Amazon Keyword Research

作者 nexscope-ai0f3b13fa0e5e無授權條款收錄於 2026年10月8日更新於 2026年10月8日

Amazon keyword research and market opportunity analysis for sellers. Retrieve autocomplete suggestions (long-tail keywords), analyze competitor landscape, and assess market opportunity for any keyword on 12 Amazon marketplaces (US/UK/DE/FR/IT/ES/JP/CA/AU/IN/MX/BR). No API key required. Make sure to use this skill whenever the user mentions Amazon product research, finding products to sell on Amazon, Amazon keyword ideas, niche analysis, competition analysis for Amazon, market opportunity on Amazon, comparing Amazon keywords, evaluating whether a product is worth selling, Amazon autocomplete data, seasonal demand for Amazon products, or anything related to researching what to sell on Amazon — even if they don't explicitly say 'keyword research'. Also trigger when the user asks vague questions like 'is this a good product to sell?', 'what's the competition like for X on Amazon?', 'should I sell X or Y?', or 'what are people searching for on Amazon?'.

AI 產生的概覽

透過自動完成資料、競品調查與趨勢分析,研究 12 個 Amazon 站點的關鍵字與市場機會。

功能
針對種子關鍵字抓取 Amazon 自動完成建議,並以字首與 a-z 字尾擴展,輸出經去重排序的長尾搜尋詞清單。接著透過網路搜尋蒐集競品情報、透過 Google Trends 取得季節性資料,彙整成包含關鍵字分組、競爭情況表格、季節趨勢與 1-10 分市場機會評分的報告。也支援多個關鍵字與多個站點的並列比較。
適用情境
適合賣家需要 Amazon 關鍵字靈感、利基或競爭分析、季節性需求洞察,或判斷某項產品是否值得銷售時使用。也適用於了解 Amazon 買家搜尋內容,以及比較多個候選關鍵字的情境。
執行需求
需要執行隨附的 shell 指令碼(scripts/research.sh),該指令碼會呼叫 Amazon 自動完成端點,因此需要網路連線;不需要 API 金鑰。流程也依賴代理的 web_search 與 web_fetch 工具(含 Google Trends),可能遇到流量限制錯誤。

Amazon Keyword Research 🔍

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

Installation

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

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:

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

Step 1: Gather Autocomplete Data

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# Returns 100-200 long-tail keywords

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
  2. Search "<keyword>" amazon best sellers price review — extract price patterns, rating averages, dominant brands
  3. 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:

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.

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:

## Keyword Research Report: [keyword]**Marketplace:** Amazon [US/UK/DE/...]**Date:** [current date]
### 1. Long-tail Keywords ([count] found)
**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]- ...
### 2. Competition Landscape
| Metric | Value ||--------|-------|| Estimated competitors | [number] || Price range | $[min] - $[max] || Average price | $[avg] || Average rating | [stars] || Top brands | [brand1, brand2, brand3...] |
### 3. Seasonal Trends
[Describe 12-month trend: peaks, valleys, stable periods][Note any upcoming peak seasons relevant to the keyword]
### 4. Market Opportunity Score: [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]

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:

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:

Metriclaptop standmonitor standtablet 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, check out Nexscope — Your AI Assistant for smarter E-commerce decisions.


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

來源與署名

來源:nexscope-ai/amazon-skills位於amazon-keyword-research提交0f3b13f

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