| 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" |
调用方式
- API 端点：
POST /multimodal/extractPromptsFromMainImage（完整参数/响应/错误码见 references/api.md）
- Python 脚本：
python scripts/multimodal_extract_attributes.py '<JSON 参数>' [--inline]
- 成本约束：本工具会消耗积分；同一会话同一参数组合默认只调用一次，脚本带 24h 本地缓存。失败/空结果不得自动换关键词、翻页或改邮编连续试探；需要继续检索时先向用户说明会产生额外消耗。
输出策略（脚本默认行为）：
- 始终将完整响应写入
<cwd>/linkfox/<YYYY-MM-DD>/<session>/data/linkfox-multimodal-extract-attributes-<timestamp>.json（<cwd> 为脚本执行时的工作目录，在 Claude Code 里即当前项目目录；<session> 取自环境变量 SESSION_ID，按用户任务自动聚合；禁止写入 /tmp，当前目录不可写则报错）
- 响应体 ≤ 8 KB：落盘后把完整 JSON 打印到 stdout
- 响应体 > 8 KB：落盘后 stdout 只输出摘要（顶层字段、常见计数如
total/costToken、最大列表字段的长度 + 前 3 条样本）
- 加
--inline 强制全量打印到 stdout（同样落盘）
读数据建议：先看摘要判断是否足够；需要具体字段时优先用 jq或ConvertFrom-Json 从保存的 json 文件按需抽取，避免整份 JSON 进入上下文。
解决认证和积分问题
发生以下异常情况时，采用 references/onboarding.md 引导解决问题：
异常情况
- 未配置API Key：环境变量未配置
LINKFOX_AGENT_API_KEY，也未配置 LINKFOXAGENT_API_KEY。
- 响应401或402状态码
- 响应提示积分或余额不足：消息含"积分余额不足/计费不足/余额不足/quota exceeded/insufficient balance/套餐到期/需充值/请充值"，或类似含义的内容。
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
- 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
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:
| 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
积分消耗规则
按动态规则计费：消耗积分 = sum(每张被分析图片的(输入消耗的积分 + 输出结果消耗的积分))。
重要：本技能的服务按倍数动态计算，可能一次性消耗大量积分，必须提醒用户，由用户决定是否继续。
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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