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-seller-analyticsgit 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-seller-analytics)<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-seller-analytics"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-seller-analytics/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-seller-analytics"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-seller-analytics.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 | $0.00074 | $0.03102 |
| Opus 5 | $0.00037 | $0.01551 |
| Sonnet 5 | $0.00015 | $0.00620 |
| Haiku 4.5 | $0.00007 | $0.00310 |
Grade A, and why
amazon-seller-analytics 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 — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Seller Analytics 📊
Analyze seller storefronts and reverse-engineer winning strategies. Competitive intelligence for Amazon success.
Installation
npx skills add nexscope-ai/Amazon-Skills --skill amazon-seller-analytics -g
Capabilities
- Revenue estimation: Calculate seller monthly/annual revenue from visible data
- Product portfolio analysis: Category diversification, pricing strategy, product mix
- Growth trajectory tracking: Historical expansion patterns and launch sequences
- Market positioning assessment: Brand positioning, customer targeting, competitive advantages
- Inventory strategy analysis: Stock depth, product lifecycle management, seasonal planning
- Pricing strategy evaluation: Margin optimization, competitive positioning, price changes
- Launch pattern identification: How successful sellers introduce new products
- Multi-marketplace tracking: Cross-platform seller presence and strategy
Usage Examples
Users can ask naturally. Examples:
Analyze the seller "ANKER" on Amazon - revenue, strategy, product portfolio
Study how successful kitchen gadget sellers structure their storefronts
Compare seller strategies: "RAVPower" vs "AUKEY" in electronics
Analyze seller growth patterns in the yoga/fitness category
Research top sellers in baby products - what makes them successful?
Reverse engineer the strategy of sellers making $1M+ in home decor
Workflow
Step 1: Seller Identification & Basic Intelligence
Gather foundational seller information:
- Seller discovery:
"top Amazon sellers [category]"or analyze specific seller names - Storefront access:
"[seller name] Amazon storefront"- find their seller page - Basic metrics:
"[seller name] Amazon seller feedback rating reviews" - Market presence:
"[seller name] brand Amazon marketplace years"
Key Data Points:
- Seller name and brand(s) operated
- Years active on Amazon (account age)
- Overall seller feedback score and review count
- Estimated number of active products
- Primary categories/markets served
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 · 358 lines · 74 tokens per session scan A 2fe8766dcced
amazon-seller-analytics is a skill published in the GitHub repository nexscope-ai/Amazon-Skills (649 stars, last pushed 15d ago), licensed MIT. It adds 74 tokens to every session and 3,102 once invoked, about $0.0004 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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