Agent skill

Linkfox Sellersprite Product Search

by linkfox-ai in linkfox-ai/linkfox-skills

使用卖家精灵数据搜索和筛选亚马逊商品,支持价格、月销量、BSR排名、毛利率、评分、配送方式、标签、卖家来源等多维度条件,覆盖多个亚马逊站点。当用户提到亚马逊选品调研、产品筛选、销量过滤、产品发掘、BSR分析、小众商品发现、竞品分析、市场机会评估、按商品维度的市场规模估算、毛利率筛选、SellerSprite product selection, Amazon product…

MITAuto-check passedMarketing & SEO

Install Linkfox Sellersprite Product Search

skills CLI
$ npx skills add linkfox-ai/linkfox-skills --skill linkfox-sellersprite-product-search -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install linkfox-ai/linkfox-skills linkfox-sellersprite-product-search --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/linkfox-ai/linkfox-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/linkfox-sellersprite-product-search .claude/skills/linkfox-sellersprite-product-search && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
linkfox-sellersprite-product-search
GitHub stars
107
Used in
1 other repo
Token cost
~3.6k tokens
SKILL.md length
1,518 words
Files
5 (incl. scripts, references)
Skills in repo
177
Repo updated
First seen
Licence
MIT

At a glance

使用卖家精灵数据搜索和筛选亚马逊商品,支持价格、月销量、BSR排名、毛利率、评分、配送方式、标签、卖家来源等多维度条件,覆盖多个亚马逊站点。当用户提到亚马逊选品调研、产品筛选、销量过滤、产品发掘、BSR分析、小众商品发现、竞品分析、市场机会评估、按商品维度的市场规模估算、毛利率筛选、SellerSprite product selection, Amazon product…

  • Works in 8 steps: Present data clearly: Show query results… → BSR clarification: When showing BSR… → Gross margin note: Gross margin values… → …
  • Tasks that involve Market research
  • SKILL.md covers Core Concepts, Parameter Guide, 调用方式 and 解决认证和算力问题, plus 5 more sections
  • Runs Python scripts from its folder; calls python; needs LINKFOX_AGENT_API_KEY and LINKFOXAGENT_API_KEY

What it does

Linkfox Sellersprite Product Search is an agent skill from linkfox-ai/linkfox-skills. 使用卖家精灵数据搜索和筛选亚马逊商品,支持价格、月销量、BSR排名、毛利率、评分、配送方式、标签、卖家来源等多维度条件,覆盖多个亚马逊站点。当用户提到亚马逊选品调研、产品筛选、销量过滤、产品发掘、BSR分析、小众商品发现、竞品分析、市场机会评估、按商品维度的市场规模估算、毛利率筛选、SellerSprite product selection, Amazon product selection, sales filtering, BSR analysis, profit screening, market analysis, product selection tool时触发此技能。即使用户未明确提及"卖家精灵",只要其需求涉及筛选和分析亚马逊商品级数据进行选品,也应触发此技能。

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api.md`, `references/onboarding.md` and `scripts/onboarding.py`).

It sits in Marketing & SEO, covering Market research. The licence is MIT.

When your agent uses it

  • Tasks that involve Market research

Example prompts

  • “/linkfox-sellersprite-product-search”

Requirements

  • Python 3
  • A credential in LINKFOX_AGENT_API_KEY
  • A credential in LINKFOXAGENT_API_KEY

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Present data clearly: Show query results in well-structured tables. Key columns to prioritize: ASIN, title, price, monthly sales, monthly…
  2. BSR clarification: When showing BSR data, remind users that lower values mean better rankings
  3. Gross margin note: Gross margin values are percentages. Remind users this is an estimate based on price minus FBA fees and estimated costs
  4. Pagination awareness: When the total count exceeds the returned page size, inform the user of the total result count and suggest adjusting…
  5. Snapshot labeling: When displaying historical snapshot data, clearly label the data period (e.g., "Data from December 2024 snapshot") to…
  6. Error handling: When a query fails, explain the reason based on the message field and suggest adjusting query criteria
  7. Weight unit reminder: When the user provides weight filters without specifying a unit, ask them to confirm the weight unit (g, kg, oz, or…
  8. Keyword translation: When the user provides keywords in a language different from the target marketplace, translate the keyword to the…

