Scrape competitor ads from Meta's Ad Library (Facebook, Instagram, Messenger, Threads, WhatsApp). Search by company name, Facebook Page URL, or keyword. Returns ad creatives, spend estimates, reach, impressions, and campaign details. Use for competitive ad research, messaging analysis, and creative inspiration.
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Scrape ads from Meta's Ad Library using the Apify apify/facebook-ads-scraper actor. Covers Facebook, Instagram, Messenger, Threads, and WhatsApp.
Requires APIFY_API_TOKEN env var (or --token flag). Install dependency: pip install requests.
# Search ads by company name
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--company "Nike"
# Search with country filter
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--company "Shopify" --country US
# Search by keyword (broader than company name)
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--company "project management software"
# Limit results
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--company "HubSpot" --max-ads 20
# Search by Facebook Page URL directly
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--page-url "https://www.facebook.com/nike"
# Only active ads (default), or all ads
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--company "Salesforce" --ad-status all
# Human-readable summary
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--company "Stripe" --output summary
apify/facebook-ads-scraper actor via REST APIThe script handles the advertiser lookup automatically:
facebook.com/ads/library/?q=CompanyName — the Apify actor searches Meta's Ad Library for matching advertisers--page-url for exact matchingNo need to manually find Page IDs. The Apify actor resolves the search internally.
| Flag | Default | Description |
|---|---|---|
--company | required* | Company name or keyword to search |
--page-url | none | Facebook Page URL for exact advertiser match |
--country | ALL | 2-letter country code (US, GB, DE, etc.) or ALL |
--ad-status | active | active or all (includes inactive) |
--max-ads | 50 | Maximum number of ads to return |
--output | json | Output format: json or summary |
--token | env var | Apify token (prefer APIFY_API_TOKEN env var) |
--timeout | 300 | Max seconds to wait for the Apify run |
*Either --company or --page-url is required.
Each ad in the output contains:
{
"ad_id": "123456789",
"page_name": "Nike",
"page_id": "123456789",
"ad_text": "Just Do It. Shop the latest...",
"ad_creative_link_title": "Nike.com",
"ad_creative_link_description": "Free shipping on orders...",
"ad_creative_link_url": "https://nike.com/...",
"image_url": "https://...",
"video_url": "https://...",
"ad_delivery_start_time": "2026-01-15",
"ad_delivery_stop_time": null,
"currency": "USD",
"spend_lower": 100,
"spend_upper": 499,
"impressions_lower": 1000,
"impressions_upper": 4999,
"platforms": ["facebook", "instagram"],
"status": "ACTIVE"
}
~$5 per 1,000 ads scraped on Apify Free plan. Paid plans are cheaper ($3.40-$5/1K).
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--company "Competitor Name" --country US --max-ads 100 --output summary
# Search by keyword to see all advertisers in a space
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
--company "CRM software" --max-ads 50
Run the script multiple times for each competitor and compare creative approaches, messaging, and spend ranges.
--ad-status all to include historical ads.--timeout.See references/apify-config.md for detailed API configuration, token setup, and rate limits.
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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.
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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.
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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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