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-backend-keywordsgit 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-backend-keywords)<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-backend-keywords"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-backend-keywords/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-backend-keywords"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-backend-keywords.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 | $0.00053 | $0.04152 |
| Opus 5.5 | $0.00021 | $0.01661 |
| Sonnet 5.5 | $0.00011 | $0.00830 |
| Haiku 4.5 | $0.00005 | $0.00415 |
Grade A, and why
amazon-backend-keywords 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 — 431 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Backend Keywords 🔍
Strategic Amazon backend search term optimization. Maximize search visibility within the 250-byte limit through intelligent keyword prioritization.
Installation
npx skills add nexscope-ai/Amazon-Skills --skill amazon-backend-keywords -g
Usage Examples
Backend keyword optimization strategy:
"Optimize my Amazon backend search terms - I have 15 relevant keywords but only 250 bytes to work with"
Search visibility improvement:
"My product isn't showing up for important searches - help me optimize backend keywords for better discoverability"
Keyword deduplication and prioritization:
"Clean up my backend keywords and prioritize the most valuable terms for maximum search coverage"
Core Capabilities
1. Strategic Keyword Research & Prioritization
- Comprehensive keyword research and search volume analysis for optimal term selection
- Search intent analysis and customer behavior research for relevance optimization
- Competitive keyword analysis and market opportunity identification
- ROI-based keyword prioritization and value ranking systems
2. Technical Optimization & Character Management
- 250-byte limit optimization and space allocation strategies
- Advanced deduplication and redundancy elimination techniques
- Character efficiency optimization and abbreviation strategies
- Multi-language and international marketplace optimization
3. Performance Tracking & Continuous Optimization
- Search ranking monitoring and visibility tracking systems
- Keyword performance analysis and optimization impact measurement
- Seasonal optimization and trending keyword integration
- Ongoing refinement and strategic keyword evolution processes
How It Works
Step 1: Comprehensive Keyword Research & Analysis
Strategic keyword identification and market opportunity assessment
Research and identify high-value backend keyword opportunities:
- Conduct comprehensive keyword research using multiple data sources to identify all relevant search terms
- Analyze search volume, competition levels, and commercial intent to prioritize keyword opportunities
- Research competitor backend keyword strategies and identify market gaps for competitive advantage
- Map keywords to customer search intent and purchase journey stages for strategic optimization
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 · 431 lines · 53 tokens per session scan A 2f0d02ac076c
amazon-backend-keywords is a skill published in the GitHub repository nexscope-ai/Amazon-Skills (806 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 4,152 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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