An AI-powered agent designed to audit review velocity, determine optimal request timing, and generate ToS-compliant messaging for Amazon sellers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install amazon-review-request-optimizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install amazon-review-request-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Amazon Review Request Optimizer?
The Amazon Review Request Optimizer is a specialized agent within the Openclaw Skills ecosystem built to help Amazon sellers scale their social proof safely. It bridges the gap between high order volume and low review counts by identifying underperforming ASINs and benchmarking them against category-specific standards. By analyzing delivery windows and buyer behavior, the skill provides data-driven recommendations on when and how to ask for feedback.
Beyond simple automation, this skill acts as a compliance safeguard. It meticulously scans messaging for prohibited language—such as incentivized reviews or positive-only requests—ensuring your seller account remains in good standing with Amazon’s Terms of Service. Whether you are launching a new product or maintaining a top-tier brand, this skill optimizes your review funnel for maximum conversion.
Amazon Review Request Optimizer Use Cases
- Identifying which products in your catalog have a review gap compared to category averages.
- Generating message templates that are guaranteed to be Amazon ToS-compliant.
- Determining the perfect day post-delivery to trigger a review request for specific product types.
- Auditing existing email sequences to remove high-risk language that could lead to account suspension.
- Detecting potential review suppression signals when review counts don't align with sales volume.
How Amazon Review Request Optimizer Works
- Data Input: You provide your ASIN list, monthly order volume, and current review metrics to the agent.
- Velocity Benchmarking: The skill compares your data against industry standards for categories like Electronics, Beauty, or Home & Kitchen.
- Gap Analysis: It identifies 'underperforming' ASINs that are falling below the expected review-to-order ratio.
- Timing Calculation: Based on the category and delivery timeframes, it calculates the optimal 4-14 day window for engagement.
- Template Generation: The agent creates short, high-impact messages that focus on honest, unconditional feedback.
- Compliance Validation: A final risk check is performed to ensure no marketing language or incentives are present.
Amazon Review Request Optimizer Setup
To get started with this skill in your Openclaw environment, ensure you have the directory structure initialized and use the following commands:
# Initialize the review workspace
mkdir -p ~/amazon-reviews/templates ~/amazon-reviews/audits
# Run a full health audit on your ASINs
review audit
# Save your generated templates and reports
review save
Amazon Review Request Optimizer Data Schema & Taxonomy
The Amazon Review Request Optimizer organizes data within a dedicated local workspace to maintain history and compliance logs:
| File/Folder | Description |
|---|---|
~/amazon-reviews/memory.md |
Persistent storage for ASIN profiles, category baselines, and velocity history. |
~/amazon-reviews/templates/ |
A library of approved, ToS-compliant markdown message templates. |
~/amazon-reviews/audits/ |
Dated audit reports (e.g., audit-2026-05-09.md) tracking performance over time. |
Amazon Review Request Optimizer Advanced Features
- Multi-category benchmarking for diverse product portfolios.
- Automated compliance engine that flags non-compliant language patterns and suggests replacements.
- Buyer segment prioritization, highlighting repeat buyers who have a 3x higher response rate.
- Review suppression detection that alerts sellers to account health issues when metrics don't match sales volume.
- Support for custom post-delivery window adjustments based on product complexity (e.g., assembly-required goods).
SKILL.md
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