ecommerce-reviews

Files (2)

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

SKILL.md (first lines)

# 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:**

| Platform | Reviews URL pattern |
|----------|---------------------|
| Amazon | `https://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` |
| WooCommerce | Product page URL with `#reviews` anchor; reviews are inline on the page |
| Shopify | Reviews are typically inline on the product page |
| Generic | Navigate 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.

## 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}`

From browser-act/skills, MIT.

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