linkfox-amazon-opportunity-screener

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Original

English

🇨🇳

Translation

Chinese

Amazon Opportunity Screener by Metrics

This skill guides you on how to reverse-search Amazon niches and keywords from a metrics pool aggregated from historical opportunity reports, helping sellers turn vague selection ideas (low competition, growing demand, blue ocean, pain-point opportunity, etc.) into concrete niche candidates.

本技能指导您如何从历史商机报告聚合的指标数据池中反向搜索亚马逊niche（细分赛道）与关键词，帮助卖家将模糊的选品思路（低竞争、需求增长、蓝海、痛点商机等）转化为具体的细分赛道候选对象。

Core Concepts

核心概念

This tool exposes a queryable pool of niche-level metrics (~37 fields per record) distilled from past Amazon opportunity reports. Instead of generating a fresh report (forward analysis), it lets you reverse-filter the existing pool by 30+ business dimensions and returns matching

(marketplace, keyword)

records ranked by collection time (most recent first).

Records are at the niche / keyword level, not ASIN level. Each record represents a niche snapshot — its market size, growth, competition, price tiers, demographics, top features, and review themes.

Forward vs. reverse: Use

linkfox-amazon-opportunity-report

when the user has a keyword and wants a comprehensive AI report. Use this skill when the user has business criteria (filters) and wants to discover which keywords / niches fit.

本工具提供一个可查询的niche级指标池（每条记录约37个字段），这些指标提炼自过往的亚马逊商机报告。与生成全新报告（正向分析）不同，它允许您通过30+个商业维度反向筛选现有数据池，并返回匹配的

(marketplace, keyword)

记录，按采集时间排序（最新优先）。

记录处于niche/关键词层级，而非ASIN层级。每条记录代表一个细分赛道快照——包含其市场规模、增长情况、竞争态势、价格档位、人群画像、核心功能及评论主题。

正向vs反向分析：当用户已有一个关键词并需要全面AI报告时，使用

linkfox-amazon-opportunity-report

；当用户有商业筛选条件并希望找到符合条件的关键词/细分赛道时，使用本技能。

Filter Dimensions

筛选维度

Filters are grouped into six business dimensions. All filter parameters are optional, but at least one of
keyword
/
nicheName
or any metric filter must be provided — fully empty calls are rejected.

Dimension	Example Parameters	Typical User Intent
Market size & growth	`nicheRevenue360dMinUsdAtLeastGte` , `nichePeakSearchVolumeAtLeastGte` , `nicheSearchVolumeYoyChangePctAtLeastGte` , `nichePeakMonthGte/Lte`	"Big enough market", "fast-growing", "Q4 seasonal"
Competition density	`nicheBrandCountLte` , `nicheBrandCountYoyChangePctAtLeastLte` , `nicheTop5ProductClickSharePctAtLeastLte` , `featureTop5BrandSharePctAtLeastLte`	"Newcomer-friendly", "brands fragmented", "no oligopoly", "brands exiting"
Price & tier	`priceMinUsdGte` , `priceMaxUsdLte` , `priceSweetSpotMinUsdGte/Lte` , `priceEntryClickSharePctAtLeastGte` , `priceMidClickSharePctAtLeastLte` , `priceHighClickSharePctAtLeastGte`	"Affordable focus", "premium-friendly", "mid-tier blue ocean"
Demographics	`demoPrimaryAgeMinGte` , `demoPrimaryAgeMaxLte` , `demoGenderDominant` , `demoPrimaryIncomeTier` , `demoLifeStageTagsContains`	"Female-driven", "high-income", "parents", "fitness enthusiasts"
Product features	`featureNewAvgReviewCountAtLeastLte` , `featureEstablishedAvgReviewCountAtLeastLte` , `featureEmergingTrendTagsContains` , `featureUncommonFeatureTagsContains` , `searchTopCategory1Label`	"New-product entry barrier low", "emerging trend", "uncommon feature edge", "set/kit niches"
Review insights	`reviewPositiveTop1Topic` , `reviewPositiveTop1PctAtLeastGte/Lte` , `reviewNegativeTop1Topic` , `reviewNegativeTop1PctAtLeastGte/Lte` , `reviewNegativeTop2Topic` , `reviewStrategicInsightTagsContains`	"Pain-point niche", "comfort-driven sellers", "size-issue opportunity"

See

references/api.md

for the full parameter list, types, value ranges, and response field map.

