Scrape product reviews from G2, Capterra, and Trustpilot using Apify. Single script with platform dispatch. Use when you need to monitor competitor reviews, track product sentiment, or gather customer feedback from review sites.
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Scrape product reviews from G2, Capterra, and Trustpilot using platform-specific Apify actors.
Requires APIFY_API_TOKEN env var (or --token flag). No external dependencies needed (uses stdlib urllib).
# Trustpilot reviews
python3 skills/capabilities/review-site-scraper/scripts/scrape_reviews.py \
--platform trustpilot \
--url "https://www.trustpilot.com/review/example.com" \
--max-reviews 10 --output summary
# G2 reviews with keyword filter
python3 skills/capabilities/review-site-scraper/scripts/scrape_reviews.py \
--platform g2 \
--url "https://www.g2.com/products/example/reviews" \
--keywords "pricing,support"
# Capterra reviews (uses company name, not URL)
python3 skills/capabilities/review-site-scraper/scripts/scrape_reviews.py \
--platform capterra \
--company-name "HubSpot CRM" \
--max-reviews 20
| Platform | Actor | Input | Cost |
|---|---|---|---|
| G2 | focused_vanguard/g2-reviews-scraper | --url (G2 product page URL) | Free tier available |
| Capterra | getdataforme/capterra-reviews-scraper-bulk | --company-name (company name, not a URL) | Pay-per-result |
| Trustpilot | agents/trustpilot-reviews | --url (Trustpilot review page URL) | ~$0.20/1k reviews |
| Flag | Default | Description |
|---|---|---|
--platform | required | g2, capterra, or trustpilot |
--url | none | Product review page URL (required for G2 and Trustpilot) |
--company-name | none | Company name to search (Capterra only) |
--max-reviews | 50 | Max reviews to scrape |
--keywords | none | Keywords to filter (comma-separated, OR logic) |
--days | none | Only include reviews from last N days |
--output | json | Output format: json or summary |
--token | env var | Apify token (prefer APIFY_API_TOKEN env var) |
--timeout | 300 | Max seconds for Apify run |
All platforms are normalized but each has platform-specific fields.
G2 output fields:
{
"platform": "g2",
"id": "review-id",
"product_name": "Product Name",
"title": null,
"text": "Review body text",
"rating": 4,
"author": "Reviewer Name",
"author_title": "Job Title",
"author_company": "Company Name",
"author_company_size": "51-200",
"author_industry": "Software",
"date": "2026-02-18",
"source": "organic",
"url": "https://..."
}
Capterra output fields:
{
"platform": "capterra",
"title": "Review title",
"text": "Review body text",
"overall_rating": 4,
"ease_of_use": 5,
"customer_service": 3,
"features": 4,
"author": "Reviewer Name",
"job_title": "Marketing Manager",
"industry": "Marketing and Advertising",
"usage_duration": "1-2 years",
"date": "2026-02-18",
"url": "https://..."
}
Trustpilot output fields:
{
"platform": "trustpilot",
"id": "review-id",
"title": "Review title",
"text": "Review body text",
"rating": 4,
"author": "Reviewer Name",
"date": "2026-02-18T12:00:00.000Z",
"experienced_date": "2026-02-15T00:00:00.000Z",
"likes": 2,
"input_source": "organic",
"url": "https://..."
}
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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