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Agent skill

Amazon Reviews Extractor

by browser-act in browser-act/skills

Pulls structured Amazon product reviews for an ASIN through BrowserAct's Amazon Reviews API, with no Amazon login, using a bundled Python script.

MITAuto-check passedData & Analytics

Install Amazon Reviews Extractor

skills CLI
$ npx skills add browser-act/skills --skill amazon-reviews-api-skill -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install browser-act/skills amazon-reviews-api-skill --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/browser-act/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/solutions/ecommerce/amazon-reviews-api-skill .claude/skills/amazon-reviews-api-skill && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
amazon-reviews-api-skill
GitHub stars
6.1k
Used in
2 other repos
Token cost
~1.4k tokens
SKILL.md length
627 words
Files
2 (incl. scripts)
Skills in repo
87
Repo updated
First seen
Licence
MIT

At a glance

Pulls structured Amazon product reviews for an ASIN through BrowserAct's Amazon Reviews API, with no Amazon login, using a bundled Python script.

  • Works in 5 steps: No Hallucinations: Pre-set workflows… → No Captcha Issues: No need to handle… → No IP Restrictions: No need to handle… → …
  • Collecting customer reviews for a specific Amazon ASIN
  • SKILL.md covers 📖 Introduction, ✨ Features, 🔑 API Key Setup and 🛠️ Input Parameters, plus 4 more sections
  • Runs Python scripts from its folder; calls python; needs BROWSERACT_API_KEY

What it does

You give the agent an ASIN, and it runs scripts/amazon_reviews_api.py, which calls a BrowserAct template that returns review data from the product page. According to the skill, there is no need to build a crawler, log in to Amazon, or deal with captchas and regional IP limits. It suits sentiment tracking, competitor feedback, verified-purchase checks and content analysis.

Before running, the agent checks the BROWSERACT_API_KEY environment variable and, if it is missing, asks you for a key from the BrowserAct console and waits. A run can take several minutes and prints timestamped status lines; steady new output means the task is still working, and a retry is only warranted if the output stalls for a long time or stops without a result.

When your agent uses it

  • Collecting customer reviews for a specific Amazon ASIN
  • Comparing ratings and comments of a competing product
  • Monitoring recent review sentiment for a product you sell

Example prompts

  • “Get the reviews for ASIN B07TS6R1SF and summarize the main complaints.”
  • “Pull reviews for B08N5WRWJ6 and list the verified purchase ones.”
  • “Collect reviews for both of these ASINs and highlight how customer comments differ.”

Requirements

  • Python
  • A BrowserAct API key in BROWSERACT_API_KEY
  • Network access to BrowserAct

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit 11c057b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • browseract.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • BROWSERACT_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Amazon Reviews Extractor loads about 1.4k tokens when it runs. Until then it costs about 189 tokens; SKILL.md has 627 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~189
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from browser-act/skills at commit 11c057b, republished under its MIT licence (© browser-act). 627 words, ~1,406 tokens.

Download SKILL.mdSave it as .claude/skills/amazon-reviews-api-skill/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
amazon-reviews-api-skill
description
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 market research, gathering review titles and descriptions for content analysis, scraping Amazon reviews without requiring a login.

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 BrowserAct Console to get your Key."

🛠️ Input Parameters

When calling the script, the Agent should flexibly configure parameters based on user needs:

  1. ASIN (Amazon Standard Identification Number)
    • Type: string
    • Description: The unique identifier for the product on Amazon.
    • Example: B07TS6R1SF, B08N5WRWJ6

🚀 Usage

The Agent should execute the following independent script to achieve "one-line command result":

bash
# Example call
python -u ./scripts/amazon_reviews_api.py "ASIN_HERE"
⏳ Execution Monitoring

Since this task involves automated browser operations, it may take some time (several minutes). The script will continuously output status logs with timestamps (e.g., [14:30:05] Task Status: running). Agent Instructions:

  • While waiting for the script result, keep monitoring the terminal output.
  • As long as the terminal is outputting new status logs, the task is running normally; do not mistake it for a deadlock or unresponsiveness.
  • Only if the status remains unchanged for a long time or the script stops outputting without returning a result should you consider triggering the retry mechanism.

📊 Data Output

After successful execution, the script will parse and print results directly from the API response. Each review item includes:

  • Commentator: Reviewer's name
  • Commenter profile link: Link to the reviewer's profile
  • Rating: Star rating
  • reviewTitle: Headline of the review
  • review Description: Full text of the review
  • Published at: Date the review was published
  • Country: Reviewer's country
  • Variant: Product variant info (if available)
  • Is Verified: Whether it's a verified purchase
Show full SKILL.md (244 more words)Show less

⚠️ Error Handling & Retry

If an error occurs during script execution (e.g., network fluctuations or task failure), the Agent should follow this logic:

  1. Check Output Content:

    • If the output contains "Invalid authorization", it means the API Key is invalid or expired. Do not retry; guide the user to re-check and provide the correct API Key.
    • If the output does not contain "Invalid authorization" but the task failed (e.g., output starts with Error: or returns empty results), the Agent should automatically try to re-execute the script once.

