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Agent Skill · 开发

amazon-review-request-optimizer(亚马逊审核请求优化器)

amazon-review-request-optimizer 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

amazon-review-request-optimizer 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 6,738。

总安装 4,412周安装 182GitHub Stars 公开资料未说明下载 1,441核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:amazon-review-request-optimizer(亚马逊审核请求优化器)
来源仓库:https://github.com/mguozhen/amazon-review-request-optimizer
安装命令:
openclaw skills install amazon-review-request-optimizer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install amazon-review-request-optimizer

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

优化亚马逊评论请求时机与频率控制策略。识别低效ASIN并计算最佳请求时间窗口。自动检查Amazon ToS合规性降低封号风险。安装命令:openclaw skills install amazon-review-request-optimizer。操作前务必阅读最新平台政策防止违规触发风控。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install amazon-review-request-optimizer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

amazon-review-request-optimizer 怎么安装?

在终端执行 openclaw skills install amazon-review-request-optimizer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

amazon-review-request-optimizer 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

amazon-review-request-optimizer 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

amazon-review-request-optimizer 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

amazon-review-request-optimizer 用的人多吗?

公开数据是累计安装 4,412 次,近一周安装 182 次,下载 1,441 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

amazon-review-request-optimizer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
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skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
amazon-review-request-optimizer

亚马逊审核请求优化器 · 本页

4,412—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
amazon-review-request-optimizer
description
Amazon review request optimization agent. Identifies underperforming ASINs by review velocity, calculates optimal timing windows, checks Amazon ToS compliance, and generates compliant follow-up messaging that maximizes response rates without risking account health. Triggers: review request, amazon review, review velocity, review timing, review rate, review optimization, request review, review strategy, seller feedback, amazon feedback, review follow up, review message
allowed-tools
Bash
metadata
openclaw
homepage
https://github.com/mguozhen/amazon-review-request-optimizer

Amazon Review Request Optimizer

AI-powered Amazon review request agent — identifies which ASINs need review attention, calculates optimal timing windows, and generates ToS-compliant messaging that maximizes response rates.

Provide your ASIN list, order volume, and current review counts. The agent benchmarks your velocity against category averages, identifies review gaps, and produces compliant follow-up messaging templates ready to use in Seller Central's Request a Review tool or approved third-party tools.

Commands

review audit                       # full review health audit across all ASINs you provide
review velocity <asin>             # calculate current review velocity vs. category benchmark
review timing                      # identify optimal post-delivery timing windows by category
review message                     # generate compliant review request message templates
review benchmark                   # show category-level review rate benchmarks
review request rate                # calculate your current request-to-review conversion rate
review risk check                  # scan messaging for Amazon ToS compliance issues
review save                        # save audit and templates to ~/amazon-reviews/

What Data to Provide

The agent works with:

  • ASIN list — paste ASINs with current star rating and review count
  • Order volume — monthly units sold per ASIN (or estimate)
  • Delivery timeframes — average days to delivery for your products
  • Category — product category (electronics, kitchenware, apparel, etc.)
  • Existing messaging — paste your current follow-up emails for compliance review
  • Buyer segments — if available, distinguish new vs. repeat buyers

No API keys required. Works from pasted data and verbal descriptions.

Workspace

Creates ~/amazon-reviews/ containing:

  • memory.md — saved ASIN profiles, category baselines, and velocity history
  • templates/ — approved message templates saved as markdown
  • audits/ — past audit reports (audit-YYYY-MM-DD.md)

Analysis Framework

1. Review Velocity Benchmarks by Category

Review velocity is measured as reviews earned per orders shipped:

CategoryExpected VelocityStrong Velocity
Electronics1 review per 50 orders1 per 30 orders
Kitchenware / Home1 review per 30 orders1 per 20 orders
Apparel / Fashion1 review per 40 orders1 per 25 orders
Beauty / Personal Care1 review per 25 orders1 per 15 orders
Books1 review per 100 orders1 per 60 orders
Toys / Games1 review per 35 orders1 per 20 orders
Sports / Outdoors1 review per 40 orders1 per 25 orders

Flag ASINs performing below expected velocity as underperforming — prioritize these for review request focus.