筛选条件分为六大商业维度。所有筛选参数为可选，但必须至少提供
keyword
/
nicheName
或任意一项指标筛选条件——完全空的请求会被拒绝。

维度	示例参数	典型用户需求
市场规模与增长	`nicheRevenue360dMinUsdAtLeastGte` , `nichePeakSearchVolumeAtLeastGte` , `nicheSearchVolumeYoyChangePctAtLeastGte` , `nichePeakMonthGte/Lte`	"足够大的市场"、"增长迅速"、"Q4季节性产品"
竞争密度	`nicheBrandCountLte` , `nicheBrandCountYoyChangePctAtLeastLte` , `nicheTop5ProductClickSharePctAtLeastLte` , `featureTop5BrandSharePctAtLeastLte`	"新手友好"、"品牌分散"、"无寡头垄断"、"品牌退出"
价格与档位	`priceMinUsdGte` , `priceMaxUsdLte` , `priceSweetSpotMinUsdGte/Lte` , `priceEntryClickSharePctAtLeastGte` , `priceMidClickSharePctAtLeastLte` , `priceHighClickSharePctAtLeastGte`	"主打平价"、"适合高端市场"、"中端蓝海"
人群画像	`demoPrimaryAgeMinGte` , `demoPrimaryAgeMaxLte` , `demoGenderDominant` , `demoPrimaryIncomeTier` , `demoLifeStageTagsContains`	"女性主导"、"高收入群体"、"父母群体"、"健身爱好者"
产品功能	`featureNewAvgReviewCountAtLeastLte` , `featureEstablishedAvgReviewCountAtLeastLte` , `featureEmergingTrendTagsContains` , `featureUncommonFeatureTagsContains` , `searchTopCategory1Label`	"新品准入门槛低"、"新兴趋势"、"特色功能优势"、"套装类细分赛道"
评论洞察	`reviewPositiveTop1Topic` , `reviewPositiveTop1PctAtLeastGte/Lte` , `reviewNegativeTop1Topic` , `reviewNegativeTop1PctAtLeastGte/Lte` , `reviewNegativeTop2Topic` , `reviewStrategicInsightTagsContains`	"痛点型细分赛道"、"主打舒适感卖家"、"尺寸问题商机"

完整的参数列表、类型、取值范围及响应字段映射请查看

references/api.md

。

Supported Marketplaces

支持的站点

Currently only US (United States) is supported. Always set

amazonDomain

US

(or omit). If a user requests other marketplaces, inform them this tool currently only covers the US market.

目前仅支持**美国（US）**站点。请始终将

amazonDomain

设置为

US

（或省略该参数）。若用户请求其他站点，请告知其本工具目前仅覆盖美国市场。

API Usage

API 使用

This tool calls the LinkFox tool gateway API. See

references/api.md

for calling conventions, request parameters, and response structure. You can also execute

scripts/amazon_opportunity_screener.py

directly to run queries.

本工具调用LinkFox工具网关API。调用规范、请求参数及响应结构请查看

references/api.md

。您也可以直接执行

scripts/amazon_opportunity_screener.py

来运行查询。

How to Build Queries

如何构建查询

The user expresses business intent in natural language; you map it to the smallest viable set of filters. Principles:

Convert intent into specific bounds: "low competition" →
```
nicheBrandCountLte: 20
```
; "fast-growing" →
```
nicheSearchVolumeYoyChangePctAtLeastGte: 100
```
(≥100% YoY); "newcomer-friendly" →
```
featureNewAvgReviewCountAtLeastLte: 500
```
.
Start narrow, then loosen: First call usually with 2–4 strong filters and
```
limit=25
```
. If the result set is empty or too small, drop or widen the most aggressive filter rather than adding new ones.
Pair complementary signals: Brand-level + product-level concentration (
```
featureTop5BrandSharePctAtLeastLte
```
+
```
nicheTop5ProductClickSharePctAtLeastGte
```
) reveals "brands fragmented but products concentrated" — a brand-extension entry signal.
Snake_case fragments for tag fields:
```
featureEmergingTrendTagsContains
```
,
```
demoLifeStageTagsContains
```
,
```
reviewNegativeTop1Topic
```
, etc. accept snake_case word fragments and use LIKE matching. Pass a root word (
```
size
```
,
```
parent
```
,
```
cordless
```
) to cover normalized variants.
Faithful to user intent: Don't silently add filters the user didn't ask for. If they only said "growing", just filter on growth — don't also constrain price unless they mentioned it.

