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linkfox-ai/linkfox-skills132 installs

linkfox-ehunt-etsy-category-search

通过 `_ehunt_etsyCategorySearch`(MCP 展示名「Etsy品类查询」)在已同步到 MCP 库的 EHunt Etsy 类目数据中检索类目名称、id 与 parentIds,用于商品/店铺筛选的类目 id。当用户提到 EHunt Etsy 类目、Etsy category id、Etsy 类目树、_ehunt_etsyCategorySearch、Etsy品类查询、syncEtsyCategory / _ehunt_syncEtsyCategory(Etsy品类同步)后查类目 时触发。即使用户未写 EHunt,只要在本地已同步的 Etsy 类目库里按关键词找类目 id,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-category-search
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    No security issues detected. This skill provides functionality to search Etsy category data via the EHunt service, using a Python script to interface with the vendor's API gateway.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

EHunt Etsy 类目检索(_ehunt_etsyCategorySearch)

在具备 LinkFox「第三方数据服务」MCP 时,按工具名 _ehunt_etsyCategorySearch 调用(MCP 展示名:Etsy品类查询,以当前环境下发的工具元数据为准)。数据来自 MCP 库本地检索。

前置条件

库内须已有 _ehunt_syncEtsyCategory 写入的全量类目(MCP 展示名:Etsy品类同步)。若无数据或结果为空,应先完成同步再检索。

要点

  • 必填:keyword(子串匹配类目名称、类目 id、parentIds)。
  • 分页:page 从 1 起;pageSize 默认 50、最大 200。
  • 返回的 id 可作为 _ehunt_productQuery / 店铺侧 category 等入参的类目标识(与具体工具 schema 一致即可)。

脚本(可选)

命令行调试:python scripts/ehunt_etsy_category_search.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_category_search.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-category-search">View linkfox-ehunt-etsy-category-search on skillZs</a>