Agent skill

Linkfoxagent

by LeoYeAI in LeoYeAI/openclaw-master-skills

Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis…

MITAuto-check passedLegal & Compliance

Install Linkfoxagent

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills linkfoxagent --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/linkfoxagent .claude/skills/linkfoxagent && rm -rf skills-src

Use ~/.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/

Facts

Skill name
linkfoxagent
GitHub stars
2.2k
Token cost
~3.8k tokens
SKILL.md length
988 words
Files
17 (incl. scripts, references)
Skills in repo
972
Repo updated
First seen
Licence
MIT

At a glance

Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis…

  • Works in 2 steps: Get your API key:… → Set environment variable: export…
  • Product selection and market analysis
  • SKILL.md covers Setup, MANDATORY: Use sessions_spawn…, Writing Task Prompts and Tool Selection Priority, plus 3 more sections
  • Runs Python scripts from its folder; calls python3; reaches yxgb3sicy7.feishu.cn; needs LINKFOXAGENT_API_KEY

What it does

Linkfoxagent is an agent skill from LeoYeAI/openclaw-master-skills. Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis, 1688 sourcing, and AI image generation. Use when: (1) product selection and market analysis, (2) competitor research and ASIN lookup, (3) keyword and traffic analysis, (4) review mining and consumer insights, (5) patent/trademark/copyright detection, (6) Google/TikTok trend research, (7) 1688 supplier sourcing, (8)…

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 18 other files, including scripts and reference files (for example `_meta.json`, `references/1688.md` and `references/ai-tools.md`).

It sits in Legal & Compliance, covering Intellectual property, Competitor analysis and Image generation. It works with TikTok and 1688. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Product selection and market analysis
  • Competitor research and ASIN lookup
  • Keyword and traffic analysis
  • Review mining and consumer insights

Example prompts

  • “/linkfoxagent”

Requirements

  • Python 3
  • A credential in LINKFOXAGENT_API_KEY

Workflow steps

2 steps, taken from the first numbered list in SKILL.md.

  1. Get your API key: https://yxgb3sicy7.feishu.cn/wiki/IlkawdQP9ifKv9k22xcc7rjmnkb
  2. Set environment variable: export LINKFOXAGENT_API_KEY=your-key-here

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. 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 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • yxgb3sicy7.feishu.cn

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LINKFOXAGENT_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Linkfoxagent loads about 3.8k tokens when it runs, and up to ~25k if it reads all its reference files. Until then it costs about 180 tokens; SKILL.md has 988 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~180
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~25k

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 LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 988 words, ~3,814 tokens.

Download SKILL.mdSave it as .claude/skills/linkfoxagent/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.
name
linkfoxagent
description
Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis, 1688 sourcing, and AI image generation. Use when: (1) product selection and market analysis, (2) competitor research and ASIN lookup, (3) keyword and traffic analysis, (4) review mining and consumer insights, (5) patent/trademark/copyright detection, (6) Google/TikTok trend research, (7) 1688 supplier sourcing, (8) data aggregation and report generation, (9) cross-platform product search (Amazon/Walmart/eBay/TikTok), (10) product image analysis and similarity grouping, (11) AI product image generation.

LinkFoxAgent - Cross-border E-commerce AI Agent

LinkFoxAgent is a specialized AI agent for cross-border e-commerce with 42 built-in tools covering product research, competitor analysis, keyword tracking, review insights, patent detection, AI image generation, and more.

Setup

  1. Get your API key: https://yxgb3sicy7.feishu.cn/wiki/IlkawdQP9ifKv9k22xcc7rjmnkb
  2. Set environment variable: export LINKFOXAGENT_API_KEY=your-key-here

MANDATORY: Use sessions_spawn for All Tasks

NEVER call linkfox.py directly from the main session. LinkFoxAgent tasks take 1-5 minutes. You MUST use sessions_spawn to dispatch every task to a sub-agent. This keeps the main session responsive and delivers results automatically when done.

