github/awesome-copilot

competitor-ad-intelligence

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.

92Collecting
See how to use itView GitHub source
npx skills add https://github.com/github/awesome-copilot --skill "skills/competitor-ad-intelligence"
Automated source guide

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Turn competitor-ad-intelligence's source instructions into a guide you can follow

According to the pinned SKILL.md from github/awesome-copilot: 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.

npx skills add https://github.com/github/awesome-copilot --skill "skills/competitor-ad-intelligence"
Check the pinned source

Best fit

  • "What ads are my competitors running?"
  • "Tear down [competitor]'s ad strategy"
  • "Find new creative angles for our paid campaigns"

Bring this context

  • A concrete task that matches the documented purpose of competitor-ad-intelligence.
  • The files, examples, or context the task depends on.
  • Your constraints, target environment, and definition of done.

Expected outputs

  • Competitors analyzed: [list]
  • Meta ads collected: [N]
  • Google ads collected: [N]

Key source sections

Read competitor-ad-intelligence through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

Phase 0: Intake

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…

SKILL.md · Phase 0: Intake
Competitor names + domains (e.g., apollo.io, clay.run)Your product/domain — for comparison framingChannels: Meta only, Google only, or both? (default: both)
02

Phase 1: Scrape Meta Ads

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

SKILL.md · Phase 1: Scrape Meta Ads
Ad copy (headline + primary text)Visual type (image / video / carousel)CTA button text
03

Phase 2: Scrape Google Ads

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

SKILL.md · Phase 2: Scrape Google Ads
Headline variants (up to 3)Description linesAd type (Search / Display / YouTube / Shopping)
04

Phase 3: Analyze Creative Patterns

After collecting all ads, perform structured analysis.

SKILL.md · Phase 3: Analyze Creative 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"
05

Phase 4: Landing Page & Funnel Analysis

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

SKILL.md · Phase 4: Landing Page & Funnel Analysis
Hero headline — Does it match the ad promise?Subheadline — Value prop expansionPrimary CTA — What action are they driving? (Demo / Free trial / Sign up / Download)

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Task-start prompt

Confirm source fit, inputs, and outputs before acting.

Use competitor-ad-intelligence to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.

Source-guided execution

Make the Agent explicitly follow the key extracted sections.

Apply the pinned competitor-ad-intelligence source to [task]. Pay particular attention to these source sections: “Phase 0: Intake”, “Phase 1: Scrape Meta Ads”, “Phase 2: Scrape Google Ads”, “Phase 3: Analyze Creative Patterns”, “Phase 4: Landing Page & Funnel Analysis”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].

Result-review prompt

Check omissions, permissions, and source drift before delivery.

Review the current competitor-ad-intelligence result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.

Output checklist

Verify each item before delivery

The task matches the purpose documented in the SKILL.md.

The source section “Phase 0: Intake” has been checked.

The source section “Phase 1: Scrape Meta Ads” has been checked.

The source section “Phase 2: Scrape Google Ads” has been checked.

The source section “Phase 3: Analyze Creative Patterns” has been checked.

Inputs, constraints, and acceptance criteria are explicit.

Unverified facts, compatibility, and outcome claims are clearly marked.

Any file, command, network, or data action has been reviewed.

Choose a different workflow

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Open source detail

FAQ

What does competitor-ad-intelligence do?

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.

How do I start using competitor-ad-intelligence?

The catalog detected this source-specific install command: npx skills add https://github.com/github/awesome-copilot --skill "skills/competitor-ad-intelligence". Inspect the command and pinned source before running it.

Which Agent platforms does it declare?

No dedicated Agent platform is declared in the pinned source record.

Repository stars
37,126
Repository forks
4,654
Quality
92/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

92/100
Documentation30/30
Specificity25/25
Maintenance20/20
Trust signals17/25

Compare before choosing

Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

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View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 6 min

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 TypePatternExample
Fear/LossRisk of missing out or falling behind"Your competitors are already using AI SDRs"
OutcomeDirect result promise"10x your pipeline in 30 days"
QuestionChallenges current assumption"Still doing outbound manually?"
Social proofNames customers or numbers"Join 500+ B2B teams using [product]"
ContrarianChallenges conventional wisdom"Cold email isn't dead. Your copy is."
EmpathyValidates their pain"We know SDR ramp time is brutal"
Product-ledFeature 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

FormatMetaGoogle
Static image[N]N/A
Video[N][N]
Carousel[N]N/A
Search textN/A[N]
Display bannerN/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:

DimensionAnalysis
Strategic intentWhat is this campaign trying to achieve? (Awareness / Lead gen / Free trial / Competitive displacement)
Target personaWho is this ad speaking to? (Role, pain, stage)
Positioning betWhat market position are they claiming?
Hook strategyFear / Outcome / Social proof / Contrarian / Product-led
Conversion pathAd → LP → CTA → [Demo call / Free trial / Content download]
Longevity signalHow long has this been running? (Longer = likely working)
A/B tests detectedMultiple creatives to same LP = active testing

Budget Allocation Inference

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

PlatformAd Count% of TotalEstimated 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 TypeDescription
Message-LP mismatchAd promises one thing, LP delivers another
Single-persona dependencyAll ads target the same persona — missing segments
Platform concentrationHeavy on one platform, absent from others
No social proofAds or LPs lack credibility markers
Weak CTAAsking for too much too soon (demo before value)
Generic positioningClaims anyone could make — not differentiated
Stale creativeSame 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

ComponentCost
Ad library research (web_search)Free
Landing page fetchingFree
Web Archive lookup (deep mode)Free
AnalysisFree (LLM reasoning)
TotalFree

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]"
Skill path
skills/competitor-ad-intelligence/SKILL.md
Commit SHA
9933dcad5be5
Repository license
MIT
Data collected