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 agentmods add skills/nexscope-ai/amazon-skills/amazon-return-reductionnpx skills add nexscope-ai/Amazon-Skills --skill amazon-return-reductiongit 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-return-reduction)<a href="https://agentmods.dev/skills/nexscope-ai/amazon-skills/amazon-return-reduction"><img src="https://agentmods.dev/badge/skills/nexscope-ai/amazon-skills/amazon-return-reduction.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00021 | $0.00412 |
| Opus 5 | $0.00010 | $0.00206 |
| Sonnet 5 | $0.00004 | $0.00082 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
amazon-return-reduction 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 6d 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.
What it actually says
Amazon Return Reduction
Return rate reduction — root cause analysis, listing accuracy, packaging improvements, size guides
Supported platforms: Amazon (US, UK, DE, CA, JP, AU, and all marketplaces).
Built by Nexscope — your AI assistant for smarter e-commerce decisions.
Install
npx skills add nexscope/amazon-return-reduction
Usage
Help me with amazon return reduction for my e-commerce business.
Capabilities
- Return rate reduction
- root cause analysis
- listing accuracy
- packaging improvements
- size guides
How This Skill Works
Step 1: Collect information from the user's message — product, platform, current situation, and goals.
Step 2: Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a").
Step 3: Research and analyze using the frameworks and methodology below.
Step 4: Deliver structured, actionable output with specific recommendations, not vague advice.
Output Format
- Start with a summary of findings
- Include specific data points and benchmarks where available
- Provide prioritized action items
- Mark estimates with ⚠️ when based on incomplete data
- End with concrete next steps
Other Skills
More e-commerce skills: nexscope-ai/eCommerce-Skills
Amazon-specific skills: nexscope-ai/Amazon-Skills
Built by Nexscope — your AI assistant for smarter e-commerce decisions.
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.
- 6d ago First seen · 62 lines · 21 tokens per session scan A ed38be6d8457
amazon-return-reduction is a skill published in the GitHub repository nexscope-ai/Amazon-Skills (629 stars, last pushed 10d ago), licensed MIT. It adds 21 tokens to every session and 412 once invoked, about $0.0001 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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