Segment Anything Model Guide
Orchestra-Research/AI-Research-SKILLs
Guide to using Meta's Segment Anything Model for zero-shot image segmentation with point, box or mask prompts, or automatic mask generation.
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…
Install Linkfox Multimodal Extract Attributes
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-extract-attributes -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-extract-attributes --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-extract-attributes .claude/skills/linkfox-multimodal-extract-attributes && 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-extract-attributes" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-extract-attributes into .claude/skills/linkfox-multimodal-extract-attributes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-extract-attributes", 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-extract-attributesType 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-extract-attributes -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-extract-attributes --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-extract-attributes .agents/skills/linkfox-multimodal-extract-attributes && 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-extract-attributes" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-extract-attributes into .agents/skills/linkfox-multimodal-extract-attributes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-extract-attributes", 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-extract-attributes -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-extract-attributes --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-extract-attributes .cursor/skills/linkfox-multimodal-extract-attributes && 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-extract-attributes" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-extract-attributes into .cursor/skills/linkfox-multimodal-extract-attributes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-extract-attributes", 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-extract-attributes--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-extract-attributes -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install linkfox-ai/linkfox-skills linkfox-multimodal-extract-attributes --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-extract-attributes .gemini/skills/linkfox-multimodal-extract-attributes && 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-extract-attributes" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-extract-attributes into .gemini/skills/linkfox-multimodal-extract-attributes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-extract-attributes", 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-extract-attributesInstalls 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-extract-attributes -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-extract-attributes .github/skills/linkfox-multimodal-extract-attributes && 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-extract-attributes" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-extract-attributes into .github/skills/linkfox-multimodal-extract-attributes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-extract-attributes", 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-extract-attributes -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-extract-attributes --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-extract-attributes .opencode/skills/linkfox-multimodal-extract-attributes && 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-extract-attributes" agent skill from https://github.com/linkfox-ai/linkfox-skills/tree/main/skills/linkfox-multimodal-extract-attributes into .opencode/skills/linkfox-multimodal-extract-attributes/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "linkfox-multimodal-extract-attributes", 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-extract-attributes- GitHub stars
- 107
- Token cost
- ~2.2k tokens
- SKILL.md length
- 1,044 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 177
- Repo updated
- First seen
- Licence
- MIT
At a glance
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…
- Works in 5 steps: Be dimension-specific: Clearly state… → One or few dimensions per call: For… → Use concrete terms: "Identify the… → …
- Tasks that involve Computer vision
- SKILL.md covers Core Concepts, Parameter Guide, 调用方式 and 解决认证和算力问题, plus 5 more sections
- Runs Python scripts from its folder; calls python; needs LINKFOX_AGENT_API_KEY and LINKFOXAGENT_API_KEY
What it does
Linkfox Multimodal Extract Attributes is an agent skill from linkfox-ai/linkfox-skills. 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。
Its SKILL.md is about 2.2k 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_extract_attributes.py`).
It sits in AI & LLM Engineering, covering Computer vision. The licence is MIT.
When your agent uses it
- Tasks that involve Computer vision
Example prompts
- “/linkfox-multimodal-extract-attributes”
Requirements
- Python 3
- A credential in LINKFOX_AGENT_API_KEY
- A credential in LINKFOXAGENT_API_KEY
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Be dimension-specific: Clearly state what visual attribute(s) to extract. "Extract the dominant color of each product" is better than…
- 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…
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 Extract Attributes loads about 2.2k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 90 tokens; SKILL.md has 1,044 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). 1,044 words, ~2,181 tokens.
Download SKILL.mdSave it as
.claude/skills/linkfox-multimodal-extract-attributes/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- 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-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。
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") andattributeValue(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.
Show full SKILL.md (436 more words)Show less
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
productsarray 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
refResultDataor 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
算力消耗规则
按动态规则计费：消耗算力 = sum(每张被分析图片的(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-extract-attributes of linkfox-ai/linkfox-skills.
- SKILL.md
- references/api.md
- references/onboarding.md
- scripts/multimodal_extract_attributes.py
- scripts/onboarding.py
Open the folder on GitHubat commit 38fef04
Compare with similar skills
Linkfox Multimodal Extract Attributes 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 Extract Attributes this skilllinkfox-ai/linkfox-skills | 107 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3.3k | Automated safety check: Pass | MIT | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Yolo Master AgentTencent/YOLO-Master | 745 | — | ~755 | Automated safety check: Pass | AGPL-3.0 | |
| Video Understandjjyaoao/HelloAgents | 3.2k | 1 repos | ~6.2k | Automated safety check: Pass | MIT | |
| LLaVA Vision-Language ModelOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~2k | 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 Extract Attributes
What does Linkfox Multimodal Extract Attributes do?
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…. Linkfox Multimodal Extract Attributes is an agent skill from linkfox-ai/linkfox-skills.
When should I use Linkfox Multimodal Extract Attributes?
Linkfox Multimodal Extract Attributes fits situations like: tasks that involve Computer vision.
How do I install Linkfox Multimodal Extract Attributes in Claude Code?
Run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-extract-attributes -a claude-code`. Or copy the skill folder (skills/linkfox-multimodal-extract-attributes in linkfox-ai/linkfox-skills) into .claude/skills/linkfox-multimodal-extract-attributes in your project. Claude Code loads it when a task matches its description.
How do I install Linkfox Multimodal Extract Attributes in Codex?
Run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-multimodal-extract-attributes -a codex`. Or copy the skill folder (skills/linkfox-multimodal-extract-attributes in linkfox-ai/linkfox-skills) into .agents/skills/linkfox-multimodal-extract-attributes in your project. Codex loads it when a task matches its description.
Can I use Linkfox Multimodal Extract Attributes 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-extract-attributes -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-extract-attributes, .gemini/skills/linkfox-multimodal-extract-attributes, .github/skills/linkfox-multimodal-extract-attributes and .opencode/skills/linkfox-multimodal-extract-attributes in your project.
What does Linkfox Multimodal Extract Attributes need to run?
Going by SKILL.md and its folder, Linkfox Multimodal Extract Attributes 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 Extract Attributes 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 Extract Attributes 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 Extract Attributes use?
Linkfox Multimodal Extract Attributes 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 Extract Attributes use?
About 2.2k tokens (SKILL.md is roughly 8.7k 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 Extract Attributes?
Skills that share tags, products or a category with Linkfox Multimodal Extract Attributes: Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars), Yolo Master Agent (Tencent/YOLO-Master, 745 stars) and Video Understand (jjyaoao/HelloAgents, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Linkfox Multimodal Extract Attributes?
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.