Analysiere und dekonstruiere die Paid-Advertising-Strategien deiner Wettbewerber auf Meta und Google, um kreative Lücken und Schwachstellen in deren Funnels zu finden.
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- 11. Nov. 2026
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Was macht der competitor-ad-intelligence Skill?
competitor-ad-intelligence ist ein Agent Skill. Analysiere und dekonstruiere die Paid-Advertising-Strategien deiner Wettbewerber auf Meta und Google, um kreative Lücken und Schwachstellen in deren Funnels zu finden.
Zusammenfassung erzeugt aus der SKILL.md des Skills.
Über den competitor-ad-intelligence skill
Marketing-Teams und Growth-Strategen nutzen diesen Skill, um die Paid-Advertising-Taktiken ihrer Rivalen zu dekonstruieren. Durch die Analyse aktiver Kampagnen auf Meta und Google identifiziert das Tool, welche Messaging-Hooks, visuellen Formate und Call-to-Action-Muster in einem bestimmten Markt aktuell Ergebnisse liefern. Es geht über einfaches Scraping hinaus und bietet ein strukturiertes Teardown davon, wie Wettbewerber ihre Ads mit spezifischen Landingpages und Conversion-Funnels verknüpfen.
Ein Agent sollte diesen Skill laden, wenn ein User kreative Lücken finden oder die Positionierung eines Wettbewerbers verstehen möchte. Er nutzt Web Search, um auf öffentliche Ressourcen wie die Meta Ad Library und das Google Ads Transparency Center zuzugreifen, kombiniert mit Webpage Fetching, um die Effektivität von Landingpages zu auditieren. Der Prozess liefert einen umfassenden strategischen Report, der Hook-Verteilungen, Campaign-Clustering, Schätzungen zur Budgetallokation und spezifische Schwachstellenanalysen enthält, um dir bei der Entwicklung von Gegenstrategien zu helfen.
Was ein agent damit machen kann
- 1Wettbewerber-Funnels reverse-engineerenAnalysiere, wie ein Wettbewerber Nutzer von einem spezifischen Ad-Hook zu seinem finalen Call-to-Action auf der Landingpage führt.
- 2Kreative White Spaces identifizierenFinde Werbe-Angles und Formate, die aktuell keiner deiner Wettbewerber in deiner Kategorie nutzt.
- 3Messaging-Strategie der Konkurrenz auditierenFinde heraus, ob Rivalen auf Fear, Social Proof oder ergebnisbasierte Hooks setzen, um Kunden zu gewinnen.
- 4Strategische Schwachstellen erkennenIdentifiziere Schwachstellen wie Mismatches zwischen Message und Landingpage oder eine zu starke Abhängigkeit von einer einzelnen Plattform.
Geschrieben basierend auf dem SKILL.md des skills. Jeder use case wurde gegen ein zitat daraus geprüft.
So installierst du den competitor-ad-intelligence Skill
Jeder Befehl kopiert den Skill-Ordner in das Skills-Verzeichnis des Clients. Starte den Client neu oder beginne eine neue Session, um ihn zu laden.Den competitor-ad-intelligence Skill in Claude Code installieren
~/.claude/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.claude/skills/competitor-ad-intelligenceDen competitor-ad-intelligence Skill in Claude Code (project) installieren
.claude/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence .claude/skills/competitor-ad-intelligenceDen competitor-ad-intelligence Skill in Codex installieren
~/.agents/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.agents/skills/competitor-ad-intelligenceDen competitor-ad-intelligence Skill in Gemini CLI installieren
~/.gemini/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.gemini/skills/competitor-ad-intelligenceDen competitor-ad-intelligence Skill in Claude.ai installieren
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
Automatisierte Checks
Geluften am 15. Nov. 2026Vollständiger Bericht →Hat unsere automatischen Prüfungen bestanden. Das ist kein Sicherheitsaudit: Lesen Sie die Skripte, bevor ein Agent sie ausführt. Wie die Checks funktionieren
- Source Repository verknüpft und erreichbargithub/awesome-copilot/skills/competitor-ad-intelligence is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/competito ↗Bestanden
- Open Source Lizenz (OSI)MITspdx.org/licenses/MIT.html ↗Bestanden
- Commit oder Release im letzten JahrLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Bestanden
- Keine Download-und-Ausführen-Muster oder Zugangsdatendiebstahl in SkriptenNo scripts in the skill folderBestanden
- Keine versteckten Anweisungen in SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗Bestanden
- Vorab erlaubte Tools sind eingeschränktNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗Bestanden
- SKILL.md folgt dem Agent-Skills-FormatValid name and description; says when to use it; 378 linesgithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗Bestanden
SKILL.md
Auf GitHub ansehenCompetitor 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]"
Dateien in diesem Skill
1competitor-ad-intelligence Skill-Fragen
Wie installiere ich den competitor-ad-intelligence Skill in Claude Code?
Führe npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.claude/skills/competitor-ad-intelligence aus. Claude Code lädt Skills aus ~/.claude/skills (alle Projekte) oder .claude/skills (ein Projekt) und nutzt einen Skill, wenn seine Beschreibung zur Aufgabe passt.
Welche Clients können den competitor-ad-intelligence Skill nutzen?
Jeder Client, der das Agent Skills-Format liest: Claude Code, Codex, Gemini CLI und claude.ai (als ZIP hochladen). Skripte im Skill laufen nur dort, wo der Client Codeausführung erlaubt.
Welche Plattformen kann dieser Skill analysieren?
Der Skill konzentriert sich darauf, Ads von Meta (Facebook und Instagram) und Google (Search, Display, YouTube und Shopping) über öffentliche Ad Libraries zu scrapen.
Benötige ich API-Keys, um diesen Skill zu nutzen?
Nein, der Skill nutzt Web Search und öffentliche Ad Libraries wie die Meta Ad Library und das Google Ads Transparency Center, die keine Credentials erfordern.