用户用自然语言表达商业需求，您需要将其映射为最小可行的筛选条件集合。原则如下：

将需求转化为具体阈值：“低竞争”→
```
nicheBrandCountLte: 20
```
；“增长迅速”→
```
nicheSearchVolumeYoyChangePctAtLeastGte: 100
```
（同比≥100%）；“新手友好”→
```
featureNewAvgReviewCountAtLeastLte: 500
```
。
先窄后宽：首次调用通常设置2-4个严格筛选条件，
```
limit=25
```
。若结果集为空或过少，应放宽最严格的筛选条件，而非添加新条件。
搭配互补信号：品牌层级+产品层级集中度筛选（
```
featureTop5BrandSharePctAtLeastLte
```
+
```
nicheTop5ProductClickSharePctAtLeastGte
```
）可发现“品牌分散但产品集中”的情况——这是品牌拓展的切入信号。
标签字段使用蛇形命名片段：
```
featureEmergingTrendTagsContains
```
、
```
demoLifeStageTagsContains
```
、
```
reviewNegativeTop1Topic
```
等参数接受蛇形命名的单词片段，并使用模糊匹配。传入词根（如
```
size
```
、
```
parent
```
、
```
cordless
```
）即可覆盖标准化变体。
忠实用户需求：不要擅自添加用户未提及的筛选条件。若用户仅说“增长型”，仅筛选增长相关条件即可——除非用户提到价格，否则不要限制价格。

Common Scenarios

常见场景

1. Niche reverse-lookup by keyword

json

{"keyword": "whoop band", "limit": 25}

2. Newcomer-friendly low-competition niches

json

{"nicheBrandCountLte": 20, "featureNewAvgReviewCountAtLeastLte": 500, "limit": 25}

3. High-growth blue ocean (≥100% YoY, brands not yet flooding in)

json

{"nicheSearchVolumeYoyChangePctAtLeastGte": 100, "nicheBrandCountYoyChangePctAtLeastLte": 30, "limit": 25}

4. Mid-tier price gap (low-price dominates, mid-tier scarce)

json

{"priceEntryClickSharePctAtLeastGte": 70, "priceMidClickSharePctAtLeastLte": 5, "limit": 25}

5. Pain-point entry — strong size complaints

json

{"reviewNegativeTop1Topic": "size", "reviewNegativeTop1PctAtLeastGte": 70, "limit": 25}

6. Premium-friendly female-driven niches

json

{"demoGenderDominant": "female", "demoPrimaryIncomeTier": "high", "priceHighClickSharePctAtLeastGte": 25, "limit": 25}

7. Q4 seasonal niches with ≥100k peak search

json

{"nichePeakMonthGte": 11, "nichePeakMonthLte": 12, "nichePeakSearchVolumeAtLeastGte": 100000, "limit": 25}

8. Track niches around a known competitor brand

json

{"featureTopBrandsContains": "WHOOP", "limit": 50}

1. 通过关键词反向查找细分赛道

json

{"keyword": "whoop band", "limit": 25}

2. 新手友好的低竞争细分赛道

json

{"nicheBrandCountLte": 20, "featureNewAvgReviewCountAtLeastLte": 500, "limit": 25}

3. 高增长蓝海（同比≥100%，品牌尚未大量涌入）

json

{"nicheSearchVolumeYoyChangePctAtLeastGte": 100, "nicheBrandCountYoyChangePctAtLeastLte": 30, "limit": 25}

4. 中端价格缺口（低价主导，中端稀缺）

json

{"priceEntryClickSharePctAtLeastGte": 70, "priceMidClickSharePctAtLeastLte": 5, "limit": 25}

5. 痛点切入——尺寸投诉强烈

json

{"reviewNegativeTop1Topic": "size", "reviewNegativeTop1PctAtLeastGte": 70, "limit": 25}

6. 适合高端市场的女性主导细分赛道

json

{"demoGenderDominant": "female", "demoPrimaryIncomeTier": "high", "priceHighClickSharePctAtLeastGte": 25, "limit": 25}

7. Q4季节性细分赛道，峰值搜索量≥10万

json

{"nichePeakMonthGte": 11, "nichePeakMonthLte": 12, "nichePeakSearchVolumeAtLeastGte": 100000, "limit": 25}

