---
name: linkfox-multimodal-product-similarity
description: 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 |

## API Usage

This tool calls the LinkFox tool gateway API. See `references/api.md` for endpoint details, request parameters, and response structure. You can also execute `scripts/multimodal_analyze_product_similarity.py` directly to run analyses.

## 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:
```
User: "Group these products by how similar they look."
Action: Call the API with refResultData set to the preceding product list JSON, using the default similarityThreshold of 60.
```

**2. Find near-identical products (strict matching)**
```
User: "Which of these products have almost the same main image?"
Action: Call the API with similarityThreshold set to 85 or higher for strict visual matching.
```

**3. Cross-brand competitor lookalike detection**
```
User: "Show me groups where different brands have similar-looking products."
Action: Call the API with includeSingleBrandGroups set to false to filter out single-brand clusters.
```

**4. Broad visual clustering (lenient threshold)**
```
User: "Roughly categorize these products by appearance."
Action: Call the API with similarityThreshold set to 40 for broad grouping.
```

**5. Combined: strict similarity across brands**
```
User: "Find products from different brands that look nearly identical."
Action: Call the API with similarityThreshold set to 80 and includeSingleBrandGroups set to false.
```

## 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


**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.

<!-- LF_LARGE_RESPONSE_BLOCK -->
## Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

```
python scripts/response_io.py run --script scripts/multimodal_analyze_product_similarity.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"
```

> Pick `--out-dir` outside any git working tree (e.g. `/tmp/...` on Unix, `%TEMP%/...` on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

`run` writes the full response to a file and emits only a schema preview + file path. `read` projects specific fields, with `--limit/--offset` for slicing and `--format json|jsonl|csv|table` for output.

**When to prefer this pattern** — apply your judgment based on the response characteristics, e.g.:
- High field count per record, or fields you don't need
- Batch/paginated results (multiple items per call)
- Long-text fields (descriptions, reviews, HTML, time series)
- Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via `read`.
<!-- /LF_LARGE_RESPONSE_BLOCK -->

---
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