How to Dispatch a Task

Before calling sessions_spawn, tell the user in the main session:

「正在向 LinkFox Agent 提交任务,请稍候(通常需要 1-5 分钟)...」

Then dispatch the sub-agent:

sessions_spawn:
  task: |
    Run the following LinkFoxAgent task and report the results back.

    Command (use heredoc to avoid shell injection):
    python3 <skill>/scripts/linkfox.py --wait --timeout 600 --stdin <<'__LINKFOX_TASK_END__'
    <TASK_PROMPT>
    __LINKFOX_TASK_END__

    The script prints to stderr: "Task submitted. messageId: <id>" if submission succeeds,
    or an error message and exits with code 1 if submission fails.

    After running the command, follow these rules strictly:

    ## If the command exits with a non-zero code OR stderr contains "Error" before any messageId:
    - The task submission FAILED. Report back:
      「任务发起失败。请检查 LINKFOXAGENT_API_KEY 是否已正确配置:
        1. 确认环境变量已设置:export LINKFOXAGENT_API_KEY=your-key-here
        2. 获取 API Key:https://yxgb3sicy7.feishu.cn/wiki/IlkawdQP9ifKv9k22xcc7rjmnkb
        3. 重启 OpenClaw 网关使环境变量生效
      错误详情:<stderr 内容>」

    ## If stderr contains "Task submitted. messageId: <id>":
    - Submission SUCCEEDED. Do NOT send any intermediate message — the main agent has already told the user the task is dispatched. Wait silently for the command to finish (stdout).

    ## After the command completes (stdout):
    1. Parse stdout — it contains a status line, an optional ShareURL, a reflection summary, and result entries.
    2. If status is "error" or "cancel", report the error clearly.
    3. If status is "finished", summarize the reflection and list all results.
    4. HTML report URLs in results are available for your reference. Decide autonomously whether to share them with the user based on context — do not forward them blindly.
    5. **ShareURL:** If the output contains a line `ShareURL: <url>`, always forward it to the user verbatim. This is the full conversation share link for this LinkFoxAgent run — the user can open it to review the complete execution process and download all related files from that page.
    6. **CSV output (JSON results with columns):** When a result line says `CSV saved to: <path>`, the script has already converted the JSON data to a CSV file with Chinese column headers at that local path. Report the path to the user. Do NOT attempt to read or display the CSV contents unless the user explicitly asks. If the user wants to receive the file, send it using the file-sending skill.
  label: "LinkFox: <short description>"
  mode: "run"
  runTimeoutSeconds: 600
  cleanup: "keep"
Dispatching Multiple Independent Tasks

When the user's request involves multiple independent lookups (e.g., "search both Amazon US and Amazon JP"), spawn one sub-agent per task in parallel.

Before spawning, tell the user:

「正在同时向 LinkFox Agent 提交 N 个任务,请稍候...」

# Sub-agent 1
sessions_spawn:
  task: |
    Run (use heredoc to avoid shell injection):
    python3 <skill>/scripts/linkfox.py --wait --timeout 600 --stdin <<'__LINKFOX_TASK_END__'
    <task A>
    __LINKFOX_TASK_END__
    Apply the same submission success/failure reporting rules as the single-task template above.
  label: "LinkFox: task A"
  mode: "run"
  runTimeoutSeconds: 600

# Sub-agent 2
sessions_spawn:
  task: |
    Run (use heredoc to avoid shell injection):
    python3 <skill>/scripts/linkfox.py --wait --timeout 600 --stdin <<'__LINKFOX_TASK_END__'
    <task B>
    __LINKFOX_TASK_END__
    Apply the same submission success/failure reporting rules as the single-task template above.
  label: "LinkFox: task B"
  mode: "run"
  runTimeoutSeconds: 600
Multi-Step Tasks That Require Post-Processing

