Ecommerce Detail Images
When to Use
Use this skill when the user wants product detail graphics from real product photos. Typical requests include close-up ecommerce images for texture, stitching, hardware, seams, edges, buttons, zippers, handles, labels, packaging details, components, internal structure, material appearance, or craftsmanship.
Use this for detail-page modules and main-image detail variants. Use ecommerce-size-images for measurements, ecommerce-main-images for covers, and ecommerce-selling-point-images for broad benefit callouts.
- Required: at least one original product image. Preserve product identity, color, shape, logo placement, material appearance, packaging, and distinctive details.
- Optional: requested detail focus, such as
五金, 走线, 面料纹理, 拉链, 包底, 内里, 接口, 瓶口, 按键, or 包装细节. If absent, choose visible low-risk detail points.
- Optional: confirmed product notes. Use only supplied facts for material, process, waterproofing, load-bearing, durability, safety, or certification claims.
- Optional: reference images. Use only for close-up composition, callout layout, crop style, lighting, background, or typography.
- Optional: platform, target language, output size, format, quality, and variation count. Default to Chinese, PNG, high quality, and square or vertical depending on the requested module.
Before prompting, separate confirmed, visible, missing, and do not claim. Do not invent material names, process names, certifications, performance claims, or test data.
Execution
Use $gpt-image-2 image editing mode through fusion-api.openai_image_edit_async_submit. This skill supplies the ecommerce detail-image strategy and prompt; $gpt-image-2 supplies execution. Do not run new oo capability discovery during normal use.
Upload local images with oo file upload "<path>" --json, then pass returned downloadUrl values as:
{"image_url":"<downloadUrl>"}
Put the product image first, then optional references.
Prompt structure:
Create a marketplace-ready ecommerce product detail image.
Primary product: preserve image 1 accurately, including color, shape, material appearance, logos, labels, and distinctive physical details.
Detail focus: <requested or visible detail points>.
Confirmed facts to display: <only user-supplied facts>.
Visible facts: <safe visual observations>.
Reference usage: use images 2+ only for close-up layout, lighting, background, typography, or callout style.
Composition: close-up detail crop plus product context, mobile-readable labels, clean callout lines, professional ecommerce hierarchy.
Avoid: invented material/process/certification/performance claims, fake test data, QR codes, phone numbers, platform badges, watermarks, distorted product details, clutter, unreadable text.
Run with $gpt-image-2:
oo connector run "fusion-api" --action "openai_image_edit_async_submit" --data @payload.json --json
Use exact fields: model: "gpt-image-2", prompt, images, output_format, quality, size, and optional n.
Result Handling
Read result URLs from .data.data[].url. Download each image with oo file download "<url>" "<output-dir>" --name "<name>" --ext "<ext>".
Use /Users/yunshi/Downloads/ecommerce-detail-images/<short-product-name>-<timestamp>/ and names like detail-image-01.png. Preview the image when practical and mention the detail focus plus confirmed facts used.
Failure Handling
- Missing product image: ask for one.
- Missing proof for material/process claims: omit the claim or ask for confirmation.
- Requested detail is not visible: ask for a clearer detail photo or create a conservative layout based on visible areas.
- Too many references: select the most relevant layout/style references.
- Connector handle, timeout, auth, billing, upload, or schema failure: follow
$gpt-image-2 guidance and report the exact blocker.
---
name: ecommerce-detail-images
description: 'Generate ecommerce detail images from product photos and confirmed product notes. Use when the user asks to create Taobao, Tmall, 1688, Amazon, Shopee, Temu, or marketplace-ready close-up detail graphics showing texture, structure, hardware, seams, components, craftsmanship, packaging details, or product feature closeups with GPT Image 2.'
metadata:
icon: "\U0001F50D"
title: Ecommerce Detail Images
companionSkill: gpt-image-2
packageName: '@zjxuyunshi/ecommerce-detail-images'
version: 0.0.1
---
# Ecommerce Detail Images
## When to Use
Use this skill when the user wants product detail graphics from real product photos. Typical requests include close-up ecommerce images for texture, stitching, hardware, seams, edges, buttons, zippers, handles, labels, packaging details, components, internal structure, material appearance, or craftsmanship.
Use this for detail-page modules and main-image detail variants. Use `ecommerce-size-images` for measurements, `ecommerce-main-images` for covers, and `ecommerce-selling-point-images` for broad benefit callouts.
## Inputs
- Required: at least one original product image. Preserve product identity, color, shape, logo placement, material appearance, packaging, and distinctive details.
- Optional: requested detail focus, such as `五金`, `走线`, `面料纹理`, `拉链`, `包底`, `内里`, `接口`, `瓶口`, `按键`, or `包装细节`. If absent, choose visible low-risk detail points.
- Optional: confirmed product notes. Use only supplied facts for material, process, waterproofing, load-bearing, durability, safety, or certification claims.
- Optional: reference images. Use only for close-up composition, callout layout, crop style, lighting, background, or typography.
- Optional: platform, target language, output size, format, quality, and variation count. Default to Chinese, PNG, high quality, and square or vertical depending on the requested module.
Before prompting, separate `confirmed`, `visible`, `missing`, and `do not claim`. Do not invent material names, process names, certifications, performance claims, or test data.
## Execution
Use `$gpt-image-2` image editing mode through `fusion-api.openai_image_edit_async_submit`. This skill supplies the ecommerce detail-image strategy and prompt; `$gpt-image-2` supplies execution. Do not run new oo capability discovery during normal use.
Upload local images with `oo file upload "<path>" --json`, then pass returned `downloadUrl` values as:
```json
{"image_url":"<downloadUrl>"}
```
Put the product image first, then optional references.
Prompt structure:
```text
Create a marketplace-ready ecommerce product detail image.
Primary product: preserve image 1 accurately, including color, shape, material appearance, logos, labels, and distinctive physical details.
Detail focus: <requested or visible detail points>.
Confirmed facts to display: <only user-supplied facts>.
Visible facts: <safe visual observations>.
Reference usage: use images 2+ only for close-up layout, lighting, background, typography, or callout style.
Composition: close-up detail crop plus product context, mobile-readable labels, clean callout lines, professional ecommerce hierarchy.
Avoid: invented material/process/certification/performance claims, fake test data, QR codes, phone numbers, platform badges, watermarks, distorted product details, clutter, unreadable text.
```
Run with `$gpt-image-2`:
```bash
oo connector run "fusion-api" --action "openai_image_edit_async_submit" --data @payload.json --json
```
Use exact fields: `model: "gpt-image-2"`, `prompt`, `images`, `output_format`, `quality`, `size`, and optional `n`.
## Result Handling
Read result URLs from `.data.data[].url`. Download each image with `oo file download "<url>" "<output-dir>" --name "<name>" --ext "<ext>"`.
Use `/Users/yunshi/Downloads/ecommerce-detail-images/<short-product-name>-<timestamp>/` and names like `detail-image-01.png`. Preview the image when practical and mention the detail focus plus confirmed facts used.
## Failure Handling
- Missing product image: ask for one.
- Missing proof for material/process claims: omit the claim or ask for confirmation.
- Requested detail is not visible: ask for a clearer detail photo or create a conservative layout based on visible areas.
- Too many references: select the most relevant layout/style references.
- Connector handle, timeout, auth, billing, upload, or schema failure: follow `$gpt-image-2` guidance and report the exact blocker.