Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nexscope-ai/Amazon-Skills --skill amazon-product-researchgit clone --depth 1 https://github.com/nexscope-ai/Amazon-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-product-research)<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-product-research"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-product-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-product-research"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-product-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 14 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00095 | $0.03107 |
| Opus 5 | $0.00048 | $0.01554 |
| Sonnet 5 | $0.00019 | $0.00621 |
| Haiku 4.5 | $0.00010 | $0.00311 |
Grade A, and why
amazon-product-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Product Research 🔍
Complete product research framework for Amazon sellers. Validate ideas, analyze opportunities, assess competition.
Installation
npx skills add nexscope-ai/Amazon-Skills --skill amazon-product-research -g
Capabilities
- Product opportunity scoring: Comprehensive 1-10 rating across 8 key factors
- Demand analysis: Search volume, seasonal patterns, growth trends
- Competition assessment: Competitor count, dominance, market fragmentation
- Profit potential calculation: Margin analysis, FBA fee impact, pricing strategies
- Market entry analysis: Barriers, investment required, time to profitability
- Sourcing guidance: Supplier options, MOQ requirements, quality considerations
- Risk evaluation: Market risks, regulatory issues, trend sustainability
- Multi-marketplace support: US, UK, DE, FR, IT, ES, JP, CA, AU, IN, MX, BR
Usage Examples
Users can ask naturally. Examples:
Research "wireless earbuds" as a product opportunity on Amazon
I want to sell yoga mats. Is this a good product to research?
Analyze the market for "smart water bottles" - demand, competition, profit potential
Should I sell "phone cases" or "phone stands"? Compare both opportunities
Research "Hundehalsbänder" on Amazon Germany - full market analysis
I found a product on AliExpress for $3, sells on Amazon for $25. Research this opportunity
Workflow
Step 1: Product & Market Intelligence
Gather comprehensive market data using web_search:
- Search volume & interest:
"[product]" Amazon search volume trends - Market size indicators:
"[product]" market size revenue Amazon" - Category positioning:
"[product]" Amazon category best sellers" - Seasonal patterns:
"[product]" seasonal demand trends Amazon"
What to extract:
- Approximate search volume (if available)
- Market growth indicators (growing/stable/declining)
- Category context (main category, subcategories)
- Seasonal fluctuations and peak periods
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 317 lines · 95 tokens per session scan A c46f7e54049a
amazon-product-research is a skill published in the GitHub repository nexscope-ai/Amazon-Skills (661 stars, last pushed 17d ago), licensed MIT. It adds 95 tokens to every session and 3,107 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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