Agent skill

Ecommerce Reviews

by browser-act in browser-act/skills

Extract customer reviews from any e-commerce product page or reviews page.

MITAuto-check passedSales & Support

Install Ecommerce Reviews

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

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

GitHub CLI
$ gh skill install browser-act/skills ecommerce-reviews --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/ecommerce-reviews .claude/skills/ecommerce-reviews && 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
ecommerce-reviews
GitHub stars
6.1k
Token cost
~1.4k tokens
SKILL.md length
520 words
Files
2 (incl. scripts)
Skills in repo
87
Repo updated
First seen
Licence
MIT

At a glance

Extract customer reviews from any e-commerce product page or reviews page.

  • : product reviews
  • SKILL.md covers Language, Objective, Prerequisites and Pre-execution Checks, plus 6 more sections
  • Runs Python scripts from its folder; calls python; reaches amazon.com
  • Customer feedback

What it does

Ecommerce Reviews is an agent skill from browser-act/skills. Extract customer reviews from any e-commerce product page or reviews page. Returns reviewer name, star rating, date, review title, review body, verified purchase status, and helpful votes per review. Works on Amazon, WooCommerce, Shopify, and any site with standard review markup. Supports pagination for multi-page review sections. Use when: product reviews, customer feedback, review scraping, get reviews, sentiment analysis data, review extraction, customer ratings, extract customer opinions, product feedback…

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/extract-reviews.py`).

It sits in Sales & Support, covering E-commerce operations, Customer feedback analysis and Web scraping. It works with WooCommerce and Shopify. The repository describes itself as: Browser automation CLI built for AI agents. Break through anti-bot walls, hand off to humans across platforms when stuck. Parallel multi-task execution, independent multi-session… The licence is MIT.

When your agent uses it

  • : product reviews
  • Customer feedback
  • Review scraping
  • Sentiment analysis data

Example prompts

  • “/ecommerce-reviews”

Requirements

  • Python 3

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

    Hosts in commands or code, which the agent is likely to contact:

    • amazon.com

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

  • Credentials

    Names no API keys, tokens, secrets or passwords.

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

Context cost

Ecommerce Reviews loads about 1.4k tokens when it runs. Until then it costs about 164 tokens; SKILL.md has 520 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~164
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). 520 words, ~1,371 tokens.

Download SKILL.mdSave it as .claude/skills/ecommerce-reviews/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ecommerce-reviews
description
Extract customer reviews from any e-commerce product page or reviews page. Returns reviewer name, star rating, date, review title, review body, verified purchase status, and helpful votes per review. Works on Amazon, WooCommerce, Shopify, and any site with standard review markup. Supports pagination for multi-page review sections. Use when: product reviews, customer feedback, review scraping, get reviews, sentiment analysis data, review extraction, customer ratings, extract customer opinions, product feedback, user reviews, review mining, bulk review collection, review analysis, scrape ratings and comments, ecommerce review data.

E-commerce — Product Reviews

Product URL → paginated customer reviews (reviewer, rating, date, title, body, verified, helpful votes)

Language

All process output to user (progress updates, process notifications) follows the user's language.

Objective

Extract customer reviews from any publicly accessible e-commerce product or reviews page using a multi-strategy approach (JSON-LD Review → Amazon DOM → WooCommerce DOM → generic microdata → generic CSS patterns).

Prerequisites

  • Target browser is open and connected
  • No login required for public review pages

Pre-execution Checks

1. Tool Readiness

If browser-act has been confirmed available in the current session → skip this step.

Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

Capability Components

This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page. JS code is encapsulated in Python files under the scripts/ directory, invoked via eval "$(python scripts/xxx.py {params})". Use the bash tool for execution.

DOM: Extract reviews from current page

Navigate to the product/reviews page first, then extract:

bash
eval "$(python scripts/extract-reviews.py --max-reviews 20)"

Parameters:

  • --max-reviews: max reviews to return per page, default 20

Output example:

json
{
  "count": 20,
  "reviews": [
    {
      "reviewer": "John D.",
      "rating": 5.0,
      "date": "Reviewed in the United States on May 15, 2026",
      "title": "Great product, exactly as described",
      "body": "I've been using this for two weeks and it works perfectly...",
      "verified": true,
      "helpful_votes": 42
    }
  ]
}
Composite: Product URL → reviews with sort and pagination

Step 1 — Navigate to reviews page:

PlatformReviews URL pattern
Amazonhttps://www.amazon.com/product-reviews/{ASIN}?sortBy=recent (most recent) or sortBy=helpful
Amazon (from product page)Scroll to reviews section or click "See all reviews" link, wait stable
WooCommerceProduct page URL with #reviews anchor; reviews are inline on the page
ShopifyReviews are typically inline on the product page
GenericNavigate to product URL; reviews section is usually below product info

Step 2 — Extract reviews:

bash
eval "$(python scripts/extract-reviews.py --max-reviews 20)"

Step 3 — Paginate (Amazon): Amazon review pages support URL pagination:

  • Most recent sort: https://www.amazon.com/product-reviews/{ASIN}?sortBy=recent&pageNumber={page}
  • Helpful sort: https://www.amazon.com/product-reviews/{ASIN}?sortBy=helpful&pageNumber={page}

For each page: navigate {reviews_url_with_page} → wait stable → re-run extract-reviews.py

Termination: when count returns 0, or no new reviews appear compared to prior page.

