competitor-ad-intelligence

Scrape competitor ads from Meta Ad Library and Google Ads Transparency Center, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad landscape, find new creative directions, or identify weaknesses in a competitor's paid strategy.

380 stars

Best use case

competitor-ad-intelligence is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Scrape competitor ads from Meta Ad Library and Google Ads Transparency Center, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad landscape, find new creative directions, or identify weaknesses in a competitor's paid strategy.

Teams using competitor-ad-intelligence should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/competitor-ad-intelligence/SKILL.md --create-dirs "https://raw.githubusercontent.com/gooseworks-ai/goose-skills/main/skills/composites/competitor-ad-intelligence/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/competitor-ad-intelligence/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How competitor-ad-intelligence Compares

Feature / Agentcompetitor-ad-intelligenceStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Scrape competitor ads from Meta Ad Library and Google Ads Transparency Center, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad landscape, find new creative directions, or identify weaknesses in a competitor's paid strategy.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

```markdown
# 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]"

Related Skills

competitor-monitoring-system

381
from gooseworks-ai/goose-skills

Set up and run ongoing competitive intelligence monitoring for a client. Tracks competitor content, ads, reviews, social, and product moves.

competitor-content-tracker

381
from gooseworks-ai/goose-skills

Monitor competitor content across blogs, LinkedIn, and Twitter/X on a recurring basis. Surfaces new posts, trending topics, and content gaps you can own. Chains blog-scraper, linkedin-profile-post-scraper, and twitter-scraper. Use when you want a weekly digest of what competitors are publishing and which topics are generating engagement.

competitor-ad-teardown

381
from gooseworks-ai/goose-skills

Deep-dive analysis of a competitor's ad strategy. Scrapes their Meta + Google ads, reverse-engineers their funnel (ad → landing page → CTA), identifies positioning bets, and produces a strategic teardown. Goes beyond ad-creative-intelligence by analyzing the full conversion path and strategic intent behind each campaign.

ad-creative-intelligence

381
from gooseworks-ai/goose-skills

Scrape competitor ads from Meta and Google ad libraries, cluster by hook/angle/format, and surface new creative directions your team hasn't tested. Chains meta-ad-scraper and google-ad-scraper. Use when a marketing team wants to understand the competitive ad landscape before launching a campaign, or wants fresh creative inspiration from what's already working in the market.

competitor-signals

380
from gooseworks-ai/goose-skills

Extract leads from competitor product activity — Product Hunt commenters/upvoters, HN posts about competitors, case studies, testimonials, tech press, and switching signals. Detects people actively switching from competitors as highest-priority leads.

review-intelligence-digest

380
from gooseworks-ai/goose-skills

Scrape G2, Capterra, and Trustpilot reviews for your product and competitors, then extract recurring themes, objections, proof points, and exact customer language for use in messaging. Chains review-site-scraper with LLM analysis. Produces a weekly or monthly digest that feeds directly into copywriting, positioning, and sales enablement. Use when a marketing team needs to ground messaging in real customer language.

competitor-intel

380
from gooseworks-ai/goose-skills

Competitor intelligence system. Research competitors across web, Reddit, Twitter/X, LinkedIn, and blogs. Build deep competitor profiles, monitor content and positioning changes, track what gets traction, and identify competitive gaps. Covers data collection, content tracking, and strategy analysis. Pure research skill — uses web search, web fetch, and optionally Apify for social scraping. No scripts required.

orthogonal-competitor-research

380
from gooseworks-ai/goose-skills

Research competitors - products, pricing, team, funding, and strategy

competitor-post-engagers

380
from gooseworks-ai/goose-skills

Find leads by scraping engagers from a competitor's top LinkedIn posts. Given one or more company page URLs, scrapes recent posts, ranks by engagement, selects the top N, extracts all reactors and commenters, ICP-classifies, and exports CSV. Use when someone wants to "find leads engaging with competitor content" or "scrape people who interact with [company]'s LinkedIn posts".

client-packet-engine

381
from gooseworks-ai/goose-skills

Batch client packet generator. Takes company names/URLs, runs intelligence + strategy generation, presents strategies for human selection, executes selected strategies in pitch-packet mode (no live campaigns or paid enrichment), and packages into local delivery packets.

client-package-notion

381
from gooseworks-ai/goose-skills

Package all work done for a client into a shareable Notion page with subpages and Google Sheets. Reads the client's folder (strategies, campaigns, content, leads, notes) and builds a structured Notion workspace the client can browse. Lead list CSVs are uploaded to Google Sheets and linked from the Notion pages. Use when you want to deliver work to a client in a polished, navigable format.

client-package-local

381
from gooseworks-ai/goose-skills

Package all work done for a client into a local filesystem delivery package with .md files and Google Sheets. Reads the client's folder (strategies, campaigns, content, leads, notes) and builds a structured directory with dated deliverables. Lead lists are uploaded to Google Sheets and linked from the markdown files. Use when you want to deliver work to a client in a polished, navigable format without requiring Notion.