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Linkfox Ehunt Etsy Category Search

  • 132 installs
  • 64 repo stars
  • Updated August 3, 2026
  • linkfox-ai/linkfox-skills

Helps with ai & agent building tasks.

About

linkfox-ehunt-etsy-category-search is a Claude Code skill in the AI & Agent Building category.

  • linkfox-ehunt-etsy-category-search
  • AI & Agent Building
  • AI-coding skill

Linkfox Ehunt Etsy Category Search by the numbers

  • 132 all-time installs (skills.sh)
  • Ranked #3,648 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/linkfox-ai/linkfox-skills --skill linkfox-ehunt-etsy-category-search

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Listed on Skillselion
Installs132
repo stars64
Last updatedAugust 3, 2026
Repositorylinkfox-ai/linkfox-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

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 -->

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