amazon-keyword-research

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Amazon Keyword Research 🔍

Amazon关键词研究 🔍

Free keyword research for Amazon sellers. No API key — works out of the box.

面向亚马逊卖家的免费关键词研究工具。无需API密钥，开箱即用。

Installation

安装

bash

npx skills add nexscope-ai/Amazon-Skills --skill amazon-keyword-research -g

bash

npx skills add nexscope-ai/Amazon-Skills --skill amazon-keyword-research -g

Capabilities

功能特性

Long-tail keyword mining: Extract 100-200 real search terms from Amazon's autocomplete engine
Competitor landscape analysis: Product count, price range, average rating, review distribution, top brands
Seasonal trend detection: 12-month Google Trends data to identify peak seasons and demand shifts
Market opportunity scoring: 1-10 score combining competition density, price room, and demand signals
Multi-marketplace support: US, UK, DE, FR, IT, ES, JP, CA, AU, IN, MX, BR
Keyword comparison: Side-by-side analysis of multiple keywords

长尾关键词挖掘：从亚马逊自动补全引擎提取100-200个真实搜索词
竞品格局分析：产品数量、价格区间、平均评分、评论分布、头部品牌
季节性趋势检测：12个月Google Trends数据，识别销售旺季与需求变化
市场机会评分：结合竞争密度、价格空间与需求信号的1-10分评分体系
多站点支持：美国、英国、德国、法国、意大利、西班牙、日本、加拿大、澳大利亚、印度、墨西哥、巴西
关键词对比：多个关键词的并排分析

Usage Examples

使用示例

Users can ask naturally. Examples:

Research the keyword "portable blender" on Amazon US

Find long-tail keywords for "yoga mat" on Amazon

I want to sell resistance bands. What does the Amazon keyword landscape look like?

Compare "laptop stand" vs "monitor stand" on Amazon US — which has more opportunity?

Analyze "Küchenmesser" on Amazon Germany

Research "water bottle" across Amazon US, UK, and DE

用户可自然提问。示例：

Research the keyword "portable blender" on Amazon US

Find long-tail keywords for "yoga mat" on Amazon

I want to sell resistance bands. What does the Amazon keyword landscape look like?

Compare "laptop stand" vs "monitor stand" on Amazon US — which has more opportunity?

Analyze "Küchenmesser" on Amazon Germany

Research "water bottle" across Amazon US, UK, and DE

Workflow

工作流程

Step 1: Gather Autocomplete Data

步骤1：收集自动补全数据

Run the bundled script to collect Amazon autocomplete suggestions:

bash

<skill>/scripts/research.sh "<keyword>" [marketplace]

Parameters:

```
keyword
```
(required): The seed keyword to research
```
marketplace
```
(optional):
```
us
```
(default),
```
uk
```
,
```
de
```
,
```
fr
```
,
```
it
```
,
```
es
```
,
```
jp
```
,
```
ca
```
,
```
au
```
,
```
in
```
,
```
mx
```
,
```
br
```

What the script does:

Queries Amazon's autocomplete API with the seed keyword
Expands with prefixes: "best [keyword]", "cheap [keyword]", "top [keyword]"
Expands with a-z suffixes: "[keyword] a", "[keyword] b", ... "[keyword] z"
Returns deduplicated, sorted list of real search suggestions — one per line

Why this matters: Amazon autocomplete reflects what real shoppers are actually typing. These aren't guesses — they're demand signals directly from Amazon's search engine. The prefix and alphabet expansion catches long-tail terms that basic autocomplete misses, which are often lower competition and higher intent.

Example:

bash

<skill>/scripts/research.sh "portable blender" us

运行内置脚本收集亚马逊自动补全建议：

bash

<skill>/scripts/research.sh "<keyword>" [marketplace]

参数:

```
keyword
```
(必填): 用于调研的种子关键词
```
marketplace
```
(可选):
```
us
```
(默认),
```
uk
```
,
```
de
```
,
```
fr
```
,
```
it
```
,
```
es
```
,
```
jp
```
,
```
ca
```
,
```
au
```
,
```
in
```
,
```
mx
```
,
```
br
```

脚本功能:

用种子关键词查询亚马逊自动补全API
扩展前缀: "best [keyword]", "cheap [keyword]", "top [keyword]"
扩展a-z后缀: "[keyword] a", "[keyword] b", ... "[keyword] z"
返回去重、排序后的真实搜索建议列表 — 每行一个

重要性: 亚马逊自动补全反映了真实买家的实际搜索内容。这些不是猜测 — 而是直接来自亚马逊搜索引擎的需求信号。前缀和字母扩展能捕捉到基础自动补全遗漏的长尾词，这些词通常竞争更低、购买意向更高。

示例:

bash

<skill>/scripts/research.sh "portable blender" us

Returns 100-200 long-tail keywords


For multi-marketplace research, run the script once per marketplace.


