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-listing-optimizationgit 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-listing-optimization)<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-listing-optimization"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-listing-optimization/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-listing-optimization"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-listing-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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]
- medium MCP Rug Pull · line 92 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 | $0.00161 | $0.04408 |
| Opus 5.5 | $0.00064 | $0.01763 |
| Sonnet 5.5 | $0.00032 | $0.00882 |
| Haiku 4.5 | $0.00016 | $0.00441 |
Grade A, and why
amazon-listing-optimization 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 — 429 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon Listing Optimization 📝
Build keyword-optimized listings from scratch, or audit and optimize existing ones. No API key — works out of the box.
Installation
npx skills add nexscope-ai/Amazon-Skills --skill amazon-listing-optimization -g
Two Modes
| Mode | When to Use | Input | Output |
|---|---|---|---|
| A — Create | Building a new listing | Keywords and/or competitor ASINs + product info + tone | Full listing copy + keyword coverage score |
| B — Optimize | Improving an existing listing | Your ASIN or URL (+ optional keywords or competitor ASINs) | Optimized listing copy + audit report + gap analysis |
Mode A — Three Ways to Start
| Input Source | How it Works |
|---|---|
| Keywords | User provides keyword list → skill prioritizes and generates listing |
| Competitor ASINs | User provides 1-3 competitor ASINs → skill fetches their listings, extracts their keywords, then generates a listing that covers all their keywords and more |
| Both | User provides keywords + competitor ASINs → skill merges both sources for maximum coverage |
Capabilities
- Keyword-driven listing generation: Import keywords (from amazon-keyword-research, manual list, or extracted from competitor ASINs), rank by priority, generate copy that maximizes keyword coverage
- Competitor keyword extraction: Fetch competitor listings and automatically extract their title/bullet keywords as your baseline
- 8-dimension audit & scoring: Title, bullets, description, images, A+ content, pricing, reviews, SEO coverage
- Keyword coverage tracking: Visual map showing which keywords appear in title / bullets / description / missing
- Tone selection: Professional, Friendly, Urgent, Luxury — affects AI copywriting style
- Competitive benchmarking: Compare your listing against competitors
- Multi-marketplace: US, UK, DE, FR, IT, ES, JP, CA, AU, IN, MX, BR
Usage Examples
Mode A — Create from Keywords
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 429 lines · 161 tokens per session scan A c9622e2ac733
amazon-listing-optimization is a skill published in the GitHub repository nexscope-ai/Amazon-Skills (795 stars, last pushed 1mo ago), licensed MIT. It adds 161 tokens to every session and 4,408 once invoked, about $0.0006 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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