linkfox-ehunt-temu-store-query

Original：🇺🇸 English

Translated

2 scriptsChecked / no sensitive code detected

通过 EHunt Temu 店铺查询（网关路由 `ehunt/temu/storeQuery`）按多维度筛选 Temu 店铺（店名/ID、国家站点、后台类目、全托管/半托管、总/周/月销量与销售额、评分、评论、粉丝、商品数、开店时间等）。当用户提到 EHunt Temu 店铺、Temu 店铺分析、Temu seller、Temu 店铺排行、Temu 半托管店铺、Temu 销售额、temu stores、Temu store query 时触发。即使用户未写 EHunt，只要在 Temu 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-temu-store-query

SKILL.md Content

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EHunt Temu 店铺查询（

ehunt/temu/storeQuery

）

在具备 LinkFox「第三方数据服务」MCP 时，对应网关路由 ehunt/temu/storeQuery
调用（MCP 展示名：Temu 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（总/周/月销量、总/周/月销售额、评分、评论、粉丝、商品数），组成上游区间。
站点：
```
siteId
```
国家站点 ID，多个逗号分隔（如
```
211
```
=美国、
```
76
```
=英国）。
类目：
```
category
```
后台类目 ID，多个逗号分隔。
托管模式：
```
isLocal
```
（0=全托管，1=半托管，字符串）。
开店时间：
```
listedTimeBegin
```
/
```
listedTimeEnd
```
（YYYY-MM-DD）。
排序：
```
sortBy
```
为「字段-方向」字符串，如
```
order_week_count-0
```
（周销量降序，默认）、
```
order_count-0
```
、
```
total_revenue-0
```
、
```
rating-0
```
。

脚本（可选）

命令行调试：

python scripts/ehunt_temu_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_temu_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-temu-store-query

NPX Install

Tags

SKILL.md Content

EHunt Temu 店铺查询（
`ehunt/temu/storeQuery`
）

要点

脚本（可选）

参考

Handling Large Responses

linkfox-ehunt-temu-store-query

NPX Install

Tags

SKILL.md Content

EHunt Temu 店铺查询（ehunt/temu/storeQuery）

要点

脚本（可选）

参考

Handling Large Responses

EHunt Temu 店铺查询（
`ehunt/temu/storeQuery`
）