Agent skill · Backend & API

amazon-reviews-api-skill

This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a competitive product, tracking sentiment of recent Amazon reviews, extracting verified purchase reviews for quality assessment, summarizing user experiences from Amazon product pages, monitoring product performance through customer reviews, collecting reviewer profiles and links for m

browser-actgithub.com/browser-actGitHub ↗
claude-codecodexcursorships scriptsMIT
Install
npx skills add browser-act/skills --skill amazon-reviews-api-skill --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 5 KB
Bundled scripts: yes
Path: solutions/ecommerce/amazon-reviews-api-skill/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 5,160
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Amazon Reviews Automation Extraction Skill ## 📖 Introduction This skill provides a one-stop Amazon review collection service through BrowserAct's Amazon Reviews API template. It can directly extract structured review results from Amazon product pages. By simply providing an ASIN, you can get clean, usable review data without building crawler scripts or requiring an Amazon account login. ## ✨ Features 1. **No Hallucinations**: Pre-set workflows avoid AI generative hallucinations, ensuring stable and precise data extraction. 2. **No Captcha Issues**: No need to handle reCAPTCHA or other verification challenges. 3. **No IP Restrictions**: No need to handle regional IP restrictions or geofencing. 4. **Faster Execution**: Tasks execute faster compared to pure AI-driven browser automation solutions. 5. **Cost-Effective**: Significantly lowers data acquisition costs compared to high-token-consuming AI solutions. ## 🔑 API Key Setup Before running, check the `BROWSERACT_API_KEY` environment variable. If not set, do not take other measures; ask and wait for the user to provide it. **Agent must inform the user**: > "Since you haven't configured the BrowserAct API Key, please visit the [Br

What's inside
Steps it walks through
  1. 📖 Introduction
  2. ✨ Features
  3. 🔑 API Key Setup
  4. 🛠️ Input Parameters
  5. 🚀 Usage
  6. ⏳ Execution Monitoring
  7. 📊 Data Output
  8. ⚠️ Error Handling & Retry
  9. 🌟 Typical Use Cases
Ships with 1 file
  • scripts/amazon_reviews_api.py
Commands it runs
Example call
python -u ./scripts/amazon_reviews_api.py "ASIN_HERE"
More from skills
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About this skill
What does the amazon-reviews-api-skill skill do?

This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a competitive product, tracking sentiment of recent Amazon reviews, extracting verified purchase reviews for quality assessment, summarizing user experiences from Amazon product pages, monitoring product performance through customer reviews, collecting reviewer profiles and links for m

How do I install it?

Run `npx skills add browser-act/skills --skill amazon-reviews-api-skill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From browser-act/skills, a repository with 5,160 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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