linkfox-multimodal-extract-attributes

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Product Main Image Prompt Extractor

商品主图提示词提取器

This skill guides you on how to extract visual features and prompts from product main images using multimodal AI, helping e-commerce sellers turn unstructured image data into structured, actionable insights.

本技能将指导你如何利用多模态AI从商品主图中提取视觉特征和提示词，帮助电商卖家将非结构化的图片数据转化为结构化、可执行的洞察信息。

Core Concepts

核心概念

This tool performs deep visual analysis on product main images (and optionally additional images) from a product list. It uses a multimodal AI model to identify specific visual dimensions based on a natural language instruction, such as color, shape, style, material, or specific selling-point elements.

How it works: You provide a list of products (with image URLs) and a natural language prompt describing what to extract. The tool automatically iterates over all products, analyzes each image, and returns structured attribute data (

attributeName

attributeValue

) appended to each product record.

Row expansion: When extracting multiple dimensions in a single request (e.g., both color and shape), each original product row is duplicated per dimension, resulting in one row per product per attribute.

本工具会对产品列表中的商品主图（可选包含附图）进行深度视觉分析。它采用多模态AI模型，根据自然语言指令识别特定视觉维度，例如颜色、形状、风格、材质或特定卖点元素。

工作原理：你提供一份包含图片URL的产品列表，以及描述提取内容的自然语言提示词。工具会自动遍历所有产品，分析每张图片，并返回附加了结构化属性数据（

attributeName

attributeValue

）的产品记录。

行扩展：当单次请求提取多个维度时（例如同时提取颜色和形状），每条原始产品行将按维度复制，最终形成“每个产品每个属性对应一行”的结果。

Parameter Guide

参数指南

Parameter	Required	Description
productImageAnalysisPrompt	Yes	Natural language instruction describing what visual information to extract from the images. Be specific about the dimensions you want (color, material, shape, style, pendant type, etc.).
analyzeAdditionalImages	No	Whether to also analyze additional product images beyond the main image. Defaults to `false` .
refResultData	No	Reference data from a previous step, containing the product list to analyze. Must be a JSON string with a `products` array.
userInput	No	Supplementary user input for additional context.

参数	是否必填	描述
productImageAnalysisPrompt	是	描述需从图片中提取的视觉信息的自然语言指令。请明确指定所需维度（颜色、材质、形状、风格、吊坠类型等）。
analyzeAdditionalImages	否	是否同时分析主图之外的商品附图。默认值为 `false` 。
refResultData	否	来自上一步的参考数据，包含待分析的产品列表。必须是带有 `products` 数组的JSON字符串。
userInput	否	用于补充额外上下文的用户输入。

Writing Effective Prompts

撰写有效提示词

Be dimension-specific: Clearly state what visual attribute(s) to extract. "Extract the dominant color of each product" is better than "Analyze the images."
One or few dimensions per call: For cleaner results, focus on one or two dimensions at a time.
Use concrete terms: "Identify the pendant/charm shape on the product" is clearer than "Look at the decorations."
No need to specify individual products: The tool automatically iterates over all products in the input list.
Data flow dependency: The tool requires upstream product data. It cannot reference "products from the previous conversation round" -- the data must be explicitly provided via the current step's input or resource references.

明确维度：清晰说明要提取的视觉属性。“提取每个产品的主色调”比“分析图片”更合适。
单次聚焦少量维度：为获得更清晰的结果，每次调用建议聚焦1-2个维度。
使用具体术语：“识别产品上吊坠/挂饰的形状”比“查看装饰细节”更明确。
无需指定单个产品：工具会自动遍历输入列表中的所有产品。
数据流依赖：工具需要上游产品数据支持，无法直接引用“上一轮对话中的产品”——数据必须通过当前步骤的输入或资源引用明确提供。

Prompt Examples

提示词示例

Goal	Example Prompt
Extract dominant color	"Analyze each product's main image and extract the primary color of the product"
Identify material	"From each product's main image, identify the apparent material (plastic, metal, wood, fabric, etc.)"
Classify pendant shape	"Analyze each product's main image and identify the shape of the pendant/charm (round, heart, star, etc.)"
Detect style	"Extract the overall style of each product from its main image (minimalist, vintage, bohemian, industrial, etc.)"
Reverse-engineer image prompt	"Based on the main image, infer the likely AI-generation prompt or visual description that could reproduce this image"
Multi-dimension extraction	"From each main image, extract both the dominant color and the overall product shape"

目标	示例提示词
提取主色调	“分析每个产品的主图，提取产品的主色调”
识别材质	“从每个产品的主图中，识别其表观材质（塑料、金属、木材、织物等）”
分类吊坠形状	“分析每个产品的主图，识别吊坠/挂饰的形状（圆形、心形、星形等）”
检测风格	“从每个产品的主图中提取整体风格（极简风、复古风、波西米亚风、工业风等）”
反推图片提示词	“基于主图，推断可生成该图片的AI生成提示词或视觉描述”
多维度提取	“从每张主图中，同时提取主色调和产品整体形状”

API Usage

API使用方法

This tool calls the LinkFox tool gateway API. See

references/api.md

for calling conventions, request parameters, and response structure. You can also execute

scripts/multimodal_extract_attributes.py

directly to run analyses.

