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Linkfox Sellersprite Market Research

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

Run SellerSprite Amazon market research to size niches, review competition density, and estimate revenue potential before sourcing or launching a private-label SKU.

About

Invokes SellerSprite to research Amazon categories and niches with sales estimates, competitor counts, price bands, and trend context. Helps agents compare opportunities, avoid oversaturated keywords, and document validate-stage business cases for FBA or merchant-fulfilled launches.

  • Amazon niche demand metrics
  • Competition concentration
  • Revenue and BSR proxies
  • Category tree navigation
  • Agent-driven market scans

Linkfox Sellersprite Market Research by the numbers

  • 244 all-time installs (skills.sh)
  • +38 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #908 of 1,879 Marketing & SEO 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-sellersprite-market-research

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Listed on Skillselion
Installs244
repo stars64
Last updatedAugust 3, 2026
Repositorylinkfox-ai/linkfox-skills

What it does

Run SellerSprite Amazon market research to size niches, review competition density, and estimate revenue potential before sourcing or launching a private-label SKU.

Files

SKILL.mdMarkdownGitHub ↗

SellerSprite Market Research

This skill helps screen and rank Amazon category markets using SellerSprite market-research data.

Core Concepts

  • 类目市场级分析:不是商品级列表,而是按类目/节点聚合后的市场画像。
  • 市场规模:月均销量、月均销售额、商品数量等。
  • 竞争结构:卖家/品牌集中度、头部集中度、自营占比、FBA/FBM 占比。
  • 入参刻度:筛选用的 GoodsCrn / BrandCrn / SellerCrn / EbcProportion / FbaProportion / FbmProportion / AmazonSelfProportionmin*/max*)须为 0~1 小数,见下文参数表与 references/api.md
  • 新品机会:新品数量、新品占比、新品均价/评分/销量等。

API Usage

  • Endpoint: POST https://tool-gateway.linkfox.com/sellersprite/market/research
  • Auth: Header Authorization: <api_key> (LINKFOXAGENT_API_KEY)
  • 完整说明见 references/api.md:含 marketplace / month / orderField 枚举,sellerLocation/newProduct/topNum,以及集中度、新品、头部、重量体积等全部筛选入参;响应含顶层字段与 data[] 类目市场指标、top10Images[] 等。
  • Runnable script: scripts/sellersprite_market_research.py

Key Parameters

接口筛选项与工具 _sellersprite_market_research 一致(70+);下表为常用子集,完整参数与出参字段见 `references/api.md`
参数类型必填说明
marketplacestring站点编码,默认 US
monthstringnearlyyyyyMM
nodeIdPathstring类目节点路径
departmentKeywordstring类目关键字路径
page / sizeinteger分页,默认 1/50,size 最大 200
orderField / orderDescstring/boolean排序字段与方向;orderDesc 默认 true(降序)
minAvgRevenue / maxAvgRevenuenumber月均销售额范围
minAvgUnits / maxAvgUnitsinteger月均销量范围
minGoodsCount / maxGoodsCountinteger商品数量范围
minGoodsCrn / maxGoodsCrnnumber商品集中度(小数 0~1,如 0.4 表示 40%,勿用整数 40
minSellerCrn / maxSellerCrnnumber卖家集中度(小数 0~1
minBrandCrn / maxBrandCrnnumber品牌集中度(小数 0~1
minAmazonSelfProportion / maxAmazonSelfProportionnumberAmazon 自营占比(小数 0~1
minFbaProportion / maxFbaProportionnumberFBA 占比(小数 0~1
minFbmProportion / maxFbmProportionnumberFBM 占比(小数 0~1
minEbcProportion / maxEbcProportionnumberA+ 数量占比(小数 0~1
minNewProportion / maxNewProportionnumber新品占比(刻度可能与上列不同,以 references/api.md / schema 为准)
minAvgPrice / maxAvgPricenumber平均价格范围
minAvgRating / maxAvgRatingnumber平均评分范围
minAvgProfit / maxAvgProfitnumber平均毛利率(%)

Usage Example

{
  "marketplace": "US",
  "month": "nearly",
  "minAvgRevenue": 10000,
  "maxGoodsCrn": 0.4,
  "minNewProportion": 10,
  "maxSellerCrn": 0.5,
  "orderField": "total_amount",
  "orderDesc": true,
  "page": 1,
  "size": 50
}

Display Rules

1. 先给出市场候选 Top N,再展示核心指标(市场规模、集中度、新品占比)。 2. 入参回显GoodsCrn / BrandCrn / SellerCrn / EbcProportion / FbaProportion / FbmProportion / AmazonSelfProportion 对应筛选为 0~1 小数;向用户说明时可换算为百分数(如传 0.4 可表述为「商品集中度上限 40%」)。响应 data[] 里若仍带「(%)」字段,与入参刻度可能不同,以返回为准。 3. 其它比例/毛利率等字段的单位以 references/api.md 为准。 4. 显示筛选条件回显,便于用户复现。 5. 若结果过少或过多,建议用户调整关键阈值(如集中度、规模阈值)。

Important Limitations

  • 必填参数:marketplace
  • 每页最多 200 条
  • 历史月份范围受第三方限制(通常近24个月)

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply: 1. The functionality or purpose described in this skill does not match actual behavior 2. The skill's results do not match the user's intent 3. The user expresses dissatisfaction or praise about this skill 4. Anything you believe could be improved

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

<!-- 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/sellersprite_market_research.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 -->

Related skills

Marketing & SEOecommercepricing

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