2. Optimal Timing Windows

Timing of the review request relative to delivery is the single highest-impact variable:

  • Day 4-7 post-delivery — optimal window for most categories; buyer has used the product enough to have an opinion but the experience is still fresh
  • Day 2-3 — too early for most physical goods; buyer may not have opened the package
  • Day 8-14 — acceptable for complex products (electronics, assembly-required) requiring more setup time
  • Day 14+ — diminishing response rate; buyer memory of experience fades
  • Adjust for category: consumables (day 7-10, after first use), apparel (day 4-6, after wearing), electronics (day 7-10, after setup)

One request per order is the Amazon-permitted maximum. Do not send multiple follow-ups.

3. Amazon ToS Compliance Checklist

Every message template must pass all compliance checks before use:

Prohibited (account suspension risk)

  • Offering incentives for reviews (discounts, refunds, free products, gift cards)
  • Asking only for positive reviews or filtering by satisfaction ("if you're happy, please review")
  • Asking buyers to change or remove an existing review
  • Including marketing content or promotions in the review request message
  • Sending to buyers who opted out of Seller Messaging

Required Elements

  • Reference to the specific order (order number or product name)
  • Genuine, unconditional request for honest feedback
  • No pressure language or urgency around leaving a review
  • If using third-party tools: must be Amazon-approved tools only

Compliant Language Patterns

  • "We'd love to hear your honest feedback about your [Product Name]"
  • "If you have a moment, a review would help other customers make informed decisions"
  • "Your experience with this product — good or bad — is valuable"

Non-Compliant Language Patterns (never use)

  • "If you loved your purchase, please leave a 5-star review"
  • "Leave a review and get 10% off your next order"
  • "Please update your review if we've resolved your issue"

4. Buyer Segment Analysis

Buyer segment dramatically affects review response rates:

  • Repeat buyers — 3x higher review response rate than first-time buyers; prioritize in request queue
  • High-value orders — buyers who spent more tend to engage more with feedback requests
  • First-time buyers — lower response rate but higher lifetime value from the review relationship; still worth requesting
  • Returns / refunds — do not send review requests to buyers who have requested a return; Amazon flags this
  • Prime vs. non-Prime — Prime buyers tend to review more; no action needed, just context

5. Message Template Framework

Every compliant review request message follows this structure:

  1. Greeting — acknowledge the order by product name (not order number in email body)
  2. Value acknowledgment — brief genuine statement about the product's purpose
  3. Honest ask — single, unconditional request for a review
  4. No pressure close — make clear a response is optional and any feedback is welcome
  5. No promotional content — zero marketing language, no upsells, no discount codes

Keep total message length under 150 words. Shorter messages have higher open and response rates.

6. Suppressed Review Detection Signals

Reviews may be suppressed by Amazon without notification. Watch for:

  • Review count not increasing despite high order volume and confirmed requests sent
  • Star rating changing without visible new reviews appearing
  • Review total on the product page not matching total reviews shown in Seller Central
  • Sudden drop in review velocity with no change in product quality or request strategy

When suppression is suspected: check Seller Central Account Health, verify no policy warnings are active, and contact Seller Support with specific ASIN and date range data.

Output Format

Every review audit outputs:

  1. Velocity Report — table of each ASIN with current velocity vs. category benchmark
  2. Priority Queue — ASINs ranked by review gap (furthest below benchmark first)
  3. Timing Recommendations — optimal send window per ASIN based on category and delivery time
  4. Compliant Message Templates — 2-3 ready-to-use templates per ASIN or category
  5. Compliance Check — pass/fail on all ToS criteria for provided or generated messages
  6. Risk Flags — any suppression signals or account health considerations

Rules

  1. Never generate message templates that offer incentives for reviews — this is an immediate suspension risk
  2. Always run compliance check before presenting any message template as ready to use
  3. Flag any ASIN with zero reviews after 100+ orders as a potential suppression case, not just a velocity issue
  4. Distinguish between "request not sent" (operational problem) and "request sent but no response" (messaging or timing problem) before recommending fixes
  5. Never recommend sending more than one review request per order — Amazon permits exactly one
  6. When auditing buyer-provided messages, highlight every non-compliant phrase individually and suggest a compliant replacement
  7. Save all templates and audit reports to ~/amazon-reviews/ when the user requests review save

平台分布

OpenClaw

75.57%
按下载量换算1,089

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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