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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…
Install Linkfox Multimodal Product Similarity
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-product-similarity --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/linkfox-multimodal-product-similarity .claude/skills/linkfox-multimodal-product-similarity && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "linkfox-multimodal-product-similarity" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-product-similarity into .claude/skills/linkfox-multimodal-product-similarity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-product-similarity", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-product-similarityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-product-similarity --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/linkfox-multimodal-product-similarity .agents/skills/linkfox-multimodal-product-similarity && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "linkfox-multimodal-product-similarity" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-product-similarity into .agents/skills/linkfox-multimodal-product-similarity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-product-similarity", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-product-similarity --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/linkfox-multimodal-product-similarity .cursor/skills/linkfox-multimodal-product-similarity && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "linkfox-multimodal-product-similarity" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-product-similarity into .cursor/skills/linkfox-multimodal-product-similarity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-product-similarity", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/linkfox-ai/linkfox-skills.git --path skills/linkfox-multimodal-product-similarity--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-product-similarity --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/linkfox-multimodal-product-similarity .gemini/skills/linkfox-multimodal-product-similarity && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "linkfox-multimodal-product-similarity" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-product-similarity into .gemini/skills/linkfox-multimodal-product-similarity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-product-similarity", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-product-similarityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/linkfox-multimodal-product-similarity .github/skills/linkfox-multimodal-product-similarity && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "linkfox-multimodal-product-similarity" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-product-similarity into .github/skills/linkfox-multimodal-product-similarity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-product-similarity", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-product-similarity --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/linkfox-multimodal-product-similarity .opencode/skills/linkfox-multimodal-product-similarity && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "linkfox-multimodal-product-similarity" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-product-similarity into .opencode/skills/linkfox-multimodal-product-similarity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-product-similarity", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
linkfox-multimodal-product-similarity- GitHub stars
- 107
- Used in
- 1 other repo
- Token cost
- ~2.4k tokens
- SKILL.md length
- 1,017 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 177
- Repo updated
- First seen
- Licence
- MIT
At a glance
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…
- Works in 2 steps: Run a product search or recommendation… → Pass that product list into this tool…
- Tasks that involve Data cleaning
- SKILL.md covers Core Concepts, Input Data Requirement, Parameters and Response Fields, plus 7 more sections
- Runs Python scripts from its folder; calls python; needs LINKFOX_AGENT_API_KEY and LINKFOXAGENT_API_KEY
What it does
Linkfox Multimodal Product Similarity is an agent skill from linkfox-ai/linkfox-skills. 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。
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api.md`, `references/onboarding.md` and `scripts/multimodal_analyze_product_similarity.py`).
It sits in Data & Analytics, covering Data cleaning. The licence is MIT.
When your agent uses it
- Tasks that involve Data cleaning
Example prompts
- “/linkfox-multimodal-product-similarity”
Requirements
- Python 3
- A credential in LINKFOX_AGENT_API_KEY
- A credential in LINKFOXAGENT_API_KEY
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- Run a product search or recommendation tool to obtain a product list.
- Pass that product list into this tool via refResultData for visual similarity grouping.
What it can do on your machine
Read from SKILL.md and the folder at commit 38fef04. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
skill.linkfox.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
LINKFOX_AGENT_API_KEYLINKFOXAGENT_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Linkfox Multimodal Product Similarity loads about 2.4k tokens when it runs, and up to ~4.3k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 1,017 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from linkfox-ai/linkfox-skills at commit 38fef04, republished under its MIT licence (© linkfox-ai). 1,017 words, ~2,436 tokens.
Download SKILL.mdSave it as
.claude/skills/linkfox-multimodal-product-similarity/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- 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:
- Run a product search or recommendation tool to obtain a product list.
- Pass that product list into this tool via
refResultDatafor 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：本工具会消耗算力；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。
解决认证和算力问题
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
- 响应提示算力或余额不足：消息含"算力余额不足/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:
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.Show full SKILL.md (428 more words)Show less
Display Rules
- 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.
- Show product images when possible: If image URLs are available, include them to help users visually verify the grouping.
- Highlight cross-brand groups: When the user cares about competitor analysis, emphasize groups containing multiple brands.
- Analysis summary: Always present the analysis summary (total products analyzed, total groups found, similarity threshold used, timestamp).
- No subjective advice: Present the grouping data objectively. Do not inject business recommendations unless the user asks.
- 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.
- 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
productsarray. - 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
算力消耗规则
按动态规则计费：消耗算力 = sum(每个商品主图提示词生成调用的(输入消耗的算力 + 输出结果消耗的算力))。
重要：本技能的服务按倍数动态计算，可能一次性消耗大量算力，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.
For more high-quality, professional cross-border e-commerce skills, set LinkFox Skills.
© linkfox-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/linkfox-multimodal-product-similarity of linkfox-ai/linkfox-skills.