When the user's request requires multiple sequential LinkFoxAgent calls (e.g., fetch data from two platforms then merge), follow this pattern:

  1. Run each LinkFoxAgent call as a separate sessions_spawn, one after another (or in parallel if independent). Collect the CSV paths returned by each.
  2. After all data tasks finish, spawn one final sessions_spawn to process or merge the CSVs using Python. Pass the absolute CSV paths as arguments.
# Final merge/processing step — spawned after all data tasks complete
sessions_spawn:
  task: |
    Run the following Python script to process/merge the CSV files and report results.

    python3 - <<'PYEOF'
    import csv, sys, os

    # Paths passed in from the data tasks above
    csv_paths = [
        "/absolute/path/to/result_1_xxx.csv",
        "/absolute/path/to/result_2_yyy.csv",
    ]

    # TODO: implement merge / analysis logic here
    # Example: read all rows and write a combined CSV
    all_rows = []
    headers = None
    for path in csv_paths:
        with open(path, encoding="utf-8-sig") as f:
            reader = csv.DictReader(f)
            if headers is None:
                headers = reader.fieldnames
            for row in reader:
                all_rows.append(row)

    out_path = os.path.join(os.path.dirname(csv_paths[0]), "merged_output.csv")
    with open(out_path, "w", newline="", encoding="utf-8-sig") as f:
        writer = csv.DictWriter(f, fieldnames=headers)
        writer.writeheader()
        writer.writerows(all_rows)

    print(f"Merged CSV saved to: {out_path}")
    PYEOF

    Report the output path back to the user. If the user wants the file, send it using the file-sending skill.
  label: "LinkFox: merge/process CSVs"
  mode: "run"
  runTimeoutSeconds: 120
  cleanup: "keep"
What Happens Under the Hood
  1. sessions_spawn creates an isolated sub-agent session
  2. The sub-agent runs linkfox.py --wait which blocks until the task finishes
  3. When done, the sub-agent's result is automatically delivered back to the main session via the announce system
  4. The user sees the result in their chat without any manual polling
Script Reference
bash
# The sub-agent uses --wait + --stdin mode (heredoc avoids shell injection)
python3 <skill>/scripts/linkfox.py --wait --stdin <<'__LINKFOX_TASK_END__'
task description here
__LINKFOX_TASK_END__

# Custom timeout (default 300s)
python3 <skill>/scripts/linkfox.py --wait --timeout 600 --stdin <<'__LINKFOX_TASK_END__'
task description here
__LINKFOX_TASK_END__

# JSON output for structured parsing
python3 <skill>/scripts/linkfox.py --wait --format json --stdin <<'__LINKFOX_TASK_END__'
task description here
__LINKFOX_TASK_END__

Writing Task Prompts

Tool Invocation Syntax

Use @工具中文名 to invoke tools. Multiple tools can be chained in a single task (max 10).

Example: @卖家精灵-选产品 筛选亚马逊美国站的 "usb charger cable",返回前40条商品数据

Parameter Constraints

Tool parameters may have maximum, minimum, and pattern constraints. Prompts must respect these or the call will fail. See the reference files below for details.

Multi-step Tasks

Chain multiple tools in numbered steps. LinkFoxAgent handles data flow between steps:

1、@亚马逊前端搜索模拟 帮我在美国亚马逊站搜索 "computer desk",返回前2页商品数据
2、@对商品标题进行分词 统计上一步商品标题中出现的功能点
3、按功能点统计月销量、月销售额、asin数

Tool Selection Priority

When the user does not specify a tool, follow these rules:

Querying Amazon product data — all three tools are fast; choose by use case:

  1. Keepa — best overall: richest fields, strong real-time accuracy. Default choice for most queries.
  2. 卖家精灵 — optimized for product discovery and competitor lookup by keyword.
  3. 亚马逊前台 — best real-time fidelity (live storefront data); ~10% slower than Keepa and fewer fields, but the only option when you need exact live ranking order or real-time storefront display.