如需多站点调研，为每个站点分别运行脚本。

Step 2: Analyze Competition

步骤2：分析竞品情况

Use

web_search

to gather competitor intelligence:

Search
```
"<keyword>" site:amazon.com
```
— note approximate result count for competition density
Search
```
"<keyword>" amazon best sellers price review
```
— extract price patterns, rating averages, dominant brands
Summarize: total competitors, price range (min/avg/max), average star rating, top 5 brands by visibility

Why this matters: Raw keyword volume means nothing without competition context. A keyword with 10,000 searches but dominated by 3 entrenched brands with 10,000+ reviews each is a very different opportunity than one with the same volume but fragmented sellers. The price range reveals margin potential — if everything is under $10, margins will be razor-thin after FBA fees.

使用

web_search

收集竞品情报:

搜索
```
"<keyword>" site:amazon.com
```
— 记录近似结果数量以了解竞争密度
搜索
```
"<keyword>" amazon best sellers price review
```
— 提取价格模式、平均评分、主导品牌
总结: 竞品总数、价格区间(最低/平均/最高)、平均星级、曝光量Top5品牌

重要性: 单纯的关键词搜索量没有竞争背景毫无意义。一个有10000次搜索但被3个拥有10000+评论的老牌品牌垄断的关键词，与搜索量相同但卖家分散的关键词相比，机会天差地别。价格区间揭示了利润潜力 — 如果所有产品都在10美元以下，扣除FBA费用后利润会极其微薄。

Step 3: Check Seasonality

步骤3：查看季节性趋势

Use

web_fetch

on Google Trends:

https://trends.google.com/trends/explore?q=<keyword>&geo=US

If Google Trends returns a 429 error, fall back to

web_search

for seasonal data:

"<keyword>" seasonal trends demand peak months

Identify: trend direction (rising/declining/stable), seasonal peaks (which months), year-over-year change.

Why this matters: Seasonality determines cash flow risk. A product that sells 80% of its volume in Q4 means you need capital for inventory months in advance and may sit on dead stock the rest of the year. Rising trends mean growing demand and more room for new entrants; declining trends mean you're fighting over a shrinking pie. This context turns a keyword from a number into a business decision.

在Google Trends上使用

web_fetch

https://trends.google.com/trends/explore?q=<keyword>&geo=US

如果Google Trends返回429错误，改用

web_search

获取季节性数据:

"<keyword>" seasonal trends demand peak months

识别: 趋势方向(上升/下降/稳定)、季节性峰值(哪些月份)、同比变化。

重要性: 季节性决定了现金流风险。一款产品80%的销量集中在第四季度，意味着你需要提前数月筹备库存资金，其余时间可能会积压滞销品。上升趋势意味着需求增长，新进入者有更多空间；下降趋势意味着你要在不断缩小的市场中竞争。这些背景信息将关键词从一个数字转化为商业决策依据。

Step 4: Synthesize Report

步骤4：生成综合报告

Combine all data into the output format below.

Why structure matters: Grouping keywords by intent (commercial vs informational vs niche) helps the seller understand not just what people search, but why they search it. The opportunity score condenses multiple signals into a single actionable number, but the breakdown behind it is what actually informs the decision — so always show the reasoning.

将所有数据整合成以下输出格式。

结构的重要性: 按意向(商业型/信息型/细分型)分组关键词，有助于卖家不仅了解人们搜索什么，还能了解他们搜索的原因。机会评分将多个信号浓缩为一个可操作的数字，但背后的细分数据才是决策的真正依据 — 因此务必展示评分理由。

Output Format

输出格式

Present the final report in this structure:

undefined

最终报告按以下结构呈现:

undefined

Keyword Research Report: [keyword]

关键词研究报告: [关键词]

Marketplace: Amazon [US/UK/DE/...] Date: [current date]

站点: 亚马逊[美国/英国/德国/...] 日期: [当前日期]

1. Long-tail Keywords ([count] found)

1. 长尾关键词(共找到[数量]个)

High Commercial Intent:

[keyword with "buy", "best", "vs", "for" etc.]
...

