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.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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://..."
}
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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).
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。