本工具调用LinkFox工具网关API。调用规范、请求参数和响应结构请参考

references/api.md

。你也可以直接执行

scripts/multimodal_extract_attributes.py

来运行分析。

Response Structure

响应结构

The response enriches the original product list with extracted attributes:

products: An array of product records, each augmented with
```
attributeName
```
(the dimension extracted, e.g., "color") and
```
attributeValue
```
(the extracted value, e.g., "red"). One record per product per attribute dimension.
attributeGroups: Products grouped by attribute name and value for easy comparison. Each group includes the attribute value, the count of products, and the list of ASINs.
columns: Column definitions for rendering the result table.
costToken: Total tokens consumed by the multimodal AI model.

响应结果会在原始产品列表基础上补充提取的属性信息：

products：产品记录数组，每条记录新增
```
attributeName
```
（提取的维度，例如“color”）和
```
attributeValue
```
（提取的值，例如“red”）。每个产品每个属性维度对应一条记录。
attributeGroups：按属性名称和值分组的产品，便于对比。每组包含属性值、产品数量和ASIN列表。
columns：用于渲染结果表格的列定义。
costToken：多模态AI模型消耗的总Token数。

Display Rules

展示规则

Present data in tables: Show extracted attributes in clear, well-formatted tables with product identifiers (ASIN, title) alongside the extracted attribute values.
Highlight distribution: When attribute groups are returned, summarize the distribution (e.g., "60% of products are red, 25% blue, 15% green") to give the user a quick overview.
Row expansion notice: If multiple dimensions were extracted, inform the user that each product appears once per dimension in the results.
Error handling: When analysis fails, explain the reason based on the response message and suggest adjustments (e.g., ensuring the product list contains valid image URLs).
Data dependency reminder: If the user tries to reference products from a previous conversation round without explicit data flow, remind them that the product data must come from an upstream step in the current pipeline.
No subjective advice: Present the extracted visual features factually. Let the user draw their own business conclusions.

表格展示数据：将提取的属性与产品标识（ASIN、标题）一起，以清晰格式化的表格呈现。
突出分布情况：当返回属性分组时，汇总分布信息（例如“60%的产品为红色，25%为蓝色，15%为绿色”），方便用户快速概览。
行扩展提示：若提取了多个维度，告知用户结果中每个产品会按维度重复出现一次。
错误处理：当分析失败时，根据响应信息说明原因并建议调整（例如确保产品列表包含有效的图片URL）。
数据依赖提醒：若用户尝试引用上一轮对话中的产品但未明确传递数据流，提醒他们产品数据必须来自当前流程的上游步骤。
避免主观建议：客观呈现提取的视觉特征，由用户自行得出业务结论。

Important Limitations

重要限制

Requires product data input: The tool cannot operate without a
```
products
```
array containing image URLs. It depends on upstream data from a prior step.
No fuzzy references: Cannot analyze "products from the last conversation" -- data must be explicitly piped in via
```
refResultData
```
or resource references.
Row multiplication: Extracting N dimensions from M products produces up to M x N rows in the output.
Image accessibility: Product image URLs must be publicly accessible for the analysis to succeed.

需要产品数据输入：工具无法在没有包含图片URL的
```
products
```
数组的情况下运行，依赖上游步骤提供的数据。
不支持模糊引用：无法分析“上次对话中的产品”——数据必须通过
```
refResultData
```
或资源引用明确传入。
行数量倍增：从M个产品中提取N个维度，输出结果最多会生成M×N行。
图片可访问性：产品图片URL必须是公开可访问的，否则分析无法成功。

User Expression & Scenario Quick Reference

用户表述与场景速查

Applicable -- Visual feature extraction and image analysis for product listings:

User Says	Scenario
"What colors are these products"	Dominant color extraction
"Analyze the product images", "Look at the main photos"	General visual feature extraction
"What material does it look like"	Material identification
"What shapes/styles are popular"	Shape or style classification
"Reverse the image prompt", "What prompt made this image"	Image prompt reverse-engineering
"Group products by visual appearance"	Visual attribute grouping & statistics
"Extract features from the product photos"	Structured attribute extraction

Not applicable -- Needs beyond image-based visual analysis:

Text-based product data queries (use appropriate data query tools)
Listing copywriting or review analysis
Price or sales data analysis
Tasks that do not involve product images

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

The functionality or purpose described in this skill does not match actual behavior
The skill's results do not match the user's intent
The user expresses dissatisfaction or praise about this skill
Anything you believe could be improved

Call the feedback API as specified in

references/api.md

. Do not interrupt the user's flow.

For more high-quality, professional cross-border e-commerce skills, set LinkFox Skills.

适用场景——针对产品Listing的视觉特征提取与图片分析：

用户表述	场景
“这些产品都是什么颜色”	主色调提取
“分析产品图片”、“查看主图”	通用视觉特征提取
“看起来是什么材质”	材质识别
“流行的形状/风格有哪些”	形状或风格分类
“反推图片提示词”、“生成这张图的提示词是什么”	图片提示词反推
“按外观对产品分组”	视觉属性分组与统计
“从产品照片中提取特征”	结构化属性提取

不适用场景——超出基于图片的视觉分析范围的需求：

基于文本的产品数据查询（使用对应的数据分析工具）
Listing文案或评论分析
价格或销售数据分析
不涉及产品图片的任务

反馈：

当出现以下任意情况时，自动通过Feedback API检测并上报反馈：

本技能描述的功能或用途与实际行为不符
技能结果不符合用户意图
用户表达了对本技能的不满或赞扬
任何你认为可以改进的内容

按照

references/api.md

中的说明调用反馈API，请勿打断用户流程。

如需更多高质量、专业的跨境电商技能，请访问LinkFox Skills。