多模态产品图片相似度分析与分组。当用户提到产品图片相似度、视觉分组、查找外观相似的商品、基于图片去重、竞品同款检测、同款商品聚类、按外观分组、image similarity, product image comparison, visual clustering, same-style recognition, appearance deduplication, image grouping时触发此技能。即使用户未明确说"图片相似度"，只要其意图涉及商品主图对比、视觉聚类、识别视觉上相同或相似的商品，或根据外观、颜色、构图等视觉特征对商品列表进行后处理，也应触发此技能。

Multimodal Product Image Similarity Analysis This skill guides you on how to analyze and group products by the visual similarity of their main images. It helps Amazon sellers identify same style products, detect competitor lookalikes, and organize product lists into visually coherent clusters. Core Concepts Product Image Similarity Analysis uses multimodal AI to compare the main images of products and automatically group them based on visual features such as appearance, color, composition, and material. It is a post processing tool it operates on product data that has already been retrieved by a preceding step (e.g., product search, product recommendations). Similarity threshold : The similarityThreshold parameter controls how visually close two products must be to land in the same group. It is an integer from 0 to 100 representing a percentage. A higher value means stricter matching (only near identical images group together); a lower value means more lenient matching (broader visual clusters). The default is 60 . Single brand group filtering : The includeSingleBrandGroups flag (default true ) controls whether groups containing products from only one brand are included in the results. Setting it to false filters out single brand groups, which is useful when the user wants to focus on cross brand visual overlaps (e.g., competitor lookalike analysis). Input Data Requirement This tool requires a products list from a preceding step. It cannot fetch product data on its own. The typical workflow is: 1. Run a product search or recommendation tool to obtain a product list. 2. Pass that product list into this tool via refResultData for visual similarity grouping. The input data must be a JSON object containing a products array. Parameters Parameter Type Required Description similarityThreshold integer No Similarity threshold (0 100), default 60 . Higher = stricter matching. includeSingleBrandGroups boolean No Whether to include groups with only one brand, default true . Set to false to focus on cross brand similarity. refResultData string No JSON string of the preceding tool's result data containing the product list. userInput string No The original user query or instruction text. Response Fields Field Type Description groups array List of similarity groups. Each group contains groupNumber , reason , brandCount , and an asins array of product details. analysisInfo object Summary: totalProductsAnalyzed , totalGroupsFound , similarityThreshold , analysisTimestamp . tables array Tabular result data, each element with data , columns , and name . total integer Total number of result items. title string Result title. type string Rendering style hint. costToken integer Total LLM tokens consumed (input + output). Group Item (asins array element) Field Type Description asin string Product ASIN productId string Product ID brand string Brand name price number Price rating number Rating score ratings integer Number of ratings monthlySalesUnits integer Monthly sales units monthlySalesRevenue number Monthly sales revenue monthlySalesUnitsGrowthRate number Monthly sales growth rate imageUrl string Main image URL productImageUrls array All product image URLs imagePrompt string AI generated image description asinUrl string Product detail page URL availableDate string Listing date color string Color material string Material 调用方式 API 端点： POST /multimodal/analyzeProductSimilarity （完整参数/响应/错误码见 references/api.md ） Python 脚本： python scripts/multimodal analyze product similarity.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 product similarity <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。 Usage Examples 1. Group search results by visual similarity (default threshold) After obtaining a product list from a search tool, pass the results to this tool to cluster visually similar items: 2. Find near identical products (strict matching) 3. Cross brand competitor lookalike detection 4. Broad visual clustering (lenient threshold) 5. Combined: strict similarity across brands Display Rules 1. Present grouping results clearly : Show each similarity group with its group number, the reason for grouping, brand count, and a table of products within the group. 2. Show product images when possible : If image URLs are available, include them to help users visually verify the grouping. 3. Highlight cross brand groups : When the user cares about competitor analysis, emphasize groups containing multiple brands. 4. Analysis summary : Always present the analysis summary (total products analyzed, total groups found, similarity threshold used, timestamp). 5. No subjective advice : Present the grouping data objectively. Do not inject business recommendations unless the user asks. 6. Large result sets : When there are many groups, show the most significant ones first (e.g., groups with the most products or the most brands) and inform the user about additional groups. 7. Error handling : When a request fails, explain the reason based on the response message and suggest adjustments (e.g., check that the input product data is valid, adjust the threshold). Important Limitations Post processing only : This tool cannot fetch product data on its own. It must receive product data from a preceding step. No database storage : Results are not stored in a database. Do not use database query tools for secondary analysis on the output. Input format : The input must be a JSON object containing a products array. Direct to summary : After this tool completes, pass the results directly to the summary stage. Do not perform additional intermediate data computations. User Expression & Scenario Quick Reference Applicable Visual similarity analysis on product lists: User Says Scenario "Group these by how they look" Visual clustering "Find similar looking products", "find lookalikes" Similarity detection "Which products look the same" Image deduplication "Show me competitor copycats" Cross brand lookalike analysis "Cluster by appearance / color / style" Visual categorization "Are there duplicates in this list" Image based dedup "Same style products from different brands" Cross brand similarity Not applicable Needs beyond image similarity: Text based product comparison (titles, descriptions, keywords) Price or sales based grouping without visual component Product search or discovery (this tool only post processes existing lists) Review analysis, listing optimization, advertising strategy 积分消耗规则按动态规则计费：消耗积分 = sum(每个商品主图提示词生成调用的(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: 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. For more high quality, professional cross border e commerce skills, set [LinkFox Skills](https://skill.linkfox.com/).

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