What it can do on your machine

Read from SKILL.md and the folder at commit 38fef04. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • skill.linkfox.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LINKFOX_AGENT_API_KEY
    • LINKFOXAGENT_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Linkfox Sellersprite Product Search loads about 3.6k tokens when it runs, and up to ~7.9k if it reads all its reference files. Until then it costs about 95 tokens; SKILL.md has 1,518 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~95
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from linkfox-ai/linkfox-skills at commit 38fef04, republished under its MIT licence (© linkfox-ai). 1,518 words, ~3,635 tokens.

Download SKILL.mdSave it as .claude/skills/linkfox-sellersprite-product-search/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
linkfox-sellersprite-product-search
description
使用卖家精灵数据搜索和筛选亚马逊商品,支持价格、月销量、BSR排名、毛利率、评分、配送方式、标签、卖家来源等多维度条件,覆盖多个亚马逊站点。当用户提到亚马逊选品调研、产品筛选、销量过滤、产品发掘、BSR分析、小众商品发现、竞品分析、市场机会评估、按商品维度的市场规模估算、毛利率筛选、SellerSprite product selection, Amazon product selection, sales filtering, BSR analysis, profit screening, market analysis, product selection tool时触发此技能。即使用户未明确提及"卖家精灵",只要其需求涉及筛选和分析亚马逊商品级数据进行选品,也应触发此技能。

This skill guides you on how to search, filter, and analyze Amazon product data via the SellerSprite product database, helping Amazon sellers make data-driven product selection decisions.

Core Concepts

SellerSprite Product Search provides access to a comprehensive Amazon product database with rich filtering dimensions. It supports real-time data (last 30 days) as well as monthly historical snapshots for year-over-year and month-over-month comparisons. Supported marketplace codes are only: US, UK, DE, FR, JP, CA, IT, ES, MX, and IN (same as the gateway schema).

BSR (Best Sellers Rank): A lower BSR value means better sales performance in its category. A BSR of 1 means the top-selling product in that category. When a user says "BSR improved", it means the numeric value decreased; "BSR dropped" means the value increased.

Data snapshot: The dataSnapshotMonth parameter controls which time period to query. Use nearly (the default) for real-time last-30-day data, or a yyyyMM string (e.g., 202412) to query a historical monthly snapshot. This is useful for seasonal analysis and year-over-year comparison.

Match types for keywords: When searching by keyword, three matching strategies are available:

  • Phrase match (default): Product titles must contain the keyword phrase
  • Fuzzy match: Broader matching with related terms
  • Exact match: Strict exact-string matching

