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linkfox-ehunt-shopify-product-query

linkfox-ehunt-shopify-product-query

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

linkfox-ehunt-shopify-product-query is a skill for querying Shopify product data through LinkFox's third-party data service. It documents filters, sorting, pagination, and ways to save and inspect large responses.

Key features

  1. 01Queries Shopify products through a gateway route
  2. 02Supports pagination and range filters
  3. 03Filters by ads, suppliers, and deleted status
  4. 04Sorts by sales, price, ads, and other metrics
  5. 05Persists large responses for selective reading

Use cases

  1. 01Finding products within price or sales ranges
  2. 02Filtering products by ads or supplier availability
  3. 03Extracting selected fields from large query results

MCP support

It invokes the `ehunt/shopify/productQuery` 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-product-query

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

Bundled files

References and scripts installed along with SKILL.md.

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

Details

From the project README

EHunt Shopify 商品查询（ehunt/shopify/productQuery）

在具备 LinkFox「第三方数据服务」MCP 时，对应网关路由 ehunt/shopify/productQuery 调用（MCP 展示名：Shopify 商品查询，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（建议 ≤50）。
  • 区间入参：*Min / *Max 成对出现，组成上游区间；只填一侧时上游为「起始~」或「~结束」。
  • 排序：sortBy 为整数枚举（默认 14=周销量降序，另含价格/广告数/竞争度/销售额等多种取值，详见 references/api.md）。
  • 布尔类筛选：facebookAd（1=有广告）、hasSupplier（1=有货源，0=无）、showDeleted（1=含已下架）均为整数开关。
  • 发货国家：country 传两位国家代码（如 US）。

脚本（可选）

命令行调试：python scripts/ehunt_shopify_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。

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_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.

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