competitor-ad-intelligence

name	competitor-ad-intelligence
description	Use this skill when the user asks to analyze, tear down, or reverse-engineer a competitor's paid ads. Trigger for prompts like "what ads is [competitor] running", "tear down their ad strategy", "competitor ad analysis", "find ad angles we haven't tried", or "reverse-engineer their paid funnel". Do not trigger for organic/SEO competitor research or website positioning analysis.
license	MIT
compatibility	Cross-platform. Uses web search and public ad libraries (Meta Ad Library, Google Ads Transparency Center) only — no API keys or credentials required.
metadata	{"version":"1.0","author":"GooseWorks","source":"https://github.com/gooseworks-ai/goose-skills"}

Competitor Ad Intelligence

Scrape competitor ads from Meta and Google, analyze creative patterns, reverse-engineer landing page funnels, and produce a full strategic teardown — hooks, formats, positioning bets, vulnerabilities, and counter-plays.

Core principle: A competitor's ad portfolio is a window into their growth strategy. Long-running ads reveal what converts. New ads reveal what they're testing. Landing pages reveal their positioning bets. The best ad creative teams start with evidence from what's already working, then differentiate.

When to Use

• "What ads are my competitors running?"
• "Tear down [competitor]'s ad strategy"
• "Find new creative angles for our paid campaigns"
• "Reverse-engineer [competitor]'s paid funnel"
• "What hooks are working in [our space]?"
• "Audit the ad landscape before we launch"
• "Find weaknesses in [competitor]'s ad strategy"
• "What format — video, image, carousel — is dominant in our category?"

Phase 0: Intake

Gather from the user:

1. Competitor names + domains (e.g., apollo.io, clay.run)
2. Your product/domain — for comparison framing
3. Channels: Meta only, Google only, or both? (default: both)
4. Depth level:
   • Standard: Ad scrape + creative analysis + landing page analysis
   • Deep: Standard + historical comparison + funnel reconstruction + counter-plays
5. Product category — helps frame analysis
6. Known competitor landing pages? — any URLs already spotted in their ads

Phase 1: Scrape Meta Ads

For each competitor domain, scrape ads from Meta Ad Library.

Use web_search to find competitor ads in the Meta Ad Library (publicly accessible, no API key needed):

web_search: site:facebook.com/ads/library "[competitor_name]"
web_search: "[competitor_name]" Meta Ad Library active ads
web_search: "[competitor_name]" facebook ads examples

You can also visit the Meta Ad Library directly: https://www.facebook.com/ads/library/?active_status=active&ad_type=all&country=US&q=<competitor_name>

Use fetch_webpage on the Ad Library URL to extract ad details if your agent supports it.

Note: Apify actors for Meta Ad Library scraping exist but are unreliable as of April 2026 due to Meta's anti-scraping measures. Use web_search as the primary method.

Collect per ad:

• Ad copy (headline + primary text)
• Visual type (image / video / carousel)
• CTA button text
• Landing page URL
• Active duration (first seen, still running or stopped)
• Platforms (Facebook, Instagram, Audience Network)
• Ad variations (A/B tests — same landing page, different creative)

Phase 2: Scrape Google Ads

For each competitor domain, scrape ads from Google Ads Transparency Center.

Use web_search to find competitor ads in Google Ads Transparency Center (publicly accessible):

web_search: site:adstransparency.google.com "[competitor_name]"
web_search: "[competitor_name]" Google Ads transparency
web_search: "[competitor_name]" google search ads examples

You can also visit directly: https://adstransparency.google.com/?search_text=<competitor_name>

Use fetch_webpage on the Transparency Center URL to extract ad details if your agent supports it.

Collect per ad:

• Headline variants (up to 3)
• Description lines
• Ad type (Search / Display / YouTube / Shopping)
• Landing page URL
• Geographic targeting (if visible)

Phase 3: Analyze Creative Patterns

After collecting all ads, perform structured analysis.

