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-review-analyzergit 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-review-analyzer)<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-review-analyzer"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-review-analyzer/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-review-analyzer"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-review-analyzer.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 | $0.00068 | $0.01136 |
| Opus 5 | $0.00034 | $0.00568 |
| Sonnet 5 | $0.00014 | $0.00227 |
| Haiku 4.5 | $0.00007 | $0.00114 |
Grade A, and why
amazon-review-analyzer 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 10d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Review Analyzer 💬
Transform customer reviews into competitive intelligence and product improvement roadmaps.
Installation
npx skills add nexscope-ai/Amazon-Skills --skill amazon-review-analyzer -g
Usage Examples
Competitor review analysis:
"Analyze reviews for competitor yoga mats - what are customers complaining about?"
Product improvement insights:
"What do customers love/hate about wireless earbuds under $100?"
Market opportunity identification:
"Find unmet needs in the home security camera category from reviews"
Core Capabilities
1. Sentiment Pattern Analysis
- Star rating distribution analysis
- Positive vs negative theme extraction
- Emotional sentiment scoring
- Satisfaction trend identification
2. Complaint Mining & Prioritization
- Recurring complaint identification
- Issue severity ranking by frequency
- Quality vs usability problem separation
- Return/refund trigger analysis
3. Feature Request Extraction
- Customer-suggested improvements
- Unmet need identification
- Feature demand prioritization
- Innovation opportunity mapping
4. Competitive Review Intelligence
- Cross-competitor sentiment comparison
- Alternative product mentions
- Switching behavior patterns
- Market gap identification
How It Works
Step 1: Review Data Collection
Using web search and Amazon review mining
Gather comprehensive review data:
- Sample recent reviews across rating levels
- Extract recurring themes and language patterns
- Identify high-impact feedback signals
- Categorize by complaint type and severity
Step 2: Sentiment & Theme Analysis
Multi-dimensional review intelligence
Analyze customer feedback patterns:
- Sentiment scoring by product features
- Complaint frequency and severity ranking
- Feature request identification and prioritization
- Competitive mention analysis
Step 3: Actionable Insights Generation
Transform feedback into strategy
Generate specific recommendations:
- Product improvement priorities
- Marketing message opportunities
- Competitive positioning angles
- Quality issue mitigation strategies
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.
- 10d ago First seen · 163 lines · 68 tokens per session scan A 4487bc490242
amazon-review-analyzer is a skill published in the GitHub repository nexscope-ai/Amazon-Skills (649 stars, last pushed 15d ago), licensed MIT. It adds 68 tokens to every session and 1,136 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 2026-08-30.
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