Amazon Keyword Research

What it does

When your agent uses it

• The user mentions Amazon product research
• Finding products to sell on Amazon
• Amazon keyword ideas
• Competition analysis for Amazon

Example prompts

• “t explicitly say”
• “. Also trigger when the user asks vague questions like”
• “s the competition like for X on Amazon?”
• “/amazon-keyword-research”

Requirements

• Node.js
• A Bash shell

Workflow steps

4 steps, taken from the step headings in SKILL.md.

1. 1Gather Autocomplete Data
2. 2Analyze Competition
3. 3Check Seasonality
4. 4Synthesize Report

What it can do on your machine

Read from SKILL.md and the folder at commit 0f3b13f. 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 1 file in scripts/ (Shell), which the agent can run.

  Shell commands in SKILL.md call:

  • npx

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

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • trends.google.com

  Also links to:

  • nexscope.ai

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

• Credentials

  Names no API keys, tokens, secrets or passwords.

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

Context cost

Amazon Keyword Research loads about 2k tokens when it runs. Until then it costs about 247 tokens; SKILL.md has 649 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~247

When it runs · the whole SKILL.md, loaded when a task matches

~2k

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

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 nexscope-ai/Amazon-Skills at commit 0f3b13f, republished under its MIT licence (© nexscope-ai). 649 words, ~2,028 tokens.

name

amazon-keyword-research

description

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?'.

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

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

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

<skill>/scripts/research.sh "portable blender" us
# Returns 100-200 long-tail keywords

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

"<keyword>" seasonal trends demand peak months

## 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]

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

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

© nexscope-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 2 other files (scripts) in amazon-keyword-research of nexscope-ai/Amazon-Skills.

• SKILL.md
• _meta.json
• scripts/research.sh

Open the folder on GitHubat commit 0f3b13f