- SKILL.md
- references/api.md
- references/onboarding.md
- scripts/multimodal_analyze_product_similarity.py
- scripts/onboarding.py
Open the folder on GitHubat commit 38fef04
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in linkfox-ai/linkfox-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Linkfox Multimodal Product Similarity next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Linkfox Multimodal Product Similarity this skilllinkfox-ai/linkfox-skills | 107 | 1 repos | ~2.4k | Automated safety check: Pass | MIT | |
| Question2reportrefraction-ray/xalpha | 2.7k | — | ~3.2k | Automated safety check: Pass | MIT | |
| Dingo VerifyMigoXLab/dingo | 757 | — | ~741 | Automated safety check: Notes | Apache-2.0 | |
| Data Validationplatonai/Browser4 | 1.2k | — | ~896 | Automated safety check: Pass | Apache-2.0 | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Issues DeduplicationJetBrains/ideavim | 10k | — | ~1.3k | Automated safety check: Pass | MIT |
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亚马逊ABA（品牌分析）搜索词数据的查询与分析，涵盖15个站点近3年的周维度数据。当用户提到ABA数据、亚马逊搜索词分析、关键词挖掘、搜索排名趋势、市场机会分析、季节性关键词、高点击低转化分析、蓝海词发现、竞品关键词分析、ABA data, search term report, keyword mining, search ranking trends, blue ocean…
Linkfox Amazon Alexa Search
linkfox-ai/linkfox-skills
通过亚马逊前台的 Alexa 购物助手发起自然语言问答，获取与问题相关的导购回答、推荐商品分组、ASIN 列表，以及可继续追问的问题。每次调用仅支持 1 条 prompt，如需追问须由 agent 总结上下文后拼接新问题发起新请求。可用 url 补充亚马逊页面上下文。当用户提到亚马逊 Alexa、Alexa 购物助手、亚马逊智能助手、AI…
Linkfox Amazon Opportunity Search By Metrics
linkfox-ai/linkfox-skills
亚马逊反向选品：基于历史商业洞察报告沉淀的指标数据池，按 30+ 项商业维度（市场规模与增长、价格区间与档位份额、竞争密度与头部集中度、人群画像如年龄/性别/收入、评论卖点与痛点等）反向筛选亚马逊赛道与关键词。当用户提到反向选品、指标筛选、细分市场反查、蓝海赛道挖掘、低竞争赛道、新人友好赛道、品牌分散市场、痛点切入、卖点反查、定价档位机会、人群画像选品、Amazon niche reverse…
Linkfox Amazon Product Detail
linkfox-ai/linkfox-skills
通过ASIN获取亚马逊商品详细信息，包括标题、图片、五点描述、规格参数、A+页面、价格、评分评论、变体等；可在取得原始HTML时尝试提取Item Highlights（商品亮点）。当用户提到亚马逊商品详情、ASIN查询、商品页面数据、Listing分析、五点描述提取、Item…
Linkfox Amazon Reviews List
linkfox-ai/linkfox-skills
按ASIN获取并分析亚马逊商品评论，支持15个站点(含美国站)，按星级筛选评论。当用户提到亚马逊评论、美国站评论、商品评价、买家投诉、差评、好评、星级评分、评论分析、评论情感、产品改良建议、Vine评论、已验证购买评论、竞品评论研究、Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review…
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Questions about Linkfox Multimodal Product Similarity
What does Linkfox Multimodal Product Similarity do?
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…. Linkfox Multimodal Product Similarity is an agent skill from linkfox-ai/linkfox-skills.
When should I use Linkfox Multimodal Product Similarity?
Linkfox Multimodal Product Similarity fits situations like: tasks that involve Data cleaning.
How do I install Linkfox Multimodal Product Similarity in Claude Code?
Run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a claude-code`. Or copy the skill folder (skills/linkfox-multimodal-product-similarity in linkfox-ai/linkfox-skills) into .claude/skills/linkfox-multimodal-product-similarity in your project. Claude Code loads it when a task matches its description.
How do I install Linkfox Multimodal Product Similarity in Codex?
Run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a codex`. Or copy the skill folder (skills/linkfox-multimodal-product-similarity in linkfox-ai/linkfox-skills) into .agents/skills/linkfox-multimodal-product-similarity in your project. Codex loads it when a task matches its description.
Can I use Linkfox Multimodal Product Similarity in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-product-similarity -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/linkfox-multimodal-product-similarity, .gemini/skills/linkfox-multimodal-product-similarity, .github/skills/linkfox-multimodal-product-similarity and .opencode/skills/linkfox-multimodal-product-similarity in your project.
What does Linkfox Multimodal Product Similarity need to run?
Going by SKILL.md and its folder, Linkfox Multimodal Product Similarity needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named LINKFOX_AGENT_API_KEY and LINKFOXAGENT_API_KEY. Our summary lists: Python 3; A credential in LINKFOX_AGENT_API_KEY; A credential in LINKFOXAGENT_API_KEY.
Does Linkfox Multimodal Product Similarity access the network?
SKILL.md names 1 domain. As links in the text: skill.linkfox.com. This is read from the text; nothing was executed.
Is Linkfox Multimodal Product Similarity safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Linkfox Multimodal Product Similarity use?
Linkfox Multimodal Product Similarity is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Linkfox Multimodal Product Similarity use?
About 2.4k tokens (SKILL.md is roughly 9.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.8k tokens, read only when the agent opens those files.
What are the alternatives to Linkfox Multimodal Product Similarity?
Skills that share tags, products or a category with Linkfox Multimodal Product Similarity: Question2report (refraction-ray/xalpha, 2.7k stars), Dingo Verify (MigoXLab/dingo, 757 stars), Data Validation (platonai/Browser4, 1.2k stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Linkfox Multimodal Product Similarity?
linkfox-ai (a GitHub user) maintains it in linkfox-ai/linkfox-skills, which has 107 GitHub stars. The repository holds 177 skills in this directory. The repository was last updated on September 14, 2026.
Source: linkfox-ai/linkfox-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.