8. 追踪竞品品牌相关的细分赛道

json

{"featureTopBrandsContains": "WHOOP", "limit": 50}

Display Rules

展示规则

Present data only: Render the returned niches as a clean comparison table — niche name / keyword, market size, growth, brand count, price range, key tags. No subjective business advice.
Surface the active filters: Echo the filter set you used so the user can adjust ("当前筛选：品牌数 ≤ 20 且搜索量同比 ≥ 100%").
Time-snapshot reminder: Records reflect data at collection time and are not continuously updated. Mention this when results look stale or contradict a user's external knowledge.
Empty / few-result handling: If
```
data
```
is empty or very short, suggest widening the most aggressive filter rather than re-asking the user from scratch.
Error handling: When a query fails, explain the reason based on the
```
msg
```
field (most often the "fully empty parameters" guard) and suggest adding at least one filter.
No secondary aggregation: The results power frontend rendering and are not stored, so they cannot be fed into
```
@智能数据查询
```
(intelligent data query) for further aggregation. If users ask for grouped statistics across niches, do the calculation locally or pull a wider
```
limit
```
first.

仅展示数据：将返回的细分赛道整理为清晰的对比表格——包含细分赛道名称/关键词、市场规模、增长情况、品牌数量、价格区间、核心标签。不提供主观商业建议。
显示当前筛选条件：回显您使用的筛选条件，方便用户调整（例如：“当前筛选：品牌数 ≤ 20 且搜索量同比 ≥ 100%”）。
提醒数据快照属性：记录反映的是采集时的数据，并非实时更新。当结果看起来过时或与用户外部认知矛盾时，需提及这一点。
空结果/少结果处理：若
```
data
```
为空或结果极少，建议放宽最严格的筛选条件，而非从头询问用户。
错误处理：当查询失败时，根据
```
msg
```
字段解释原因（最常见的是“参数完全为空”的拦截），并建议至少添加一个筛选条件。
不支持二次聚合：结果用于前端展示，不存储，因此无法传入
```
@智能数据查询
```
进行进一步聚合。若用户要求对细分赛道进行分组统计，请本地计算或先扩大
```
limit
```
取值。

Important Limitations

重要限制

US only: Currently only supports the United States marketplace (
```
amazonDomain
```
=
```
US
```
).
No pagination: There is no
```
page
```
parameter. Increase
```
limit
```
(max 200) to widen the candidate pool; results are sorted by collection time (newest first).
At least one filter required: Calls with no
```
keyword
```
/
```
nicheName
```
and no metric filter are rejected.
Snapshot data: Records are aggregated from historical opportunity reports; new reports refresh the pool over time, but individual records are not real-time.
Niche-level granularity: The output is niche / keyword level, not ASIN level. To dig into specific products inside a niche, hand off to
```
linkfox-amazon-search
```
,
```
linkfox-keepa-product-search
```
, etc.

仅支持美国站点：目前仅支持美国市场（
```
amazonDomain
```
=
```
US
```
）。
无分页功能：没有
```
page
```
参数。可增大
```
limit
```
（最大200）来扩大候选池；结果按采集时间排序（最新优先）。
至少需要一个筛选条件：未提供
```
keyword
```
/
```
nicheName
```
且无指标筛选条件的请求会被拒绝。
快照数据：记录聚合自历史商机报告；新报告会定期更新数据池，但单条记录并非实时数据。
细分赛道层级粒度：输出为niche/关键词层级，而非ASIN层级。若要深入了解细分赛道内的具体产品，请转用
```
linkfox-amazon-search
```
、
```
linkfox-keepa-product-search
```
等工具。

User Expression & Scenario Quick Reference

用户表达与场景速查

Applicable — Niche-level reverse selection on the US Amazon market:

User Says	Scenario
"Low-competition niches", "newcomer-friendly", "brand-light"	Brand-density filter
"Brands are exiting", "old players retreating"	Negative brand-count YoY
"Fast-growing niche", "trending up", "≥100% YoY"	Search-volume YoY filter
"Mid-tier blue ocean", "low-price dominates but mid is scarce"	Price-tier share gap
"Premium-friendly", "high-income consumers"	Income tier + high-tier share
"Female / male / mixed market"	Gender dominance filter
"Parents / students / retirees / fitness enthusiasts"	Life-stage tag
"Strong size / quality / durability pain point"	Negative review topic + share
"Comfort-driven", "value-driven sellers"	Positive review topic + share
"Track all niches around brand X"	`featureTopBrandsContains`
"Q4 seasonal niches", "Prime Day window"	Peak month + peak volume

