Amazon Keyword Research 🔍
Free keyword research for Amazon sellers. No API key — works out of the box.
Installation
CODEBLOCK0
Capabilities
- - 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
Usage Examples
Users can ask naturally. Examples:
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Workflow
Step 1: Gather Autocomplete Data
Run the bundled script to collect Amazon autocomplete suggestions:
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Parameters:
- -
keyword (required): The seed keyword to research - INLINECODE1 (optional):
us (default), uk, de, fr, it, es, jp, ca, au, in, mx, INLINECODE13
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:
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For multi-marketplace research, run the script once per marketplace.
Step 2: Analyze Competition
Use web_search to gather competitor intelligence:
- 1. 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.
Step 3: Check Seasonality
Use web_fetch on Google Trends:
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If Google Trends returns a 429 error, fall back to web_search for seasonal data:
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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.
Step 4: Synthesize Report
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.
Output Format
Present the final report in this structure:
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Multi-Keyword Comparison
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:
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How to execute: Run the script 3 times:
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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.
Limitations
This skill uses publicly available data (Amazon autocomplete + web search). It does not provide exact monthly search volumes or sales estimates. For precise data, stay tuned for Nexscope — coming soon.
Part of the Nexscope suite — AI-powered Amazon seller tools.
Amazon 关键词研究 🔍
为亚马逊卖家提供的免费关键词研究工具。无需API密钥——开箱即用。
安装
bash
npx skills add nexscope-ai/Amazon-Skills --skill amazon-keyword-research -g
功能
- - 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个月谷歌趋势数据,识别旺季和需求变化
- 市场机会评分:1-10分,综合竞争密度、价格空间和需求信号
- 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
使用示例
用户可自然提问。示例:
研究亚马逊美国站关键词便携式搅拌机
查找亚马逊上瑜伽垫的长尾关键词
我想卖阻力带。亚马逊关键词格局如何?
比较亚马逊美国站笔记本电脑支架和显示器支架——哪个机会更大?
分析亚马逊德国站厨房刀
研究亚马逊美国站、英国站和德国站的水瓶
工作流程
第一步:收集自动补全数据
运行捆绑脚本收集亚马逊自动补全建议:
bash
/scripts/research.sh <关键词> [站点]
参数:
- - 关键词(必填):要研究的种子关键词
- 站点(可选):us(默认)、uk、de、fr、it、es、jp、ca、au、in、mx、br
脚本功能:
- - 使用种子关键词查询亚马逊自动补全API
- 通过前缀扩展:最佳[关键词]便宜[关键词]顶级[关键词]
- 通过a-z后缀扩展:[关键词] a[关键词] b……[关键词] z
- 返回去重、排序后的真实搜索建议列表——每行一个
为何重要: 亚马逊自动补全反映真实购物者的实际输入。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获基本自动补全遗漏的长尾词,这些词通常竞争更低、意图更强。
示例:
bash
/scripts/research.sh 便携式搅拌机 us
返回100-200个长尾关键词
多站点研究时,每个站点运行一次脚本。
第二步:分析竞争
使用web_search收集竞品情报:
- 1. 搜索<关键词> site:amazon.com——记录近似结果数量以评估竞争密度
- 搜索<关键词> amazon best sellers price review——提取价格模式、评分均值、主导品牌
- 总结:总竞品数、价格区间(最低/平均/最高)、平均星级评分、按可见度排名前5品牌
为何重要: 没有竞争背景的原始关键词量毫无意义。一个搜索量1万但被3个拥有1万+评论的根深蒂固品牌主导的关键词,与相同搜索量但卖家分散的关键词,机会截然不同。价格区间揭示利润潜力——如果所有产品都在10美元以下,扣除FBA费用后利润将极其微薄。
第三步:检查季节性
使用web_fetch访问谷歌趋势:
https://trends.google.com/trends/explore?q=<关键词>&geo=US
如果谷歌趋势返回429错误,回退到web_search获取季节性数据:
<关键词> 季节性趋势 需求 旺季 月份
识别:趋势方向(上升/下降/稳定)、季节性高峰(哪些月份)、同比变化。
为何重要: 季节性决定现金流风险。一个产品80%的销量在第四季度,意味着你需要提前数月备货资金,并在全年其余时间面临滞销库存。上升趋势意味着需求增长和新进入者空间更大;下降趋势意味着你在争夺缩小的蛋糕。这些背景将关键词从数字转化为商业决策。
第四步:综合报告
将所有数据整合为以下输出格式。
为何结构重要: 按意图(商业型vs信息型vs细分型)对关键词分组,帮助卖家理解人们搜索什么以及为什么搜索。机会评分将多个信号浓缩为一个可操作数字,但其背后的分解才是真正指导决策的关键——因此始终展示推理过程。
输出格式
按以下结构呈现最终报告:
关键词研究报告:[关键词]
站点: 亚马逊[美国/英国/德国/...]
日期: [当前日期]
1. 长尾关键词(发现[数量]个)
高商业意图:
信息型/研究型:
细分/特定型:
2. 竞争格局
$[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. 季节性趋势
[描述12个月趋势:高峰、低谷、稳定期]
[标注与该关键词相关的即将到来的旺季]
4. 市场机会评分:[X/10]
评分分解:
- - 竞争密度:[低/中/高] — [原因]
- 价格空间:[低/中/高] — [原因]
- 需求趋势:[增长/稳定/下降] — [原因]
- 细分潜力:[低/中/高] — [原因]
建议: [1-2句可操作建议]
多关键词对比
当用户要求比较两个或多个关键词时,对每个关键词分别运行完整工作流程(步骤1-4),然后以并排对比表格呈现结果。
示例用户输入:
比较亚马逊美国站的笔记本电脑支架显示器支架和平板支架——我应该卖哪个?
执行方式: 运行脚本3次:
bash
/scripts/research.sh 笔记本电脑支架 us
/scripts/research.sh 显示器支架 us
/scripts/research.sh 平板支架 us
然后对每个关键词完成步骤2-3,并输出对比表格:
| 指标 | 笔记本电脑支架 | 显示器支架 | 平板支架 |
|---|
| 长尾词数量 | — | — | — |
| 平均价格 |
— | — | — |
| 头部品牌主导度 | — | — | — |
| 趋势方向 | — | — | — |
| 机会评分 | — | — | — |
最后给出建议,说明哪个关键词机会最佳及原因。
局限性
本技能使用公开可用数据(亚马逊自动补全+网络搜索)。不提供精确的月度搜索量或销售预估。如需精确数据,敬请关注即将推出的Nexscope。
属于Nexscope套件——AI驱动的亚马逊卖家工具。