linkfox-ehunt-shopify-store-query

Original：🇺🇸 English

Translated

2 scriptsChecked / no sensitive code detected

通过 EHunt Shopify 店铺查询（网关路由 `ehunt/shopify/storeQuery`）按多维度筛选独立站 Shopify 店铺（店名/域名、国家、创建年限、产品数、广告数、月访问量、月订单量、社媒粉丝等）。当用户提到 EHunt Shopify 店铺、Shopify 店铺分析、独立站店铺、Shopify seller、独立站竞品店铺、Shopify 月访问量、独立站广告库、shopify stores、Shopify store query 时触发。即使用户未写 EHunt，只要在 Shopify 独立站上找店铺、筛店铺数据或分析店铺表现，也应触发此技能。

23installs

Sourcelinkfox-ai/linkfox-skills

Added on2026-07-04

NPX Install

npx skill4agent add linkfox-ai/linkfox-skills linkfox-ehunt-shopify-store-query

SKILL.md Content

View Translation Comparison →

EHunt Shopify 店铺查询（

ehunt/shopify/storeQuery

）

在具备 LinkFox「第三方数据服务」MCP 时，对应网关路由 ehunt/shopify/storeQuery
调用（MCP 展示名：Shopify 店铺查询，确切工具名以当前环境下发的工具元数据为准）。鉴权与上游路由由网关处理；若响应含根级

code

字段，是否成功以实网为准。

要点

分页：
```
page
```
从 1 起；
```
pageSize
```
默认 20、最大 100。
区间入参：
```
*Min
```
/
```
*Max
```
成对出现（产品数、广告数、月访问量、月订单量），组成上游区间。
店铺年限
```
year
```
：1=最近 1 年、2=1~~2 年、3=2~~3 年、4=3 年以上。
排序：
```
sortBy
```
整数枚举（0=产品数,1=类目数,2=月访问量,3=FB 粉丝,4=Ins 粉丝,5=广告数,6=相关度,7=月订单数默认）；
```
orderBy
```
为
```
desc
```
（默认）/
```
asc
```
。
国家：
```
country
```
传国家代码（如
```
US
```
、
```
CN
```
）。

脚本（可选）

命令行调试：

python scripts/ehunt_shopify_store_query.py '<JSON>'

（需

LINKFOXAGENT_API_KEY

）。详见 references/api.md 末尾。

参考

入参/出参表见 references/api.md。

Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/ehunt_shopify_store_query.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"

Pick
--out-dir
outside any git working tree (e.g.
/tmp/...
on Unix,
%TEMP%/...
on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

run

writes the full response to a file and emits only a schema preview + file path.

read

projects specific fields, with

--limit/--offset

for slicing and

--format json|jsonl|csv|table

for output.

When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:

High field count per record, or fields you don't need
Batch/paginated results (multiple items per call)
Long-text fields (descriptions, reviews, HTML, time series)
Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via

read

.

linkfox-ehunt-shopify-store-query

NPX Install

Tags

SKILL.md Content

EHunt Shopify 店铺查询（
`ehunt/shopify/storeQuery`
）

要点

脚本（可选）

参考

Handling Large Responses

linkfox-ehunt-shopify-store-query

NPX Install

Tags

SKILL.md Content

EHunt Shopify 店铺查询（ehunt/shopify/storeQuery）

要点

脚本（可选）

参考

Handling Large Responses

EHunt Shopify 店铺查询（
`ehunt/shopify/storeQuery`
）