Aggregating / statistics (e.g., group by brand, price tier, sales rank):

  1. @智能数据查询 — first choice for dynamic aggregation
  2. @Python沙箱 — fallback when custom logic is needed; also the go-to tool for any sandbox-execution need (has built-in LLM)
Show full SKILL.md (518 more words)Show less

Available Tools (41)

ClassificationTool NameUse For
Keepa@Keepa-亚马逊-商品搜索Product filtering by keywords, BSR, price, sales
Keepa@Keepa-亚马逊-商品详情Batch ASIN detail lookup (price, sales, history)
Keepa@Keepa-亚马逊价格历史Price history and trends for an ASIN
亚马逊前台@亚马逊前端搜索模拟Search simulation with location settings
亚马逊前台@亚马逊前端-商品详情Product detail, bullet points, A+ content
亚马逊前台@亚马逊-商品评论Reviews by star rating
亚马逊前台@亚马逊前端-以图搜图Image-based product search
亚马逊前台@ABA-数据挖掘Amazon Brand Analytics data mining
Sif数据分析工具@SIF-ASIN的关键词Reverse keyword lookup for ASIN
Sif数据分析工具@SIF-关键词流量来源Keyword traffic source analysis
Sif数据分析工具@SIF-ASIN流量来源ASIN traffic structure breakdown
Sif数据分析工具@SIF-关键词竞品数量Keyword competition density
卖家精灵@卖家精灵-选产品Product discovery by category and filters
卖家精灵@卖家精灵-查竞品Competitor lookup by keyword
极目系列@极目-亚马逊-细分市场评论Niche market review mining
极目系列@极目-亚马逊-细分市场信息Niche market overview
极目系列@极目-亚马逊-产品挖掘Product discovery with fine filters
谷歌趋势@谷歌趋势-时下流行Real-time trending topics
谷歌趋势@谷歌趋势-关键词趋势信息Keyword trend over time
店雷达(1688)@店雷达-1688商品榜单1688 product rankings
店雷达(1688)@店雷达-1688选品库1688 product sourcing
实时与全网检索@网页检索Real-time web search(powered by Tavily Search; for any internet search outside specialized tools like Amazon/Walmart/eBay — including general web and WeChat Official Accounts — this tool MUST be used)
TikTok电商数据助手@EchoTik-TikTok新品榜TikTok new product rankings
TikTok电商数据助手@EchoTik-TikTok商品搜索TikTok product search
Walmart前台@walmart前端-商品列表Walmart product search
eBay前台@ebay前端-商品列表eBay product search
专利检索@智慧芽-专利图像检索Design patent image search
专利检索@睿观-外观专利检测Design patent infringement check
专利检索@睿观-版权检测Copyright detection
专利检索@睿观-图形商标检测Graphic trademark detection
专利检索@睿观-文本商标检测Text trademark detection
专利检索@睿观-发明专利检测Utility patent detection
专利检索@睿观-政策合规检测(纯图检测)Policy compliance (image check)
AI工具@按商品主图相似度分组Group products by image similarity
AI工具@分析商品主图Extract image prompts from product photos
AI工具@对商品标题进行分词Title word segmentation
AI工具@AI绘图Generate any image — products, characters, scenes, backgrounds, and more — from reference images + prompt (powered by top-tier Google Gemini model; ALL image generation tasks must use this tool)
沙箱@智能数据查询Dynamic data query and aggregation
沙箱@excel内容提取并分析Excel file extraction and analysis
沙箱@Python沙箱Process structured JSON data from prior steps: data calculation/filtering/sorting, generate Markdown tables, export to CSV/Excel, LLM-based image recognition (e.g. A+ image color/composition). Built-in LLM — use for ALL sandbox-execution needs. Restrictions: no nested calls; structured JSON only (no plain text/files); no chart generation or analysis reports.
沙箱@智能Excel处理Smart Excel processing
Tool Reference Files (by classification)