Hook Pattern Clustering

Group all ad headlines/openers by hook type:

Hook Type	Pattern	Example
Fear/Loss	Risk of missing out or falling behind	"Your competitors are already using AI SDRs"
Outcome	Direct result promise	"10x your pipeline in 30 days"
Question	Challenges current assumption	"Still doing outbound manually?"
Social proof	Names customers or numbers	"Join 500+ B2B teams using [product]"
Contrarian	Challenges conventional wisdom	"Cold email isn't dead. Your copy is."
Empathy	Validates their pain	"We know SDR ramp time is brutal"
Product-led	Feature as hook	"[Feature] is live — see what's new"

Count how many ads per competitor use each hook type. This reveals their primary messaging strategy.

Format Distribution

Format	Meta	Google
Static image	[N]	N/A
Video	[N]	[N]
Carousel	[N]	N/A
Search text	N/A	[N]
Display banner	N/A	[N]

CTA Taxonomy

List all unique CTAs found. Common patterns:

• Urgency: "Start free", "Try now", "Get started today"
• Low-friction: "See how it works", "Watch demo", "Learn more"
• Outcome: "Book a demo", "Get your free audit", "Calculate your ROI"

Phase 4: Landing Page & Funnel Analysis

For each unique landing page URL found in ads, fetch and analyze:

fetch_webpage: [landing_page_url]

Or use curl if fetch_webpage is unavailable.

Extract per landing page:

• Hero headline — Does it match the ad promise?
• Subheadline — Value prop expansion
• Primary CTA — What action are they driving? (Demo / Free trial / Sign up / Download)
• Social proof — Logos, testimonials, case study metrics
• Pricing visibility — Is pricing shown or hidden?
• Form fields — How much info do they ask for?
• Page type — General homepage / dedicated LP / feature page / use-case page
• Message match score — How well does the LP deliver on the ad's promise? (1-10)

Campaign Clustering

Group all ads into logical campaigns by:

• Landing page destination — Ads pointing to the same URL = same campaign
• Messaging theme — Similar copy angles = same strategic bet
• Audience signal — Different copy for different personas

Per-Campaign Funnel Analysis

For each campaign cluster:

Dimension	Analysis
Strategic intent	What is this campaign trying to achieve? (Awareness / Lead gen / Free trial / Competitive displacement)
Target persona	Who is this ad speaking to? (Role, pain, stage)
Positioning bet	What market position are they claiming?
Hook strategy	Fear / Outcome / Social proof / Contrarian / Product-led
Conversion path	Ad → LP → CTA → [Demo call / Free trial / Content download]
Longevity signal	How long has this been running? (Longer = likely working)
A/B tests detected	Multiple creatives to same LP = active testing

Budget Allocation Inference

Based on ad volume and platform distribution, estimate where they're concentrating spend:

Platform	Ad Count	% of Total	Estimated Focus
Meta (Facebook)	[N]	[X%]	[Awareness / Retargeting]
Meta (Instagram)	[N]	[X%]	[Visual / younger audience]
Google Search	[N]	[X%]	[Bottom-funnel capture]
Google Display	[N]	[X%]	[Awareness / retargeting]
YouTube	[N]	[X%]	[Education / awareness]

Phase 5: Strategic Analysis

Creative Gap Analysis

Identify across all competitors:

1. Angles nobody is running — Hook types absent from competitor ads = white space
2. Overcrowded angles — If everyone leads with "save time", avoid it or be more specific
3. Format opportunities — If no one is running video in your space, it may stand out
4. Underutilized proof — Are competitors avoiding specific proof points you could own?
5. CTA patterns to test — What CTAs do the longest-running ads use?

Vulnerability Analysis

Identify weaknesses in each competitor's ad strategy:

Vulnerability Type	Description
Message-LP mismatch	Ad promises one thing, LP delivers another
Single-persona dependency	All ads target the same persona — missing segments
Platform concentration	Heavy on one platform, absent from others
No social proof	Ads or LPs lack credibility markers
Weak CTA	Asking for too much too soon (demo before value)
Generic positioning	Claims anyone could make — not differentiated
Stale creative	Same ads running unchanged for months — fatigue risk

Historical Comparison (Deep Mode)

If Web Archive data exists for their landing pages:

• Has their positioning changed in the last 6-12 months?
• What campaigns did they retire? (Possible losers)
• What campaigns have they scaled up? (Possible winners)

Phase 6: Output

# Competitor Ad Intelligence Report — [DATE]

## Coverage
- Competitors analyzed: [list]
- Meta ads collected: [N]
- Google ads collected: [N]
- Unique landing pages analyzed: [N]
- Estimated active campaigns: [N]

---

## Executive Summary

[3-5 sentence summary: What is the competitive ad landscape? What's working? Where are the gaps and vulnerabilities?]

