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/eCommerce-Skills --skill product-differentiation-amazongit clone --depth 1 https://github.com/nexscope-ai/eCommerce-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/ecommerce-skills/product-differentiation-amazon)<a href="https://agentmods.dev/skills/nexscope-ai/ecommerce-skills/product-differentiation-amazon"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/product-differentiation-amazon/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/ecommerce-skills/product-differentiation-amazon"><img src="https://agentmods.dev/badge/skills/nexscope-ai/ecommerce-skills/product-differentiation-amazon.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 16 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 | $0.00052 | $0.01125 |
| Opus 5 | $0.00026 | $0.00562 |
| Sonnet 5 | $0.00010 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
product-differentiation-amazon 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 8d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Differentiation — Amazon 🎯
Develop winning product differentiation strategies by analyzing competitor reviews and market positioning.
Installation
npx skills add nexscope-ai/eCommerce-Skills --skill product-differentiation-amazon -g
Features
- Competitor Matrix — Side-by-side product comparison
- Pain Point Mining — Extract issues from negative reviews
- USP Extraction — Identify selling points from positive reviews
- Differentiation Opportunities — Find gaps in the market
- Positioning Strategy — Market positioning recommendations
- Action Plan — Prioritized improvement roadmap
Progressive Analysis Levels
| Level | Required Data | Unlocked Analysis |
|---|---|---|
| L1 Basic | Product info | Basic comparison matrix |
| L2 Pain Points | + Competitor negative reviews | Pain point analysis |
| L3 USP | + Your positive reviews | Selling point extraction |
| L4 Complete | + Market data | Full strategy & action plan |
Analysis Dimensions
| Dimension | Method | Output |
|---|---|---|
| Feature Gap | Competitor comparison | Missing features list |
| Pain Points | Negative review NLP | Top complaints ranked |
| Selling Points | Positive review NLP | Key USPs identified |
| Price Position | Price-value mapping | Positioning quadrant |
| Quality Signals | Review sentiment | Quality perception score |
Usage
Interactive Mode
python3 scripts/analyzer.py
With Parameters
python3 scripts/analyzer.py '{
"your_asin": "B08XXXXXX1",
"competitor_asins": ["B08XXXXXX2", "B08XXXXXX3"],
"category": "Electronics"
}'
Demo Mode
python3 scripts/analyzer.py --demo
Input Example
{
"your_product": {
"asin": "B08XXXXXX1",
"title": "Wireless Earbuds Pro",
"price": 49.99,
"rating": 4.2,
"features": ["Bluetooth 5.0", "30h battery", "IPX5"]
},
"competitors": [
{
"asin": "B08XXXXXX2",
"title": "Competitor Earbuds A",
"price": 39.99,
"rating": 4.0
}
],
"negative_reviews": [...],
"positive_reviews": [...]
}
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 172 lines · 52 tokens per session scan A 95874f8d9095
product-differentiation-amazon is a skill published in the GitHub repository nexscope-ai/eCommerce-Skills (908 stars, last pushed 16d ago), licensed MIT. It adds 52 tokens to every session and 1,125 once invoked, about $0.0003 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 2026-09-03.
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