  2. Retry Limit:

    • Automatic retry is limited to one time. If the second attempt fails, stop retrying and report the specific error information to the user.

🌟 Typical Use Cases

  1. Competitor Analysis: Extract reviews for competitors' products to understand their strengths and weaknesses.
  2. Product Feedback: Summarize feedback for your own products to identify areas for improvement.
  3. Market Research: Collect data on customer preferences and common complaints in a specific category.
  4. Sentiment Monitoring: Monitor recent reviews to detect shifts in customer sentiment.
  5. QA Insights: Use customer reviews to identify potential quality issues or bugs.
  6. Sentiment Analysis Prep: Gather review text and ratings for detailed emotion modeling.
  7. Verified Purchase Analysis: Compare feedback from verified vs. unverified buyers.
  8. Geographic Insights: Analyze product performance across different reviewer countries.
  9. Variant Comparison: Understand which product variants (size/color) receive the best feedback.
  10. Historical Trend Tracking: Retrieve and analyze review publication dates to track product lifecycle sentiment.

© browser-act, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in solutions/ecommerce/amazon-reviews-api-skill of browser-act/skills.

  • SKILL.md
  • scripts/amazon_reviews_api.py

Open the folder on GitHubat commit 11c057b

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in browser-act/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Amazon Reviews Extractor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Amazon Reviews Extractor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Amazon Reviews Extractor this skillbrowser-act/skills6.1k2 repos~1.4kAutomated safety check: PassMIT
Product Reel Generatorgooseworks-ai/goose-skills1.2k1 repos~2.3kAutomated safety check: NotesMIT
Extractactionbook/actionbook1.6k2 repos~3.4kAutomated safety check: PassApache-2.0
Apify Product Data Setupapify/awesome-skills2641 repos~2kAutomated safety check: PassApache-2.0
E-commerce Product Extractionfirecrawl/web-agent1.2k—~344Automated safety check: PassMIT
Google Maps Reviews Scrapergmapsscraper/google-maps-agent-skills132—~1.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Amazon Reviews Extractor

What does Amazon Reviews Extractor do?

Pulls structured Amazon product reviews for an ASIN through BrowserAct's Amazon Reviews API, with no Amazon login, using a bundled Python script. py, which calls a BrowserAct template that returns review data from the product page. According to the skill, there is no need to build a crawler, log in to Amazon, or deal with captchas and regional IP limits.

When should I use Amazon Reviews Extractor?

Amazon Reviews Extractor fits situations like: collecting customer reviews for a specific Amazon ASIN; comparing ratings and comments of a competing product; monitoring recent review sentiment for a product you sell.

How do I install Amazon Reviews Extractor in Claude Code?

Run `npx skills add browser-act/skills --skill amazon-reviews-api-skill -a claude-code`. Or copy the skill folder (solutions/ecommerce/amazon-reviews-api-skill in browser-act/skills) into .claude/skills/amazon-reviews-api-skill in your project. Claude Code loads it when a task matches its description.

How do I install Amazon Reviews Extractor in Codex?

Run `npx skills add browser-act/skills --skill amazon-reviews-api-skill -a codex`. Or copy the skill folder (solutions/ecommerce/amazon-reviews-api-skill in browser-act/skills) into .agents/skills/amazon-reviews-api-skill in your project. Codex loads it when a task matches its description.

Can I use Amazon Reviews Extractor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add browser-act/skills --skill amazon-reviews-api-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/amazon-reviews-api-skill, .gemini/skills/amazon-reviews-api-skill, .github/skills/amazon-reviews-api-skill and .opencode/skills/amazon-reviews-api-skill in your project.

What does Amazon Reviews Extractor need to run?

Going by SKILL.md and its folder, Amazon Reviews Extractor needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named BROWSERACT_API_KEY. Our summary lists: Python; A BrowserAct API key in BROWSERACT_API_KEY; Network access to BrowserAct.

Does Amazon Reviews Extractor access the network?

SKILL.md names 1 domain. As links in the text: browseract.com. This is read from the text; nothing was executed.

Is Amazon Reviews Extractor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Amazon Reviews Extractor use?

Amazon Reviews Extractor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Amazon Reviews Extractor use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Amazon Reviews Extractor?

Skills that share tags, products or a category with Amazon Reviews Extractor: Product Reel Generator (gooseworks-ai/goose-skills, 1.2k stars), Extract (actionbook/actionbook, 1.6k stars), Apify Product Data Setup (apify/awesome-skills, 264 stars) and E-commerce Product Extraction (firecrawl/web-agent, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Amazon Reviews Extractor?

browser-act (a GitHub organization) maintains it in browser-act/skills, which has 6,114 GitHub stars. The repository holds 87 skills in this directory. The repository was last updated on August 24, 2026.

Source: browser-act/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.