue is resolved.
- Connector handle or timeout: follow `$gpt-image-2` result/polling guidance before rerunning. Do not start a duplicate task only because a wait window ended.
- GPT Image 2 connector failure or billing/auth blocker: report the exact blocker from oo output and the next useful action.