Informational / Research:

[keyword with "how to", "what is", "review" etc.]
...

Niche / Specific:

[long, specific keywords indicating clear purchase intent]
...

高商业意向:

包含"buy", "best", "vs", "for"等词汇的关键词
...

信息调研型:

包含"how to", "what is", "review"等词汇的关键词
...

细分特定型:

长且具体、明确显示购买意向的关键词
...

2. Competition Landscape

2. 竞品格局

Metric	Value
Estimated competitors	[number]
Price range	$[min] - $[max]
Average price	$[avg]
Average rating	[stars]
Top brands	[brand1, brand2, brand3...]

指标	数值
预估竞品数量	[数字]
价格区间	$[最低]-$[最高]
平均价格	$[平均]
平均评分	[星级]
头部品牌	[品牌1, 品牌2, 品牌3...]

3. Seasonal Trends

3. 季节性趋势

[Describe 12-month trend: peaks, valleys, stable periods] [Note any upcoming peak seasons relevant to the keyword]

[描述12个月趋势: 峰值、低谷、稳定期] [标注与该关键词相关的即将到来的销售旺季]

4. Market Opportunity Score: [X/10]

4. 市场机会评分: [X/10]

Score breakdown:

Competition density: [low/medium/high] — [why]
Price room: [low/medium/high] — [why]
Demand trend: [growing/stable/declining] — [why]
Niche potential: [low/medium/high] — [why]

Recommendation: [1-2 sentence actionable recommendation]

undefined

评分细分:

竞争密度: [低/中/高] — [理由]
价格空间: [低/中/高] — [理由]
需求趋势: [增长/稳定/下降] — [理由]
细分潜力: [低/中/高] — [理由]

建议: [1-2句可执行建议]

undefined

Multi-Keyword Comparison

多关键词对比

When the user asks to compare two or more keywords, run the full workflow (Steps 1-4) for each keyword separately, then present results in a side-by-side comparison table.

Example user input:

Compare "laptop stand" vs "monitor stand" vs "tablet stand" on Amazon US — which one should I sell?

How to execute: Run the script 3 times:

bash

<skill>/scripts/research.sh "laptop stand" us
<skill>/scripts/research.sh "monitor stand" us
<skill>/scripts/research.sh "tablet stand" us

Then complete Steps 2-3 for each keyword, and output a comparison table:

Metric	laptop stand	monitor stand	tablet stand
Long-tail count	—	—	—
Avg price	—	—	—
Top brand dominance	—	—	—
Trend direction	—	—	—
Opportunity score	—	—	—

End with a Recommendation stating which keyword has the best opportunity and why.

当用户要求对比两个或多个关键词时，为每个关键词分别执行完整工作流程(步骤1-4)，然后以并排对比表格呈现结果。

示例用户输入:

Compare "laptop stand" vs "monitor stand" vs "tablet stand" on Amazon US — which one should I sell?

执行方式: 运行3次脚本:

bash

<skill>/scripts/research.sh "laptop stand" us
<skill>/scripts/research.sh "monitor stand" us
<skill>/scripts/research.sh "tablet stand" us

然后为每个关键词完成步骤2-3，输出对比表格:

指标	laptop stand	monitor stand	tablet stand
长尾词数量	—	—	—
平均价格	—	—	—
头部品牌垄断程度	—	—	—
趋势方向	—	—	—
机会评分	—	—	—

最后附上建议，说明哪个关键词机会最佳及原因。

Limitations

局限性

This skill uses publicly available data (Amazon autocomplete + web search). It does not provide exact monthly search volumes or sales estimates. For precise data, check out Nexscope — Your AI Assistant for smarter E-commerce decisions.

Built by Nexscope — research, validate, and act on e-commerce opportunities with AI.

本技能使用公开可用数据(亚马逊自动补全+网页搜索)。不提供精确的月度搜索量或销量预估。如需精准数据，请查看**Nexscope**——助力电商决策更明智的AI助手。

由Nexscope开发——借助AI调研、验证并把握电商机会。