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linkfox-ehunt-shopify-store-query

linkfox-ehunt-shopify-store-query

github.com/linkfox-aiMarketing & growth11389 installsUpdated 2w ago
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About linkfox-ehunt-shopify-store-query

linkfox-ehunt-shopify-store-query is a skill for querying Shopify store data through LinkFox's third-party data service. It describes pagination, filters, sorting, and handling large responses for users researching stores.

Key features

  1. 01Queries Shopify stores through a gateway route
  2. 02Supports pagination and paired range filters
  3. 03Filters by store age and country
  4. 04Sorts by store metrics and direction
  5. 05Persists large responses for selective reading

Use cases

  1. 01Finding stores matching product or traffic ranges
  2. 02Sorting store results by visits or order volume
  3. 03Inspecting selected fields from large result sets

MCP support

It invokes the `ehunt/shopify/storeQuery` route through LinkFox's third-party data service MCP. The gateway handles authentication and upstream routing.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/linkfox-ai/linkfox-skills --skill linkfox-ehunt-shopify-store-query

After installing, type/linkfox-ehunt-shopify-store-queryin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
api.md
scripts/
ehunt_shopify_store_query.py
response_io.py

Details

From the project README

EHunt Shopify 店铺查询（ehunt/shopify/storeQuery）

在具备 LinkFox「第三方数据服务」MCP 时，对应网关路由 ehunt/shopify/storeQuery 调用（MCP 展示名：Shopify 店铺查询，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。
  • 区间入参：*Min / *Max 成对出现（产品数、广告数、月访问量、月订单量），组成上游区间。
  • 店铺年限 year：1=最近 1 年、2=12 年、3=23 年、4=3 年以上。
  • 排序：sortBy 整数枚举（0=产品数,1=类目数,2=月访问量,3=FB 粉丝,4=Ins 粉丝,5=广告数,6=相关度,7=月订单数默认）；orderBy 为 desc（默认）/asc。
  • 国家：country 传国家代码（如 US、CN）。

脚本（可选）

命令行调试：python scripts/ehunt_shopify_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。

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_shopify_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.

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