Parameter Guide

Search & Filtering
ParameterTypeDescriptionDefault
keywordstringSearch keyword; translate to the target marketplace language (e.g., English for US, German for DE)-
matchTypeinteger1 = Phrase match, 2 = Fuzzy match, 3 = Exact match1
excludeKeywordsstringKeywords to exclude from results-
marketplacestringMarketplace code (allowed set only): US, UK, DE, FR, JP, CA, IT, ES, MX, INUS
nodeLabelstringAmazon category name-
nodeIdPathstringAmazon category node ID-
filterSubNodebooleanWhether to filter by subcategory node (only effective when nodeLabel or nodeIdPath is set)-
dataSnapshotMonthstringData snapshot month in yyyyMM format, or nearly for real-time last 30 daysnearly
Price & Financials
ParameterTypeDescription
minPrice / maxPricenumberPrice range filter
minProfit / maxProfitnumberGross margin range (1-100, unit: %)
minRevenue / maxRevenuenumberMonthly revenue range
minFba / maxFbanumberFBA fee range
Sales & Ranking
ParameterTypeDescription
minUnits / maxUnitsintegerMonthly sales volume range
minAmzUnit / maxAmzUnitintegerChild-ASIN last-30-day sales range (only when querying last-30-day style data, e.g. dataSnapshotMonth: "nearly")
minUnitsGrowthRate / maxUnitsGrowthRatenumberMonthly sales growth rate (%)
minBsr / maxBsrintegerMain-category BSR rank range
minBsrGrowthRate / maxBsrGrowthRatenumberBSR growth rate (%)
minBsrGrowthCount / maxBsrGrowthCountintegerBSR growth count
minSubNodeBsrRank / maxSubNodeBsrRankintegerSubcategory BSR rank (requires filterSubNode = true)
Reviews & Ratings
ParameterTypeDescription
minRating / maxRatingnumberRating score range (0-5); 3.8-4.3 indicates product improvement opportunity
minRatings / maxRatingsintegerNumber of ratings range (0-10000)
minRatingsGrowthCount / maxRatingsGrowthCountintegerMonthly new ratings count
minListingQualityScore / maxListingQualityScorenumberListing quality score range
Product Attributes
ParameterTypeDescription
minVariations / maxVariationsintegerVariation count range
minWeights / maxWeightsnumberWeight range
weightUnitstringWeight unit: g, kg, oz, lb (required if weight filters are used)
dimensionTypestringPackage dimension type (marketplace-specific codes)
minSellers / maxSellersintegerNumber of sellers range
Badges & Fulfillment
ParameterTypeDescription
badgeBestSellerstringBest Seller badge: Y / N / empty (all)
badgeAmazonsChoicestringAmazon's Choice badge: Y / N / empty (all)
badgeNewReleasestringNew Release badge: Y / N / empty (all)
fulfillmentstringFulfillment type: AMZ, FBA, FBM (comma-separated for multiple)
showVariationstringShow variations: Y / N (default N)
Seller & Brand
ParameterTypeDescription
sellerNationstringSeller country code (e.g., US, CN, HK); comma-separated for multiple
includeSellers / excludeSellersstringInclude / exclude specific sellers
includeBrands / excludeBrandsstringInclude / exclude specific brands
Listing & Pagination
ParameterTypeDescriptionDefault
hideUnlistedProductbooleanHide delisted productstrue
listedWithinLastMonthsintegerListed within last N months (1, 3, 6, 12, or 24)-
pageintegerPage number, starting from 11
sizeintegerResults per page (10-100)20
Sorting

Use the order object with two fields:

FieldTypeDescription
fieldstringSort field: total_units, total_amount, bsr_rank, price, rating, reviews, profit, reviews_rate, available_date, questions, total_units_growth, total_amount_growth, reviews_increasement, bsr_rank_cv, bsr_rank_cr, amz_unit; use "" when you intentionally omit a business sort key (per gateway schema)
descstring"true" for descending, "false" for ascending

Default sort: total_units descending.

Optional gateway / session fields

If the hosting environment supplies them, you may pass chatId, uid, requestId, and teamId as strings (see references/api.md). They are not required for ad-hoc script calls.

调用方式

  • API 端点:POST /sellersprite/productSearch(完整参数/响应/错误码见 references/api.md)
  • Python 脚本:python scripts/sellersprite_product_search.py '<JSON 参数>' [--inline]
  • 成本约束:本工具会消耗算力;同一会话同一参数组合默认只调用一次,脚本带 24h 本地缓存。失败/空结果不得自动换关键词、翻页或改邮编连续试探;需要继续检索时先向用户说明会产生额外消耗。

输出策略(脚本默认行为):

  • 始终将完整响应写入 <cwd>/linkfox/<YYYY-MM-DD>/<session>/data/linkfox-sellersprite-product-search-<timestamp>.json(<cwd> 为脚本执行时的工作目录,在 Claude Code 里即当前项目目录;<session> 取自环境变量 SESSION_ID,按用户任务自动聚合;禁止写入 /tmp,当前目录不可写则报错)
  • 响应体 ≤ 8 KB:落盘后把完整 JSON 打印到 stdout
  • 响应体 > 8 KB:落盘后 stdout 只输出摘要(顶层字段、常见计数如 total/costToken、最大列表字段的长度 + 前 3 条样本)
  • 加 --inline 强制全量打印到 stdout(同样落盘)

读数据建议:先看摘要判断是否足够;需要具体字段时优先用 jq或ConvertFrom-Json 从保存的 json 文件按需抽取,避免整份 JSON 进入上下文。

解决认证和算力问题

发生以下异常情况时,采用 references/onboarding.md 引导解决问题:

异常情况
  • 未配置API Key:环境变量未配置 LINKFOX_AGENT_API_KEY,也未配置 LINKFOXAGENT_API_KEY。
  • 响应401或402状态码
  • 响应提示算力或余额不足:消息含"算力余额不足/计费不足/余额不足/quota exceeded/insufficient balance/套餐到期/需充值/请充值",或类似含义的内容。

Usage Examples

1. Find high-sales products in a niche Search for products with keyword "yoga mat" in the US marketplace with monthly sales above 500 units, sorted by monthly sales descending.

json
{
  "keyword": "yoga mat",
  "marketplace": "US",
  "minUnits": 500,
  "order": {"field": "total_units", "desc": "true"}
}

2. Discover new product opportunities with low competition Find recently listed products (within 6 months) in the US with fewer than 50 ratings and monthly revenue above $5,000.

json
{
  "keyword": "desk organizer",
  "marketplace": "US",
  "listedWithinLastMonths": 6,
  "maxRatings": 50,
  "minRevenue": 5000,
  "order": {"field": "total_units", "desc": "true"}
}

3. Product improvement opportunity mining Find products with ratings between 3.8 and 4.3 (improvement sweet spot), monthly sales above 300, in a specific category.

json
{
  "keyword": "phone case",
  "marketplace": "US",
  "minRating": 3.8,
  "maxRating": 4.3,
  "minUnits": 300,
  "order": {"field": "total_units", "desc": "true"}
}

4. High-margin product screening Find products with gross margin above 40%, price between $15 and $50, at least 100 monthly sales.

json
{
  "marketplace": "US",
  "minProfit": 40,
  "minPrice": 15,
  "maxPrice": 50,
  "minUnits": 100,
  "order": {"field": "profit", "desc": "true"}
}

5. Seasonal year-over-year comparison Query last year's December snapshot data to compare with current data for seasonal product planning.

json
{
  "keyword": "christmas lights",
  "marketplace": "US",
  "dataSnapshotMonth": "202412",
  "minUnits": 200,
  "order": {"field": "total_units", "desc": "true"}
}

6. Chinese seller competitive landscape Find FBA-fulfilled products from Chinese sellers in a category with high monthly sales.

json
{
  "keyword": "bluetooth speaker",
  "marketplace": "US",
  "sellerNation": "CN",
  "fulfillment": "FBA",
  "minUnits": 200,
  "order": {"field": "total_units", "desc": "true"}
}

7. Best Seller & Amazon's Choice badge holders Find products carrying the Best Seller badge with strong sales performance.

json
{
  "keyword": "water bottle",
  "marketplace": "US",
  "badgeBestSeller": "Y",
  "order": {"field": "total_units", "desc": "true"}
}

8. Fast-growing products by sales growth rate Find products with monthly sales growth rate above 50%.

json
{
  "keyword": "standing desk",
  "marketplace": "US",
  "minUnitsGrowthRate": 50,
  "order": {"field": "total_units_growth", "desc": "true"}
}
Show full SKILL.md (587 more words)Show less

Display Rules

  1. Present data clearly: Show query results in well-structured tables. Key columns to prioritize: ASIN, title, price, monthly sales, monthly revenue, BSR rank, rating, ratings count, gross margin, fulfillment type
  2. BSR clarification: When showing BSR data, remind users that lower values mean better rankings
  3. Gross margin note: Gross margin values are percentages. Remind users this is an estimate based on price minus FBA fees and estimated costs
  4. Pagination awareness: When the total count exceeds the returned page size, inform the user of the total result count and suggest adjusting page or size parameters to see more results
  5. Snapshot labeling: When displaying historical snapshot data, clearly label the data period (e.g., "Data from December 2024 snapshot") to avoid confusion with real-time data
  6. Error handling: When a query fails, explain the reason based on the message field and suggest adjusting query criteria
  7. Weight unit reminder: When the user provides weight filters without specifying a unit, ask them to confirm the weight unit (g, kg, oz, or lb) before proceeding
  8. Keyword translation: When the user provides keywords in a language different from the target marketplace, translate the keyword to the appropriate language and note the translation

