linkfox-ehunt-temu-product-query

Original🇺🇸 English
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通过 EHunt Temu 商品查询(网关路由 `ehunt/temu/productQuery`)按多维度筛选 Temu 商品(关键词/商品 ID/店铺 ID、前后台类目、价格、评分、评论、总/周/日销量、上架时间、全托管/半托管、半托管地区、标签等)。当用户提到 EHunt Temu 商品、Temu 选品、拼多多跨境、Temu 爆款、Temu 半托管、全托管商品、Temu product query、temu items 时触发。即使用户未写 EHunt,只要在 Temu 上搜商品、看销量/评分/价格或筛品,也应触发此技能。

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NPX Install

npx skill4agent add linkfox-ai/linkfox-skills linkfox-ehunt-temu-product-query

Tags

Translated version includes tags in frontmatter

EHunt Temu 商品查询(
ehunt/temu/productQuery

在具备 LinkFox「第三方数据服务」MCP 时,对应网关路由
ehunt/temu/productQuery
调用(MCP 展示名:Temu 商品查询,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(建议 ≤50)。
  • 区间入参
    *Begin
    /
    *End
    成对出现(价格、评分、评论、总/周/日销量、上架时间),组成上游区间。
  • 类目
    categoryHome
    前台类目 ID、
    categoryBackend
    后台类目 ID;可先用 Temu 品类检索拿到 id。
  • 托管模式
    isLocal
    (0=全托管,1=半托管);半托管可用
    region
    限定地区(多个逗号分隔)。
  • 上下架
    soldOut
    (0=上架,1=下架)。
  • 标签
    tags
    /
    customTags
    多个用逗号分隔。
  • 排序
    sortBy
    为「字段-方向」字符串,如
    order_week-0
    (周销量降序,默认)、
    price-0
    order_total-0
    rating-0

脚本(可选)

命令行调试:
python scripts/ehunt_temu_product_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
<!-- LF_LARGE_RESPONSE_BLOCK -->

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_product_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
.
<!-- /LF_LARGE_RESPONSE_BLOCK -->