Meta와 Google의 경쟁사 유료 광고 전략을 분석하고 역설계하여, 크리에이티브의 공백과 퍼널의 취약점을 식별합니다.
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- 2026년 11월 11일
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competitor-ad-intelligence skill은 무엇을 하나요?
competitor-ad-intelligence은(는) agent skill입니다. Meta와 Google의 경쟁사 유료 광고 전략을 분석하고 역설계하여, 크리에이티브의 공백과 퍼널의 취약점을 식별합니다.
skill의 SKILL.md에서 생성된 요약입니다.
competitor-ad-intelligence skill 정보
마케팅 팀과 그로스 전략가는 경쟁사의 유료 광고 전술을 해체하기 위해 이 SKILL을 사용합니다. Meta와 Google에서 실행 중인 캠페인을 분석함으로써, 특정 시장에서 현재 어떤 메시지 훅, 비주얼 포맷, CTA 패턴이 성과를 내고 있는지 식별합니다. 단순한 스크래핑을 넘어 경쟁사가 광고를 특정 랜딩 페이지 및 전환 퍼널과 어떻게 연결하는지에 대한 구조적인 분석을 제공합니다.
사용자가 크리에이티브의 공백을 찾거나 경쟁사의 포지셔닝을 이해해야 할 때 에이전트는 이 SKILL을 로드해야 합니다. 이 SKILL은 웹 검색을 통해 Meta Ad Library 및 Google Ads Transparency Center와 같은 공개 리소스에 접근하며, 웹페이지 페칭을 통해 랜딩 페이지의 효과를 감사합니다. 이 프로세스는 훅 분포, 캠페인 클러스터링, 예산 할당 추정치 및 구체적인 취약점 평가를 포함하는 종합적인 전략 보고서를 생성하여 사용자가 대응 전략을 수립할 수 있도록 돕습니다.
agent가 수행할 수 있는 작업
- 1경쟁사 퍼널 역설계경쟁사가 특정 광고 훅에서 최종 랜딩 페이지의 CTA까지 사용자를 어떻게 이동시키는지 분석합니다.
- 2크리에이티브 화이트 스페이스 식별현재 카테고리 내에서 경쟁사들이 전혀 활용하지 않고 있는 광고 각도와 포맷을 찾아냅니다.
- 3경쟁사 메시지 전략 감사경쟁사가 고객을 유인하기 위해 공포, 사회적 증거, 또는 결과 기반 훅 중 무엇에 집중하고 있는지 파악합니다.
- 4전략적 취약점 탐지메시지와 랜딩 페이지 간의 불일치 또는 단일 플랫폼에 대한 과도한 의존성과 같은 약점을 찾아냅니다.
skill의 SKILL.md를 기반으로 작성되었습니다. 모든 사용 사례는 해당 문서의 인용구와 대조 확인되었습니다.
competitor-ad-intelligence skill 설치 방법
각 명령은 skill 폴더를 클라이언트의 skills 디렉터리에 복사합니다. 클라이언트를 재시작하거나 새 세션을 시작하여 로드하세요.Claude Code에 competitor-ad-intelligence skill 설치
~/.claude/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.claude/skills/competitor-ad-intelligenceClaude Code (project)에 competitor-ad-intelligence skill 설치
.claude/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence .claude/skills/competitor-ad-intelligenceCodex에 competitor-ad-intelligence skill 설치
~/.agents/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.agents/skills/competitor-ad-intelligenceGemini CLI에 competitor-ad-intelligence skill 설치
~/.gemini/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.gemini/skills/competitor-ad-intelligenceClaude.ai에 competitor-ad-intelligence skill 설치
npx degit github/awesome-copilot/skills/competitor-ad-intelligence competitor-ad-intelligence && zip -r competitor-ad-intelligence.zip competitor-ad-intelligence # claude.ai → Customize → Skills → + → Create skill → Upload a skill → competitor-ad-intelligence.zip
자동 검사
2026년 11월 15일에 실행됨전체 보고서 →자동 검사를 통과했습니다. 보안 감사는 아닙니다. 에이전트가 실행하기 전에 스크립트를 읽어 보세요. 검사 방식
- 소스 저장소 연결 및 접근 가능github/awesome-copilot/skills/competitor-ad-intelligence is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/competito ↗통과
- 오픈 소스 라이선스 (OSI)MITspdx.org/licenses/MIT.html ↗통과
- 최근 1년 내 커밋 또는 릴리스Last commit 2026-11-11github.com/github/awesome-copilot/commits ↗통과
- 스크립트에 다운로드 후 실행이나 자격 증명 탈취가 없음No scripts in the skill folder통과
- SKILL.md에 숨겨진 지침 없음No override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗통과
- 사전 허용 도구가 제한됨No pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗통과
- SKILL.md가 Agent Skills 형식을 따름Valid name and description; says when to use it; 378 linesgithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗통과
SKILL.md
GitHub에서 보기Competitor 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:
- Competitor names + domains (e.g.,
apollo.io,clay.run) - Your product/domain — for comparison framing
- Channels: Meta only, Google only, or both? (default: both)
- Depth level:
- Standard: Ad scrape + creative analysis + landing page analysis
- Deep: Standard + historical comparison + funnel reconstruction + counter-plays
- Product category — helps frame analysis
- 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_searchas 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 | |
|---|---|---|
| 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:
- Angles nobody is running — Hook types absent from competitor ads = white space
- Overcrowded angles — If everyone leads with "save time", avoid it or be more specific
- Format opportunities — If no one is running video in your space, it may stand out
- Underutilized proof — Are competitors avoiding specific proof points you could own?
- 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 Centerfetch_webpageorcurl— 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]"
이 스킬의 파일
1competitor-ad-intelligence skill 질문
Claude Code에 competitor-ad-intelligence skill을 어떻게 설치하나요?
npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.claude/skills/competitor-ad-intelligence를 실행합니다. Claude Code는 ~/.claude/skills(모든 프로젝트) 또는 .claude/skills(한 프로젝트)에서 skills를 로드하며, 설명이 작업과 일치할 때 skill을 사용합니다.
competitor-ad-intelligence skill을 사용할 수 있는 클라이언트는 무엇인가요?
Agent Skills 형식을 읽는 모든 클라이언트(Claude Code, Codex, Gemini CLI, claude.ai(ZIP으로 업로드))가 사용할 수 있습니다. skill 내 스크립트는 클라이언트가 코드 실행을 허용하는 경우에만 실행됩니다.
어떤 플랫폼을 분석할 수 있나요?
공개 광고 라이브러리를 사용하여 Meta(Facebook 및 Instagram)와 Google(검색, 디스플레이, YouTube, 쇼핑)의 광고를 스크래핑하는 데 집중합니다.
이 SKILL을 사용하려면 API 키가 필요한가요?
아니요, 이 SKILL은 인증이 필요 없는 Meta Ad Library 및 Google Ads Transparency Center와 같은 웹 검색 및 공개 광고 라이브러리를 사용합니다.