Important Limitations

  • Result cap: Each page returns a maximum of 100 records (size parameter max is 100)
  • Historical snapshots: Only past monthly snapshots are available; future dates are not supported
  • Weight unit required: If any weight filter is used, the weightUnit must also be provided
  • Subcategory BSR: The subcategory BSR rank filters only work when filterSubNode is set to true
  • Listed time enum only: The listedWithinLastMonths parameter only accepts specific values: 1, 3, 6, 12, or 24
  • Child ASIN 30-day sales filters: minAmzUnit / maxAmzUnit apply only to last-30-day style queries (typically dataSnapshotMonth: "nearly"); do not rely on them for historical yyyyMM snapshots

User Expression & Scenario Quick Reference

Applicable -- Product-level data queries on Amazon:

User SaysScenario
"Find products with high sales in XX category"Niche product search
"Show me low-competition products", "new product opportunities"Blue ocean product discovery
"Which products have high margins"Profitability screening
"Products with rising sales", "trending products"Growth trend detection
"What are Chinese sellers selling well"Competitive landscape analysis
"Recently launched products doing well"New product tracking
"Products with bad reviews but good sales"Product improvement opportunities
"Compare this category with last year"Seasonal / YoY analysis
"FBA products under $30 with 1000+ sales"Multi-criteria product filtering
"Best sellers in XX category"Badge-based product discovery

Not applicable -- Needs beyond product-level search data:

  • ABA search term / keyword analysis (use ABA Data Explorer instead)
  • Advertising / PPC campaign data
  • Product review text analysis
  • Listing copywriting or optimization
  • Supplier sourcing or manufacturing costs
  • Logistics and inventory planning

Boundary judgment: When users say "product research" or "market analysis", if it boils down to filtering Amazon products by sales, price, BSR, ratings, and other product attributes, then this skill applies. If they are asking about keyword search volume, search term rankings, or click/conversion share data, the ABA Data Explorer skill is more appropriate.

算力消耗规则

消耗 15 算力。

用户会因算力消耗而支付费用。请充分评估:当需要高频调用本技能,或用户对算力消耗量预期不足时,务必提醒用户,由用户决定是否继续。

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.


For more high-quality, professional cross-border e-commerce skills, set LinkFox Skills.

© linkfox-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/linkfox-sellersprite-product-search of linkfox-ai/linkfox-skills.

  • SKILL.md
  • references/api.md
  • references/onboarding.md
  • scripts/onboarding.py
  • scripts/sellersprite_product_search.py

Open the folder on GitHubat commit 38fef04

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in linkfox-ai/linkfox-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Creative Directorsmixs/creative-director-skill247—~5.1kAutomated safety check: PassCC-BY-4.0
Audience ResearchScrapeCreators/social-media-research-skills3.4k—~635Automated safety check: NotesMIT
Last 30 Days Trend Researchnexu-io/open-design100k—~1.3kAutomated safety check: PassMIT
Bggg Data Redditbinggandata/bggg-skills605—~1.2kAutomated safety check: PassMIT

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    亚马逊ABA(品牌分析)搜索词数据的查询与分析,涵盖15个站点近3年的周维度数据。当用户提到ABA数据、亚马逊搜索词分析、关键词挖掘、搜索排名趋势、市场机会分析、季节性关键词、高点击低转化分析、蓝海词发现、竞品关键词分析、ABA data, search term report, keyword mining, search ranking trends, blue ocean…

    107 GitHub starsUsed in 1 repo~2.2k tokens
    Auto-check passed
  • Linkfox Amazon Alexa Search

    linkfox-ai/linkfox-skills

    通过亚马逊前台的 Alexa 购物助手发起自然语言问答,获取与问题相关的导购回答、推荐商品分组、ASIN 列表,以及可继续追问的问题。每次调用仅支持 1 条 prompt,如需追问须由 agent 总结上下文后拼接新问题发起新请求。可用 url 补充亚马逊页面上下文。当用户提到亚马逊 Alexa、Alexa 购物助手、亚马逊智能助手、AI…

