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Linkfox Amazon Store Catalog

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

Helps with ai & agent building tasks.

About

linkfox-amazon-store-catalog is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • linkfox-amazon-store-catalog
  • AI & Agent Building
  • AI-coding skill

Linkfox Amazon Store Catalog by the numbers

  • 182 all-time installs (skills.sh)
  • +35 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #3,035 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-amazon-store-catalog

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Listed on Skillselion
Installs182
repo stars64
Last updatedAugust 3, 2026
Repositorylinkfox-ai/linkfox-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Amazon 店铺 Catalog Items

本 skill 与 `linkfox-amazon-store-auth` 等同属 Amazon Store 系列:先 `POST /spApi/storeTokens`,再 `POST /spApi/developerProxy` 转发 GET

官方参考索引

能力文档
listCatalogCategorieslistCatalogCategories
searchCatalogItemssearchCatalogItems
getCatalogItemgetCatalogItem

---

Prerequisites

1. 依赖 `linkfox-amazon-store-auth`python scripts/check_auth_dependency.py,exit 42 时需先安装授权 skill。 2. 应用需具备 Catalog Items 相关角色;searchCatalogItemsidentifiers+SKU 检索时 query 须带 `sellerId`(脚本在 identifiersType=SKU 时自动使用入参 sellerId)。

---

Current Capabilities

能力path脚本
listCatalogCategoriescatalog/v0/categorieslist_catalog_categories.py
searchCatalogItems`catalog/{2022-04-01\2020-12-01}/items`
getCatalogItemcatalog/{version}/items/{asin}get_catalog_item.py

默认 Catalog Items 版本:`2022-04-01`;入参 `catalogItemsVersion` 可改为 `2020-12-01`

共享模块:`_spapi_catalog_common.py`

---

Quick Parameters

  • listCatalogCategoriesmarketplaceId + `asin``sellerSku`(二选一)。
  • searchCatalogItemsmarketplaceIds + `keywords``identifiers` + `identifiersType`(互斥);可选 includedDatabrandNamesclassificationIdspageSizepageToken
  • getCatalogItemasinmarketplaceIds;可选 includedDatalocale

---

Scripts

export LINKFOXAGENT_API_KEY="<your-key>"

python scripts/list_catalog_categories.py '{"sellerId":"A1...","region":"NA","marketplaceId":"ATVPDKIKX0DER","asin":"B08N5WRWNW"}'

python scripts/search_catalog_items.py '{"sellerId":"A1...","region":"NA","marketplaceIds":["ATVPDKIKX0DER"],"keywords":["wireless mouse"]}'

python scripts/get_catalog_item.py '{"sellerId":"A1...","region":"NA","asin":"B08N5WRWNW","marketplaceIds":["ATVPDKIKX0DER"],"includedData":["summaries","images"]}'

---

Display Rules

1. 先看 `developerProxy.errcode` / `httpStatus`,再读 `categories` / `catalogItems` / `catalogItem`。 2. listCatalogCategories 使用 v0 查询键 `MarketplaceId`(单数),与 search/get 的 `marketplaceIds` 不同。 3. 网关 path 白名单需包含 `catalog/v0/``catalog/2022-04-01/`(或 2020-12-01)。

---

Important Limitations

  • 本 skill 读的是 Amazon 商品目录(Catalog),不是卖家订单;订单见 `linkfox-amazon-store-orders`
  • includedData、返回字段以 Amazon schema 为准,详见 `references/api.md`

Feedback: skillNamelinkfox-amazon-store-catalog

--- 更多跨境 skill:[LinkFox Skills](https://skill.linkfox.com/)

<!-- 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/check_auth_dependency.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.
This skill exposes multiple entry scripts: check_auth_dependency.py, get_catalog_item.py, list_catalog_categories.py, search_catalog_items.py. Pass --script scripts/<name>.py to choose the one you need.

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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