- name
- linkfox-multimodal-extract-attributes
- description
- 利用多模态AI分析商品主图，提取视觉特征和提示词。当用户提到分析产品图片、从商品图中提取视觉属性、识别产品Listing中的颜色/形状/材质/风格、反推图片提示词、批量视觉特征提取、将产品图信息转化为结构化数据、视觉属性统计、基于图片的商品分类、main image analysis, image feature extraction, visual attribute recognition, product image analysis, image classification, batch image analysis时触发此技能。即使用户未明确提及"图片分析"，只要其需求涉及从商品主图或附图中提取结构化信息，也应触发此技能。
Product Main Image Prompt Extractor
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.
Core Concepts
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.
Parameter Guide
| 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. |
Writing Effective Prompts
- 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.
Prompt Examples
| 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" |
API Usage
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.
Response Structure
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") andattributeValue(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.
Display Rules
- 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.
Important Limitations
- Requires product data input: The tool cannot operate without a
productsarray 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
refResultDataor 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.
User Expression & Scenario Quick Reference
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.
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