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linkfox-ai/linkfox-skills130 installs

linkfox-ehunt-etsy-store-query

通过 EHunt MCP 工具 `_ehunt_storeQuery`(展示名「Etsy店铺查询」)按多维度筛选 Etsy 店铺(销量、收藏、评论、开店时间、国家、主营类目、Raving/星标等)。当用户提到 EHunt Etsy 店铺、Etsy 店搜、Etsy seller、Etsy 店铺排行、Etsy 周销量店铺、ehunt stores、Etsy店铺查询、_ehunt_storeQuery 时触发。即使用户未写 EHunt,只要在 Etsy 上找店铺、筛店铺数据或分析店铺表现,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-store-query
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    No security issues detected. The skill is designed to query Etsy store information via the vendor's API gateway and follows security best practices for credential management.

  • Socketpass

    No alerts

  • Snykwarn

    Risk: MEDIUM · 1 issue

What does this agent skill do?

EHunt Etsy 店铺查询(_ehunt_storeQuery)

在具备 LinkFox「第三方数据服务」MCP 时,按工具名 _ehunt_storeQuery 调用(MCP 展示名:Etsy店铺查询,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。
  • 区间入参:begin* / end* 成对对应上游逗号范围;只填一侧时上游为「起始~」或「~结束」。
  • 排序:sortBy 仅 8~11(8 总销量、9 周销量、10 评论数、11 收藏数)。sortDesc:1=降序,0=升序(勿与商品接口的 sortDesc 混用)。

脚本(可选)

命令行调试:python scripts/ehunt_etsy_store_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_store_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-store-query">View linkfox-ehunt-etsy-store-query on skillZs</a>