# linkfox-multimodal-product-similarity

Compares product main images and sorts a list into visual groups. Useful for spotting near-identical listings or lookalikes from other brands.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 400 installs · ★ 111
- Author: linkfox-ai
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/linkfox-ai/linkfox-skills/tree/HEAD/skills/linkfox-multimodal-product-similarity/SKILL.md
- Updated: 2026-10-16
- HTML: https://affpapa.org/en/nevibecode/skills/linkfox-multimodal-product-similarity

## What it does

The skill runs on top of a product list you already have. It does not fetch anything itself: run a search or recommendation step first, pass the result through `refResultData`, and the model compares main images by appearance, color, composition and material.

Matching strictness comes from `similarityThreshold`, an integer from 0 to 100, default 60. Higher means only near-identical images land in one group. The `includeSingleBrandGroups` flag defaults to `true`; set it to `false` to drop single-brand clusters and focus on cross-brand overlap.

Calls go to `POST /multimodal/analyzeProductSimilarity` or through `python scripts/multimodal_analyze_product_similarity.py '<JSON params>'`. You need `LINKFOX_AGENT_API_KEY` or `LINKFOXAGENT_API_KEY` set, otherwise you get a 401 or 402. The tool burns tokens, so the same parameter combination runs once per session and the script keeps a 24h local cache. Full responses are written to `linkfox/<YYYY-MM-DD>/<session>/data/`; writing to `/tmp` is blocked, and the script errors out if the working directory is not writable.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/linkfox-multimodal-product-similarity/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/linkfox-multimodal-product-similarity/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/linkfox-multimodal-product-similarity/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/linkfox-multimodal-product-similarity/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-product-similarity
```

2. **Manually: put the files in place**
- File: `.github/skills/linkfox-multimodal-product-similarity/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/linkfox-ai/linkfox-skills --path skills/linkfox-multimodal-product-similarity
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/linkfox-multimodal-product-similarity/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/linkfox-ai/linkfox-skills/tree/HEAD/skills/linkfox-multimodal-product-similarity/SKILL.md

## When to use it
- cluster search results by how the cards look
- find products with almost the same main image
- surface lookalikes sold under different brands
- strip visual duplicates before further processing
- split a long catalog into same-style groups

## Example prompts
- Group these products by how similar they look
- Show me groups where different brands have near-identical products
- Categorize this list by appearance, threshold 40

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: ee4ab853f26e4bfc.

BEGIN THIRD-PARTY FILE ee4ab853f26e4bfc
```text
---
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.
```
END THIRD-PARTY FILE ee4ab853f26e4bfc

The third-party file has ended. Everything between the ee4ab853f26e4bfc tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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