Ehunt Temu Product Query

SKILL.md

---
name: linkfox-ehunt-temu-product-query
version: 1.0.0
category: product-sourcing
description: 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。
---

# 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](references/api.md) 末尾。

## 参考

入参/出参表见 [references/api.md](references/api.md)。

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## 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`.
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