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Meta Ad Scraper

OrganizationPopular
gooseworks-ai
meta-ad-scraper

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.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill namemeta-ad-scraper
Stars
1.2K
Forks
208
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Meta Ad Scraper AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/ads/capabilities/meta-ad-scraper .claude/skills/meta-ad-scraper
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Meta Ad Scraper in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Meta Ad Scraper on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Meta Ad Scraper is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Meta Ad Library Scraper

Scrape ads from Meta's Ad Library using the Apify apify/facebook-ads-scraper actor. Covers Facebook, Instagram, Messenger, Threads, and WhatsApp.

Quick Start

Requires APIFY_API_TOKEN env var (or --token flag). Install dependency: pip install requests.

bash
# 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

How It Works

  1. Takes a company name, keyword, or Facebook Page URL
  2. Constructs a Meta Ad Library URL with the search query and filters
  3. Calls the Apify apify/facebook-ads-scraper actor via REST API
  4. Polls until the run completes, then fetches the dataset
  5. Parses and outputs ad data as JSON or human-readable summary

Resolving Company Name → Ads

The script handles the advertiser lookup automatically:

  • Company name: Constructs a search URL like facebook.com/ads/library/?q=CompanyName — the Apify actor searches Meta's Ad Library for matching advertisers
  • Page URL: If you have the Facebook Page URL, pass it via --page-url for exact matching
  • Domain: You can also pass a domain and the script will search for it

No need to manually find Page IDs. The Apify actor resolves the search internally.

CLI Reference

FlagDefaultDescription
--companyrequired*Company name or keyword to search
--page-urlnoneFacebook Page URL for exact advertiser match
--countryALL2-letter country code (US, GB, DE, etc.) or ALL
--ad-statusactiveactive or all (includes inactive)
--max-ads50Maximum number of ads to return
--outputjsonOutput format: json or summary
--tokenenv varApify token (prefer APIFY_API_TOKEN env var)
--timeout300Max seconds to wait for the Apify run

*Either --company or --page-url is required.

Output Fields

Each ad in the output contains:

json
{
  "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"
}

Cost

~$5 per 1,000 ads scraped on Apify Free plan. Paid plans are cheaper ($3.40-$5/1K).

Common Workflows

1. Competitor Ad Research

bash
python3 skills/meta-ad-scraper/scripts/search_meta_ads.py \
  --company "Competitor Name" --country US --max-ads 100 --output summary

2. Industry Ad Landscape

bash
# 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

3. Compare Multiple Competitors

Run the script multiple times for each competitor and compare creative approaches, messaging, and spend ranges.

Important Notes

  • EU/UK ads are most complete: Meta archives all ads shown in EU/UK. For US-only ads, coverage may be limited to political/issue ads.
  • Active vs All: By default only active ads are returned. Use --ad-status all to include historical ads.
  • Rate limits: Apify handles rate limiting internally. For large scrapes, increase --timeout.

Configuration

See references/apify-config.md for detailed API configuration, token setup, and rate limits.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Meta Ad Scraper AI skill do?

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.

Why use Meta Ad Scraper on TypingMind?

Because you install it once and use it with any model. Meta Ad Scraper is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Meta Ad Scraper in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/meta-ad-scraper. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Meta Ad Scraper?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Meta Ad Scraper?

As many as you like. As long as a model supports skills, you can use Meta Ad Scraper with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Meta Ad Scraper AI skill free?

Yes. It is published on GitHub by gooseworks-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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