dvcrn LinkFoxAgent - Cross-border E-commerce AI Agent
LinkFoxAgent is a specialized AI agent for cross-border e-commerce with 41 built-in tools covering product research, competitor analysis, keyword tracking, review insights, patent detection, and more.
Setup
- Get your API key: https://yxgb3sicy7.feishu.cn/wiki/IlkawdQP9ifKv9k22xcc7rjmnkb
- 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
sessions_spawn:
task: |
Run the following LinkFoxAgent task and report the results back.
Command:
<skill>/scripts/linkfox.py --wait --timeout 600 '<TASK_PROMPT>'
After the command completes:
1. Parse the output — it contains a status line, a reflection summary, and result entries (HTML URLs or JSON data).
2. If the status is "error" or "cancel", report the error clearly.
3. If the status is "finished", summarize the reflection and list all result URLs/data.
4. If results contain HTML report URLs, include them so the user can open them directly.
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:
# Sub-agent 1
sessions_spawn:
task: "Run: <skill>/scripts/linkfox.py --wait '<task A>' ..."
label: "LinkFox: task A"
mode: "run"
runTimeoutSeconds: 600
# Sub-agent 2
sessions_spawn:
task: "Run: <skill>/scripts/linkfox.py --wait '<task B>' ..."
label: "LinkFox: task B"
mode: "run"
runTimeoutSeconds: 600
What Happens Under the Hood
sessions_spawncreates an isolated sub-agent session- The sub-agent runs
linkfox.py --waitwhich blocks until the task finishes - When done, the sub-agent's result is automatically delivered back to the main session via the announce system
- The user sees the result in their chat without any manual polling
Script Reference
# The sub-agent uses --wait mode (blocking, used inside sessions_spawn only)
<skill>/scripts/linkfox.py --wait "task description"
# Custom timeout (default 300s)
<skill>/scripts/linkfox.py --wait --timeout 600 "task description"
# JSON output for structured parsing
<skill>/scripts/linkfox.py --wait --format json "task description"
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 — prefer in this order:
- Keepa — historical/structured data, most efficient
- 卖家精灵 — product discovery and competitor lookup
- 亚马逊前台 — only when real-time data is needed, or when the user wants the actual ranking order as displayed on Amazon's storefront (slower)
Aggregating / statistics (e.g., group by brand, price tier, sales rank):
- @智能数据查询 — first choice for dynamic aggregation
- @Python沙箱 — fallback when custom logic is needed
@网页解析 — opens a remote browser session, takes the longest time of all tools. Use only when no other tool can retrieve the target content.
Available Tools (41)
| Classification | Tool Name | Use 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 |
| 实时与全网检索 | @网页解析 | URL content extraction |
| 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 |
| 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 | Execute Python code in sandbox |
| 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 (2 tools: 网页检索、网页解析): 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 (3 tools: 主图相似度分组、主图分析、标题分词): 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)