---

## Meta Ad Analysis

### Hook Distribution
| Hook Type | [Comp1] | [Comp2] | [Comp3] |
|-----------|---------|---------|---------|
| Fear/Loss | 40% | 10% | 0% |
| Outcome | 30% | 50% | 60% |
...

### Top Performing Ads (Longest Running)
**[Competitor] — [Ad Title/Hook]**
> [Ad copy excerpt]
- Format: [type]
- CTA: [text]
- Running since: [date]
- Why it likely works: [analysis]

---

## Google Ad Analysis

### Headline Patterns
[Top headline structures with examples]

### Most Common CTAs
[ranked list]

---

## Campaign Breakdown

### Campaign 1: [Inferred Campaign Name]
- **Competitor:** [name]
- **Ads in cluster:** [N]
- **Platform(s):** [Meta / Google / Both]
- **Strategic intent:** [Awareness / Lead gen / Competitive displacement / etc.]
- **Target persona:** [Description]
- **Hook strategy:** [Type]
- **Landing page:** [URL]
  - Hero: "[Headline text]"
  - CTA: "[Button text]"
  - Message match: [Score/10]
- **Longevity:** [First seen date → status]
- **A/B tests detected:** [Yes/No — what they're testing]

**Sample ad:**
> **Headline:** [text]
> **Body:** [text]
> **CTA:** [button]
> **Format:** [Image/Video/Carousel]

**Assessment:** [1-2 sentences — is this working? Why/why not?]

### Campaign 2: ...

---

## Funnel Map

[Ad: Hook/Angle] → [LP: /landing-page-url] → [CTA: Book Demo] ↓ [Ad: Different angle] → [LP: /same-or-different] → [CTA: Free Trial]

---

## Budget Allocation Estimate

| Platform | Share | Focus Area |
|----------|-------|-----------|
| [Platform] | [X%] | [Intent] |

---

## Creative Gap Analysis

### Angles Nobody Is Running
1. [Angle] — Why it could work for you: [reasoning]
2. [Angle] — ...

### Overcrowded Angles (Avoid or Differentiate)
- [Angle] — [N] of [N] competitors use this

### Format White Space
- [Format] is not being used by competitors on [platform]

---

## Vulnerability Report

### 1. [Vulnerability]
**Competitor:** [name]
**Evidence:** [What we observed]
**Your opportunity:** [How to exploit this gap]

### 2. ...

---

## Recommended Counter-Plays

### Counter-Play 1: [Name]
- **Target their weakness:** [Which vulnerability]
- **Your ad angle:** [Hook]
- **Platform:** [Where to run]
- **Proposed headline:** "[headline]"
- **Proposed body:** "[copy]"
- **LP strategy:** [What your landing page should emphasize]
- **Why test this:** [rationale]

### Counter-Play 2: ...

Cost

Component	Cost
Ad library research (web_search)	Free
Landing page fetching	Free
Web Archive lookup (deep mode)	Free
Analysis	Free (LLM reasoning)
Total	Free

Environment Variables

• No API keys required. This skill uses publicly accessible ad libraries and web search.

Tools Used

• web_search — query Meta Ad Library and Google Ads Transparency Center
• fetch_webpage or curl — fetch and analyze landing pages

Trigger Phrases

• "What ads are [competitor] running?"
• "Tear down [competitor]'s ad strategy"
• "Audit the ad landscape for [product category]"
• "Run ad intelligence for [competitors]"
• "Find new paid ad angles we haven't tried"
• "Reverse-engineer [competitor]'s paid funnel"
• "Find weaknesses in [competitor]'s ad strategy"
• "Deep competitive ad analysis on [competitor]"