Not applicable — Use other tools instead:

Need a comprehensive AI report on one keyword →
```
linkfox-amazon-opportunity-report
```
ASIN-level competitor research, sales estimation → SellerSprite / Keepa / Sorftime tools
Real-time keyword ranking, search-term mining → ABA / SIF tools
Marketplaces other than US → not yet supported by this tool
Want to run group-by aggregation over niches via
```
@智能数据查询
```
→ unsupported (data is not warehoused)

Boundary judgment: When users describe selection criteria in business language and want matching candidate niches, this skill applies. When they hand you a specific keyword and want the full multi-dimensional analysis, use

linkfox-amazon-opportunity-report

. When they want to drill into ASINs / sellers within a niche, hand off to product-search tools.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

The functionality or purpose described in this skill does not match actual behavior
The skill's results do not match the user's intent
The user expresses dissatisfaction or praise about this skill
Anything you believe could be improved

Call the feedback API as specified in

references/api.md

. Do not interrupt the user's flow.

适用场景——美国亚马逊市场的细分赛道反向选品：

用户表述	对应场景
“低竞争细分赛道”、“新手友好”、“品牌较少”	品牌密度筛选
“品牌退出”、“老玩家退场”	品牌数量同比负增长
“高增长细分赛道”、“趋势上升”、“同比≥100%”	搜索量同比筛选
“中端蓝海”、“低价主导但中端稀缺”	价格档位份额缺口
“适合高端市场”、“高收入消费者”	收入层级+高端档位份额
“女性/男性/混合市场”	性别主导筛选
“父母/学生/退休人员/健身爱好者”	人生阶段标签
“强烈的尺寸/质量/耐用性痛点”	负面评论主题+占比
“主打舒适感”、“性价比导向卖家”	正面评论主题+占比
“追踪品牌X相关的所有细分赛道”	`featureTopBrandsContains`
“Q4季节性细分赛道”、“Prime Day窗口期”	峰值月份+峰值搜索量

不适用场景——请使用其他工具：

需要针对单个关键词的全面AI报告 →
```
linkfox-amazon-opportunity-report
```
ASIN层级竞品调研、销量估算 → SellerSprite/Keepa/Sorftime工具
实时关键词排名、搜索词挖掘 → ABA/SIF工具
美国以外的站点 → 本工具暂不支持
想要通过
```
@智能数据查询
```
对细分赛道进行分组聚合 → 不支持（数据未入库）

边界判断：当用户用商业语言描述选品条件并希望找到匹配的候选细分赛道时，适用本技能；当用户提供具体关键词并需要全维度分析时，使用

linkfox-amazon-opportunity-report

；当用户想要深入细分赛道内的ASIN/卖家时，转用产品搜索工具。

反馈：

当出现以下任一情况时，自动通过Feedback API检测并提交反馈：

本技能描述的功能或用途与实际行为不符
技能结果与用户需求不匹配
用户表达对本技能的不满或赞赏
任何您认为可以改进的地方

请按照

references/api.md

中的说明调用反馈API，不要打断用户的流程。

Handling Large Responses

大响应处理

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/amazon_opportunity_screener.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

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

为避免超出Agent上下文限制，可将响应持久化到磁盘并仅提取所需字段：

python scripts/response_io.py run --script scripts/amazon_opportunity_screener.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # 或 --path "<JMESPath>"

请将
--out-dir
设置在Git工作区之外（例如Unix系统的
/tmp/...
，Windows系统的
%TEMP%/...
）。持久化的响应可能包含个人身份信息（PII）、定价或敏感授权数据——请勿提交到Git。文件不会自动删除，任务完成后请清理。

run

命令将完整响应写入文件，并仅输出 schema 预览+文件路径。

read

命令提取指定字段，支持

--limit/--offset

进行切片，以及

--format json|jsonl|csv|table

指定输出格式。

何时优先使用此模式——根据响应特征判断，例如：

每条记录字段数量多，或包含不需要的字段
批量/分页结果（单次调用返回多个条目）
长文本字段（描述、评论、HTML、时间序列）
输出需在后续步骤复用，而非立即使用

对于小型、一次性响应，直接调用主脚本即可。

⚠️ 预览内容是截断的schema+样本，而非完整数据。任何字段级别的决策必须通过

read

命令从持久化文件读取。

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