Read the relevant reference file when you need prompt templates and parameter constraints:

  • Keepa (3 tools: 商品搜索、商品详情、价格历史): See references/keepa.md
  • 亚马逊前台 (5 tools: 搜索模拟、商品详情、评论、ABA、以图搜图): See references/amazon-frontend.md
  • Sif数据分析工具 (4 tools: ASIN关键词、流量来源、竞品数量): See references/sif.md
  • 卖家精灵 (2 tools: 选产品、查竞品): See references/seller-sprite.md
  • 极目系列 (3 tools: 细分市场评论、市场信息、产品挖掘): See references/jimu.md
  • 谷歌趋势 (2 tools: 时下流行、关键词趋势): See references/google-trends.md
  • 实时与全网检索 (1 tool: 网页检索): See references/web-search.md
  • TikTok电商数据助手 (2 tools: 新品榜、商品搜索): See references/tiktok.md
  • Walmart前台 (1 tool: 商品列表): See references/walmart.md
  • eBay前台 (1 tool: 商品列表): See references/ebay.md
  • 店雷达/1688 (2 tools: 商品榜单、选品库): See references/1688.md
  • 专利检索 (7 tools: 外观专利、版权、商标、发明专利、政策合规): See references/patent.md
  • AI工具 (4 tools: 主图相似度分组、主图分析、标题分词、AI绘图): See references/ai-tools.md
  • 沙箱 (4 tools: 智能数据查询、Excel分析、Python沙箱、Excel处理): See references/sandbox.md

Examples

Example 1: Market Analysis
1、@卖家精灵-选产品 筛选亚马逊美国站的 "usb charger cable",返回符合条件的 40 条商品数据
2、@智能数据查询 根据品牌、评分值、价格(每2美金一个阶梯) 统计月销量、月销售额、月销量占比、月销售额占比
3、生成对应的初步市场分析报告
Example 2: Review Mining
@亚马逊-商品评论 @亚马逊前端-商品详情 亚马逊美国站,asin为B00163U4LK 的详情以及每个星级各100条
进行总结:展示他的人群特征、使用时刻、使用地点、使用场景、未被满足的需求、好评、差评、购买动机,每个要点要有描述、原因、数量占比。并最终给我一个改良建议
Example 3: Competitor-based Listing Optimization
努力思考,选择适合以下场景的工具,完美完成以下任务:
亚马逊美国站,asin为:B0FPZHSLYR、B0CP9Z56SW、B0FFNF9TK1、B0FS7DRCLZ、B0CP9WRDFV、B0BWMZDCCN,我的竞品就是这些,你参考他们的五点描述和A+页面内容,生成我的商品的标题、五点描述
步骤:
1)查询以上所有asin的商品详情
2)查询每个asin的关键词
3)将上一步的全部关键词,构建关键词价值打分表
4)写作前再次查询亚马逊五点描述的写作要求和Amazon cosmo算法和经典营销理论FABE法则
5)生成5点描述,要求竞品的品牌词不能作为关键词,写出符合FABE法则和最新Amazon cosmo算法的五点描述,并且将关键词价值打分表价值高的词埋入
Example 4: Visual Market Analysis
1、@亚马逊前台模拟搜索工具 筛选亚马逊美国站的,关键词为necklaces for women,默认排序,第一页的商品
2、对上一步的商品主体,统计主图不同挂件形状的销售额,绘制出不同形状的销售额占比
3、进行总结:把步骤二的数据完整的用精美的html网页显示给我看(不要精简)
Example 5: Keyword Functional Analysis
1、@亚马逊前端搜索模拟 帮我在美国亚马逊站,以"computer desk"为关键词进行搜索,同时将配送地址设置为洛杉矶,最终返回搜索结果前2页的商品数据
2、@对商品标题进行分词 统计上一步商品标题中出现的功能点
3、按功能点统计月销量、月销售额、asin数

