- name
- ad-intelligence
- description
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Ad Intelligence Skill
A two-phase competitive ad intelligence skill for marketing companies and ecommerce owners to research how competitors run ads across Meta, Google, and LinkedIn.
Quick Reference: What Data Is Available
| Field | Meta (FB/IG) | Google Transparency | |
|---|---|---|---|
| Ad copy / headline | ✅ Both phases | ✅ Both phases | ✅ Both phases |
| Creative (image/video URL) | ✅ Both phases | ✅ Both phases | ✅ Both phases |
| Ad format type | ✅ Both phases | ✅ Both phases | ✅ Both phases |
| Date first/last shown | ✅ Both phases | ✅ Both phases | ✅ Both phases |
| Platform / placements | ✅ Both phases | ✅ (Google Search, YT, Display, etc.) | ✅ Both phases |
| Active / inactive status | ✅ Both phases | ✅ Both phases | ✅ Both phases |
| CTA button | ✅ Phase 1 scrape | ❌ | ✅ Both phases |
| Destination / landing URL | ✅ Both phases | ✅ Both phases | ✅ Both phases |
| Total ads running | ✅ Phase 2 | ✅ Phase 2 | ✅ Phase 2 |
| Impression range | ✅ Phase 2 (EU/political only) | ❌ | ✅ Phase 2 (some scrapers) |
| Spend range | ✅ Phase 2 (EU/political only) | ❌ | ❌ |
| Demographic breakdown | ✅ Phase 2 (EU only) | ❌ | ❌ |
| Targeting info | ❌ | ❌ | ✅ Phase 2 (partial — language, location) |
| Advertiser ID | ✅ Phase 2 | ✅ Phase 2 | ✅ Phase 2 |
⚠️ No platform exposes: exact spend, CTR, conversion rates, ROAS, or detailed audience targeting.
Workflow
Step 1: Understand the Request
Gather from the user (ask if not provided):
- Competitor name or domain (e.g., "Nike" or "nike.com")
- Target platform(s): Meta / Google / LinkedIn (default: all three)
- Phase: Phase 1 (quick, no API keys) or Phase 2 (deeper, requires API keys)
- Country/region filter (optional, e.g., "US", "IN", "EU")
- Date range (optional, e.g., "last 30 days")
- Ad format filter (optional: image / video / carousel / text)
If the user hasn't said which phase they want, ask. If they're just exploring, start with Phase 1.
Step 2: Execute the Right Phase
Read the platform reference files for code, endpoints, and examples:
- Meta:
references/meta.md - Google:
references/google.md - LinkedIn:
references/linkedin.md
Each reference contains:
- Phase 1: Python scraping code (no API key needed)
- Phase 2a: Official/free API instructions
- Phase 2b: Third-party paid API options (SerpAPI, SearchAPI, Adyntel, Apify)
- Sample output JSON
- Known limitations
Step 3: Format the Output
Always deliver both:
A. Human-Readable Summary Report
## Ad Intelligence Report: [Company Name]
**Platforms Searched:** Meta, Google, LinkedIn
**Date:** [today]
**Phase:** 1 (Scrape) / 2 (API)
**Country:** [region]
### 📊 Overview
- Total ads found: X
- Active ads: X | Inactive: X
- Formats: X% image, X% video, X% carousel
### 🎯 Platform Breakdown
[Per platform: count, date range, notable trends]
### ✍️ Creative Themes & Messaging Patterns
[Summarize recurring hooks, CTAs, offers, tone]
### 📅 Recency & Cadence
[How often they post new ads, seasonal patterns]
### ⚠️ Data Limitations
[Note what wasn't available and why]B. Raw Structured Data (JSON)
Return normalized JSON with this schema for each ad:
{
"platform": "meta|google|linkedin",
"ad_id": "string",
"advertiser_name": "string",
"advertiser_page_url": "string",
"status": "active|inactive",
"format": "image|video|carousel|text|document",
"headline": "string|null",
"body_text": "string|null",
"cta_text": "string|null",
"destination_url": "string|null",
"creative_url": "string|null",
"platforms_served": ["facebook", "instagram", "messenger"],
"date_first_shown": "YYYY-MM-DD|null",
"date_last_shown": "YYYY-MM-DD|null",
"country": "string|null",
"spend_range": "string|null",
"impression_range": "string|null",
"targeting_summary": "string|null",
"source_phase": 1
}Step 4: Provide Next Steps
After presenting results, always suggest:
- Which Phase 2 option would give more depth for this use case
- What API keys/accounts are needed to upgrade
- Whether the data is sufficient or the user should broaden/narrow their search
Important Limitations to Always Communicate
- No private performance data: CTR, conversions, ROAS, exact spend — these are never public.
- Meta API geographic restriction: The official Meta Ad Library API only returns impression/spend data for EU-delivered ads and political/social cause ads globally. For general ecommerce ads in non-EU regions, Phase 1 scraping often gives broader coverage.
- Google has no official public API: All Google Transparency Center data is accessed via scraping (Phase 1) or third-party wrappers like SerpAPI/SearchAPI (Phase 2).
- LinkedIn targeting data is partial: Only language and location hints are sometimes visible — not job title, seniority, or company size targeting.
- Rate limits apply: All platforms rate-limit requests. Use pagination and add delays (1–2s between calls).
Phase Decision Guide
| Situation | Recommended Phase |
|---|---|
| Quick competitive overview | Phase 1 |
| No API keys available | Phase 1 |
| Need date-range filtering | Phase 2 |
| EU market + want spend/impressions | Phase 2 (official Meta API) |
| Need 500+ ads at scale | Phase 2 (paid third-party API) |
| B2B competitor research on LinkedIn | Phase 2 |
| Need creative image/video downloads | Phase 2 |
Reference Files
references/meta.md— Meta (Facebook/Instagram) scraping + API codereferences/google.md— Google Ads Transparency scraping + SerpAPI/SearchAPIreferences/linkedin.md— LinkedIn Ad Library scraping + third-party APIs