Show full SKILL.md (215 more words)Show less

Pagination

URL Pagination (Amazon): Increment pageNumber parameter in the reviews URL. Start from 1.

DOM Pagination (WooCommerce/generic): Look for a "Next" pagination link on the reviews section. Use eval "$(python ../ecommerce-listing/scripts/extract-listing-next-page.py)" to detect it, then navigate.

Termination: has_next is false, or count is 0.

Success Criteria

result.count >= 1 AND reviews[0].body != null

Known Limitations

  • Amazon: navigate from https://www.amazon.com first on fresh sessions to avoid bot detection
  • JSON-LD reviews are often limited to a small subset (3–5 reviews) even when hundreds exist; use the Amazon-specific URL for full review extraction
  • WooCommerce and Shopify review data depends on which review plugin is installed; body extraction may be null if a non-standard plugin is used
  • Review dates may be locale-formatted strings rather than ISO dates depending on the site's configuration

Execution Efficiency

  • Batch orchestration: Loop through review pages serially; add 1–2 second intervals between navigations
  • Test before batch execution: Test with page 1 before running multi-page extraction
  • Error resumption: Record page number; on failure, resume from last successful page

Experience Notes

Path: {working-directory}/browser-act-skill-forge-memories/ecommerce-scraper-ecommerce-reviews.memory.md

Before execution: If the file exists, read it first — it records unexpected situations encountered during past executions; adjust strategy order accordingly.

After execution: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line: {YYYY-MM-DD}: {what happened} → {conclusion}

© 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/ecommerce-reviews of browser-act/skills.

  • SKILL.md
  • scripts/extract-reviews.py

Open the folder on GitHubat commit 11c057b

Compare with similar skills

Ecommerce Reviews 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.

Ecommerce Reviews compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ecommerce Reviews this skillbrowser-act/skills6.1k—~1.4kAutomated safety check: PassMIT
Apify Ecommerceapify/awesome-skills265—~4.6kAutomated safety check: NotesApache-2.0
Shopifyericrisco/rsc-harness174—~3.2kAutomated safety check: PassMIT
Extractactionbook/actionbook1.6k2 repos~3.4kAutomated safety check: PassApache-2.0
Agentkeychainbase-labs/Agentkey656—~2.3kAutomated safety check: PassMIT
Checkout Purchasekeypo-us/keypo-cli182—~880Automated safety check: NotesNone

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Categories

Questions about Ecommerce Reviews

What does Ecommerce Reviews do?

Extract customer reviews from any e-commerce product page or reviews page. Ecommerce Reviews is an agent skill from browser-act/skills. Extract customer reviews from any e-commerce product page or reviews page.

When should I use Ecommerce Reviews?

Ecommerce Reviews fits situations like: : product reviews; customer feedback; review scraping; sentiment analysis data.

How do I install Ecommerce Reviews in Claude Code?

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

How do I install Ecommerce Reviews in Codex?

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

Can I use Ecommerce Reviews 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 ecommerce-reviews -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ecommerce-reviews, .gemini/skills/ecommerce-reviews, .github/skills/ecommerce-reviews and .opencode/skills/ecommerce-reviews in your project.

What does Ecommerce Reviews need to run?

Going by SKILL.md and its folder, Ecommerce Reviews needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Ecommerce Reviews access the network?

SKILL.md names 1 domain. In commands or code: amazon.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Ecommerce Reviews 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 Ecommerce Reviews use?

Ecommerce Reviews 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 Ecommerce Reviews use?

About 1.4k tokens (SKILL.md is roughly 5.5k 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 Ecommerce Reviews?

Skills that share tags, products or a category with Ecommerce Reviews: Apify Ecommerce (apify/awesome-skills, 265 stars), Shopify (ericrisco/rsc-harness, 174 stars), Extract (actionbook/actionbook, 1.6k stars) and Agentkey (chainbase-labs/Agentkey, 656 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ecommerce Reviews?

browser-act (a GitHub organization) maintains it in browser-act/skills, which has 6,122 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.