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-deal-findergit 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-deal-finder)<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-deal-finder"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-deal-finder/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-deal-finder"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-deal-finder.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 | $0.00064 | $0.03752 |
| Opus 5 | $0.00032 | $0.01876 |
| Sonnet 5 | $0.00013 | $0.00750 |
| Haiku 4.5 | $0.00006 | $0.00375 |
Grade A, and why
amazon-deal-finder 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 — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Deal Finder ⚡
Strategic deal planning and promotional optimization for Amazon sellers. Lightning Deals, coupons, and promotional ROI maximization.
Installation
npx skills add nexscope-ai/Amazon-Skills --skill amazon-deal-finder -g
Usage Examples
Deal strategy development:
"Plan Lightning Deal strategy for Q4 - which products should I promote and what's the ROI potential?"
Promotional campaign optimization:
"Compare Lightning Deals vs Coupons vs Best Deals for my electronics category - which gives best ROI?"
Deal timing and planning:
"When should I run my Lightning Deal for kitchen products to maximize sales and minimize cannibalization?"
Core Capabilities
1. Deal Type Analysis & Strategy Selection
- Comprehensive deal type evaluation (Lightning Deals, Best Deals, Coupons, Prime Exclusive)
- ROI analysis and profitability assessment for each promotional type
- Deal eligibility assessment and qualification requirements analysis
- Strategic timing and calendar planning for maximum impact
2. Product Selection & Optimization
- Product suitability analysis for different deal types and promotional strategies
- Inventory planning and stock level optimization for promotional periods
- Pricing strategy development and discount level optimization
- Competitive analysis and market positioning for promotional success
3. Performance Tracking & Campaign Management
- Deal performance monitoring and real-time optimization strategies
- Post-promotion analysis and long-term impact assessment
- Campaign calendar development and cross-promotion coordination
- ROI tracking and profitability analysis across all promotional activities
How It Works
Step 1: Deal Analysis & Strategic Planning
Comprehensive promotional opportunity assessment and strategy development
Evaluate promotional opportunities and develop strategy:
- Analyze available deal types and assess eligibility requirements for Lightning Deals, Best Deals, and coupon programs
- Evaluate product portfolio for promotional suitability based on sales velocity, margins, and competitive positioning
- Calculate ROI potential and profitability impact for different promotional strategies and discount levels
- Develop strategic promotional calendar aligned with seasonal trends, competitive landscape, and business objectives
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 · 372 lines · 64 tokens per session scan A a7bf4672984d
amazon-deal-finder is a skill published in the GitHub repository nexscope-ai/Amazon-Skills (655 stars, last pushed 16d ago), licensed MIT. It adds 64 tokens to every session and 3,752 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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