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-repricing-strategygit 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-repricing-strategy)<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-repricing-strategy"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-repricing-strategy/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-repricing-strategy"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-repricing-strategy.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 | $0.00061 | $0.03384 |
| Opus 5.5 | $0.00024 | $0.01354 |
| Sonnet 5.5 | $0.00012 | $0.00677 |
| Haiku 4.5 | $0.00006 | $0.00338 |
Grade A, and why
amazon-repricing-strategy 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 1mo 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Repricing Strategy 🏷️
Strategic repricing and Buy Box optimization for Amazon sellers. Dynamic pricing rules, competitive analysis, and automated workflows.
Installation
npx skills add nexscope-ai/Amazon-Skills --skill amazon-repricing-strategy -g
Usage Examples
Repricing strategy development:
"Set up repricing strategy for my electronics products - need to win Buy Box while protecting 25% margins"
Competitive pricing analysis:
"My main competitor keeps undercutting my prices by $0.50 - how should I respond without starting a price war?"
Repricing tool selection:
"Compare repricing tools and recommend the best one for my 500-ASIN catalog with $2M annual revenue"
Core Capabilities
1. Buy Box Strategy & Competitive Analysis
- Buy Box algorithm analysis and winning factor identification
- Competitive landscape mapping and pricing pattern analysis
- Market positioning strategy and price elasticity assessment
- Seasonal pricing trends and promotional impact evaluation
2. Dynamic Pricing Rules & Automation
- Intelligent pricing rule development and margin protection strategies
- Multi-tier pricing strategies for different product categories and lifecycles
- Automated repricing workflows with safety controls and monitoring
- Performance tracking and rule optimization based on results
3. Tool Selection & Implementation
- Repricing software evaluation and selection criteria assessment
- Implementation planning and integration with existing systems
- ROI analysis and cost-benefit evaluation of different pricing tools
- Training and optimization recommendations for maximum effectiveness
How It Works
Step 1: Market Analysis & Competitive Intelligence
Comprehensive pricing landscape assessment and strategy development
Analyze competitive pricing environment:
- Map competitive landscape including direct and indirect competitors with pricing patterns
- Analyze Buy Box winning factors including price, fulfillment method, seller metrics, and inventory levels
- Assess price elasticity and demand sensitivity across different price points and seasons
- Identify market opportunities and competitive vulnerabilities for strategic pricing advantage
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.
- 1mo ago First seen · 349 lines · 61 tokens per session scan A 9a8216c543ee
amazon-repricing-strategy is a skill published in the GitHub repository nexscope-ai/Amazon-Skills (806 stars, last pushed 2mo ago), licensed MIT. It adds 61 tokens to every session and 3,384 once invoked, about $0.0002 per session on Opus 5.5. 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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