Analysez et effectuez une rétro-ingénierie des stratégies publicitaires payantes de vos concurrents sur Meta et Google afin d'identifier les lacunes créatives et les vulnérabilités de leurs tunnels de conversion.
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- Fichiers
- 1
- Dernier commit
- 11 nov. 2026
- Licence
- MIT
- Outils autorisés
- —
Que fait la skill competitor-ad-intelligence ?
competitor-ad-intelligence est une agent skill. Analysez et effectuez une rétro-ingénierie des stratégies publicitaires payantes de vos concurrents sur Meta et Google afin d'identifier les lacunes créatives et les vulnérabilités de leurs tunnels de conversion.
Résumé généré à partir du SKILL.md de la skill.
À propos du skill competitor-ad-intelligence
Les équipes marketing et les stratèges de croissance utilisent cette SKILL pour déconstruire les tactiques publicitaires payantes de leurs rivaux. En analysant les campagnes actives sur Meta et Google, l'outil identifie quels accroches de message, formats visuels et modèles d'appel à l'action (CTA) génèrent actuellement des résultats sur un marché spécifique. Il va au-delà du simple scraping pour fournir une analyse structurée de la manière dont les concurrents relient leurs publicités à des pages de destination et des tunnels de conversion spécifiques.
Un agent doit charger cette SKILL lorsqu'un utilisateur a besoin de trouver des lacunes créatives ou de comprendre le positionnement d'un concurrent. Elle s'appuie sur la recherche web pour accéder à des ressources publiques comme la Meta Ad Library et le Google Ads Transparency Center, ainsi que sur la récupération de pages web pour auditer l'efficacité des landing pages. Le processus produit un rapport stratégique complet incluant la distribution des accroches, le clustering de campagnes, des estimations d'allocation budgétaire et des évaluations de vulnérabilités spécifiques pour aider les utilisateurs à élaborer des contre-stratégies.
Ce qu'un agent peut faire avec
- 1Rétro-ingénierie des tunnels de conversion concurrentielsAnalysez comment un concurrent dirige les utilisateurs d'une accroche publicitaire spécifique vers l'appel à l'action final de sa landing page.
- 2Identification d'espaces créatifs inexploitésTrouvez des angles publicitaires et des formats qu'aucun de vos concurrents n'utilise actuellement dans votre catégorie.
- 3Audit de la stratégie de message des concurrentsDéterminez si vos rivaux se concentrent sur la peur, la preuve sociale ou des accroches basées sur les résultats pour attirer les clients.
- 4Détection de vulnérabilités stratégiquesIdentifiez des faiblesses telles que des incohérences entre le message et la landing page ou une dépendance excessive à une seule plateforme.
Rédigé à partir du SKILL.md du skill. Chaque cas d'utilisation est vérifié par rapport à une citation de celui-ci.
Comment installer la skill competitor-ad-intelligence
Chaque commande copie le dossier de la skill dans le répertoire des skills du client. Redémarrez le client ou démarrez une nouvelle session pour la charger.Installer la skill competitor-ad-intelligence dans Claude Code
~/.claude/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.claude/skills/competitor-ad-intelligenceInstaller la skill competitor-ad-intelligence dans Claude Code (project)
.claude/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence .claude/skills/competitor-ad-intelligenceInstaller la skill competitor-ad-intelligence dans Codex
~/.agents/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.agents/skills/competitor-ad-intelligenceInstaller la skill competitor-ad-intelligence dans Gemini CLI
~/.gemini/skills/competitor-ad-intelligence/SKILL.md
$ npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.gemini/skills/competitor-ad-intelligenceInstaller la skill competitor-ad-intelligence dans Claude.ai
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
Vérifications automatisées
Exécuté le 15 nov. 2026Rapport complet →A réussi nos vérifications automatiques. Ce n'est pas un audit de sécurité : lisez les scripts avant de laisser un agent les exécuter. Fonctionnement des vérifications
- Dépôt source lié et accessiblegithub/awesome-copilot/skills/competitor-ad-intelligence is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/competito ↗Réussi
- Licence open source (OSI)MITspdx.org/licenses/MIT.html ↗Réussi
- Commit ou version publiée au cours de l'année écouléeLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Réussi
- Aucun téléchargement-exécution ni vol d'identifiants dans les scriptsNo scripts in the skill folderRéussi
- Aucune instruction cachée dans SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗Réussi
- Les outils préautorisés sont limitésNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗Réussi
- SKILL.md respecte le format Agent SkillsValid name and description; says when to use it; 378 linesgithub.com/github/awesome-copilot/blob/HEAD/skills/competito ↗Réussi
SKILL.md
Voir sur GitHubCompetitor 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]"
Fichiers de cette skill
1Questions sur la skill competitor-ad-intelligence
Comment installer la skill competitor-ad-intelligence dans Claude Code ?
Exécutez npx degit github/awesome-copilot/skills/competitor-ad-intelligence ~/.claude/skills/competitor-ad-intelligence. Claude Code charge les skills depuis ~/.claude/skills (tous les projets) ou .claude/skills (un seul projet) et utilise une skill lorsque sa description correspond à la tâche.
Quels clients peuvent utiliser la skill competitor-ad-intelligence ?
Tout client qui lit le format Agent Skills : Claude Code, Codex, Gemini CLI et claude.ai (envoi sous forme de ZIP). Les scripts de la skill ne s'exécutent que si le client autorise l'exécution de code.
Quelles plateformes cette SKILL peut-elle analyser ?
Cette SKILL se concentre sur le scraping de publicités provenant de Meta (Facebook et Instagram) et de Google (Search, Display, YouTube et Shopping) en utilisant les bibliothèques publicitaires publiques.
Ai-je besoin de clés API pour utiliser cette SKILL ?
Non, la SKILL utilise la recherche web et les bibliothèques publicitaires publiques telles que la Meta Ad Library et le Google Ads Transparency Center, qui ne nécessitent pas d'identifiants.