Retry on Failure

If a tool call fails, the response includes error details. Retry with adjusted parameters based on the error message. Common issues:

  • Parameter out of range (check min/max constraints)
  • Invalid pattern format (check regex patterns)
  • Too many tools in one task (max 10)

© LeoYeAI, 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 16 other files (scripts, references) in skills/linkfoxagent of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • references/1688.md
  • references/ai-tools.md
  • references/amazon-frontend.md
  • references/ebay.md
  • references/google-trends.md
  • references/jimu.md
  • references/keepa.md
  • references/patent.md
  • references/sandbox.md
  • references/seller-sprite.md
  • references/sif.md
  • references/tiktok.md
  • references/walmart.md
  • references/web-search.md
  • scripts/linkfox.py

Open the folder on GitHubat commit e5199b5

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Memstack Product Feedback Analyzercwinvestments/memstack423—~2.7kAutomated safety check: PassProprietary
Product Description Generatornexscope-ai/eCommerce-Skills1.1k—~3.3kAutomated safety check: PassMIT
Tiktok Shop Cross Bordernexscope-ai/eCommerce-Skills1.1k—~2.5kAutomated safety check: PassMIT
Amazon Product Search Extractorbrowser-act/skills6.1k2 repos~1.5kAutomated safety check: PassMIT

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All 972 skills in this repo
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Works with

Questions about Linkfoxagent

What does Linkfoxagent do?

Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis…. Linkfoxagent is an agent skill from LeoYeAI/openclaw-master-skills. Cross-border e-commerce AI Agent with 41 specialized tools for Amazon/TikTok/eBay/Walmart product research, competitor analysis, keyword tracking, review insights, patent detection, trend analysis, 1688 sourcing, and AI image generation.

When should I use Linkfoxagent?

Linkfoxagent fits situations like: product selection and market analysis; competitor research and ASIN lookup; keyword and traffic analysis; review mining and consumer insights.

How do I install Linkfoxagent in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a claude-code`. Or copy the skill folder (skills/linkfoxagent in LeoYeAI/openclaw-master-skills) into .claude/skills/linkfoxagent in your project. Claude Code loads it when a task matches its description.

How do I install Linkfoxagent in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill linkfoxagent -a codex`. Or copy the skill folder (skills/linkfoxagent in LeoYeAI/openclaw-master-skills) into .agents/skills/linkfoxagent in your project. Codex loads it when a task matches its description.

Can I use Linkfoxagent 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 LeoYeAI/openclaw-master-skills --skill linkfoxagent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/linkfoxagent, .gemini/skills/linkfoxagent, .github/skills/linkfoxagent and .opencode/skills/linkfoxagent in your project.

What does Linkfoxagent need to run?

Going by SKILL.md and its folder, Linkfoxagent needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named LINKFOXAGENT_API_KEY. Our summary lists: Python 3; A credential in LINKFOXAGENT_API_KEY.

Does Linkfoxagent access the network?

SKILL.md names 1 domain. In commands or code: yxgb3sicy7.feishu.cn; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Linkfoxagent 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 Linkfoxagent use?

Linkfoxagent 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 Linkfoxagent use?

About 3.8k tokens (SKILL.md is roughly 15k 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 22k tokens, read only when the agent opens those files.

What are the alternatives to Linkfoxagent?

Skills that share tags, products or a category with Linkfoxagent: Amazon Best Sellers Finder (browser-act/skills, 6.1k stars), Memstack Product Feedback Analyzer (cwinvestments/memstack, 423 stars), Product Description Generator (nexscope-ai/eCommerce-Skills, 1.1k stars) and Tiktok Shop Cross Border (nexscope-ai/eCommerce-Skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Linkfoxagent?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,159 GitHub stars. The repository holds 972 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.