--- 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 `$gpt-image-2`: ```bash oo file upload "" --json oo file upload "" --json ``` Pass each returned `downloadUrl` as an image reference in `images`, using the `$gpt-image-2` shape: ```json {"image_url":""} ``` 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: . Target language for visible text: . Selling point: . 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 `$gpt-image-2`. The underlying command shape is: ```bash oo connector run "fusion-api" \ --action "openai_image_edit_async_submit" \ --data @payload.json \ --json ``` Example payload: ```json { "model": "gpt-image-2", "prompt": "Create a marketplace-ready ecommerce selling point image...", "images": [ {"image_url": ""}, {"image_url": ""} ], "output_format": "png", "quality": "high", "size": "1024x1024" } ``` Use the exact field names from `$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 "" "" --name "" --ext ""`. `oo file download` prints `Saved to: ` and does not support `--json`. - Use a clear local output directory such as `/Users/yunshi/Downloads/ecommerce-selling-point-images/-/`. - 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.