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linkfox-ai/linkfox-skills131 installs

linkfox-ehunt-etsy-product-query

通过 EHunt MCP 工具 `_ehunt_productQuery`(展示名「Etsy商品查询」)按多维度筛选 Etsy 商品(关键词/URL、价格、销量、收藏、评论、上架时间、类目、手工/复古等类型、Pick/Bestsell/Raving 等)。当用户提到 EHunt Etsy 商品、Etsy listing、Etsy 选品、Etsy 爆款、Etsy handmade、Etsy vintage、ehunt items、Etsy商品查询、_ehunt_productQuery 时触发。即使用户未写 EHunt,只要在 Etsy 上搜商品、看销量/价格/标签或筛品,也应触发此技能。

How do I install this agent skill?

npx skills add https://github.com/linkfox-ai/linkfox-skills --skill linkfox-ehunt-etsy-product-query
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill provides a mechanism to query Etsy product information, such as sales, prices, and categories, using the EHunt service. It includes a Python script that facilitates interaction with the vendor's API gateway. The skill's behavior is consistent with its stated purpose, and no security risks were identified during the analysis.

  • Socketwarn

    1 alert: gptAnomaly

  • Snykwarn

    Risk: MEDIUM · 1 issue

What does this agent skill do?

EHunt Etsy 商品查询(_ehunt_productQuery)

在具备 LinkFox「第三方数据服务」MCP 时,按工具名 _ehunt_productQuery 调用(MCP 展示名:Etsy商品查询,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:与店铺接口相同思路,begin* / end* 成对。
  • 排序:sortBy 为 1~6(EHunt 上游 sort_by)。sortDesc:1=降序,2=升序(与 _ehunt_storeQuery 的 1/0 不同)。
  • 商品类型 productType:1 手工、2 复古、3 数字、4 定制、9 其他,多选用逗号。
  • 货币:currencyCode 默认 USD。
  • 类目 id:category 为单品类 ID;可先通过类目检索类技能拿到 id。

脚本(可选)

命令行调试:python scripts/ehunt_etsy_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_etsy_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 -->

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/linkfox-ai/linkfox-skills/linkfox-ehunt-etsy-product-query">View linkfox-ehunt-etsy-product-query on skillZs</a>