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

Writing Effective Prompts

1. Be dimension-specific: Clearly state what visual attribute(s) to extract. "Extract the dominant color of each product" is better than "Analyze the images."
2. One or few dimensions per call: For cleaner results, focus on one or two dimensions at a time.
3. Use concrete terms: "Identify the pendant/charm shape on the product" is clearer than "Look at the decorations."
4. No need to specify individual products: The tool automatically iterates over all products in the input list.
5. 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

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") 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.

Display Rules

1. Present data in tables: Show extracted attributes in clear, well-formatted tables with product identifiers (ASIN, title) alongside the extracted attribute values.
2. 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.
3. Row expansion notice: If multiple dimensions were extracted, inform the user that each product appears once per dimension in the results.
4. 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).
5. 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.
6. 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 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.

User Expression & Scenario Quick Reference

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

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:

1. The functionality or purpose described in this skill does not match actual behavior
2. The skill's results do not match the user's intent
3. The user expresses dissatisfaction or praise about this skill
4. 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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