    107 GitHub starsUsed in 1 repo~3k tokens
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  • 亚马逊反向选品:基于历史商业洞察报告沉淀的指标数据池,按 30+ 项商业维度(市场规模与增长、价格区间与档位份额、竞争密度与头部集中度、人群画像如年龄/性别/收入、评论卖点与痛点等)反向筛选亚马逊赛道与关键词。当用户提到反向选品、指标筛选、细分市场反查、蓝海赛道挖掘、低竞争赛道、新人友好赛道、品牌分散市场、痛点切入、卖点反查、定价档位机会、人群画像选品、Amazon niche reverse…

    107 GitHub starsUsed in 1 repo~3k tokens
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  • Linkfox Amazon Product Detail

    linkfox-ai/linkfox-skills

    通过ASIN获取亚马逊商品详细信息,包括标题、图片、五点描述、规格参数、A+页面、价格、评分评论、变体等;可在取得原始HTML时尝试提取Item Highlights(商品亮点)。当用户提到亚马逊商品详情、ASIN查询、商品页面数据、Listing分析、五点描述提取、Item…

    107 GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • Linkfox Amazon Reviews List

    linkfox-ai/linkfox-skills

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    107 GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed

Categories

Questions about Linkfox Sellersprite Product Search

What does Linkfox Sellersprite Product Search do?

使用卖家精灵数据搜索和筛选亚马逊商品,支持价格、月销量、BSR排名、毛利率、评分、配送方式、标签、卖家来源等多维度条件,覆盖多个亚马逊站点。当用户提到亚马逊选品调研、产品筛选、销量过滤、产品发掘、BSR分析、小众商品发现、竞品分析、市场机会评估、按商品维度的市场规模估算、毛利率筛选、SellerSprite product selection, Amazon product…. Linkfox Sellersprite Product Search is an agent skill from linkfox-ai/linkfox-skills.

When should I use Linkfox Sellersprite Product Search?

Linkfox Sellersprite Product Search fits situations like: tasks that involve Market research.

How do I install Linkfox Sellersprite Product Search in Claude Code?

Run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-sellersprite-product-search -a claude-code`. Or copy the skill folder (skills/linkfox-sellersprite-product-search in linkfox-ai/linkfox-skills) into .claude/skills/linkfox-sellersprite-product-search in your project. Claude Code loads it when a task matches its description.

How do I install Linkfox Sellersprite Product Search in Codex?

Run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-sellersprite-product-search -a codex`. Or copy the skill folder (skills/linkfox-sellersprite-product-search in linkfox-ai/linkfox-skills) into .agents/skills/linkfox-sellersprite-product-search in your project. Codex loads it when a task matches its description.

Can I use Linkfox Sellersprite Product Search in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add linkfox-ai/linkfox-skills --skill linkfox-sellersprite-product-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/linkfox-sellersprite-product-search, .gemini/skills/linkfox-sellersprite-product-search, .github/skills/linkfox-sellersprite-product-search and .opencode/skills/linkfox-sellersprite-product-search in your project.

What does Linkfox Sellersprite Product Search need to run?

Going by SKILL.md and its folder, Linkfox Sellersprite Product Search needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named LINKFOX_AGENT_API_KEY and LINKFOXAGENT_API_KEY. Our summary lists: Python 3; A credential in LINKFOX_AGENT_API_KEY; A credential in LINKFOXAGENT_API_KEY.

Does Linkfox Sellersprite Product Search access the network?

SKILL.md names 1 domain. As links in the text: skill.linkfox.com. This is read from the text; nothing was executed.

Is Linkfox Sellersprite Product Search safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Linkfox Sellersprite Product Search use?

Linkfox Sellersprite Product Search is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Linkfox Sellersprite Product Search use?

About 3.6k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.

What are the alternatives to Linkfox Sellersprite Product Search?

Skills that share tags, products or a category with Linkfox Sellersprite Product Search: Customer Research (Nexus-JPF/note-companion, 870 stars), Creative Director (smixs/creative-director-skill, 247 stars), Audience Research (ScrapeCreators/social-media-research-skills, 3.4k stars) and Last 30 Days Trend Research (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Linkfox Sellersprite Product Search?

linkfox-ai (a GitHub user) maintains it in linkfox-ai/linkfox-skills, which has 107 GitHub stars. The repository holds 177 skills in this directory. The repository was last updated on September 14, 2026.

Source: linkfox-ai/linkfox-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.