- name
- claude-marketing-skills
- description
- Full marketing department for Claude Code—56 skills, agents, and workflows for paid media, e-commerce, SEO, content, analytics, and strategy.
- triggers
- ["install claude marketing skills","set up marketing department for Claude","audit my Google Ads account","analyze Klaviyo email flows","optimize landing page conversion","run cross-platform ad audit","create SEO content strategy","generate schema markup for product pages"]
# claude-marketing-skills
> Skill by [ara.so](https://ara.so) — Marketing Skills collection.
**claude-marketing** is a collection of 56 open-source Claude Code skills, specialized agents, and autonomous workflows that give AI coding agents real marketing expertise. Each skill ships with diagnostic frameworks, industry benchmarks, audit checklists, and platform-specific reference data across paid media, SEO, e-commerce, content, CRO, analytics, and strategy.
Works with Claude Code, Cursor, Aider, Windsurf, GitHub Copilot, and Gemini CLI.
## Installation
### Clone the Repository
```bash
git clone https://github.com/thatrebeccarae/claude-marketing.git
cd claude-marketing
```
### Install Individual Skills
Copy any skill to your Claude Code skills directory:
```bash
# Example: Install Google Ads skill
cp -r skills/google-ads ~/.claude/skills/
# Example: Install Klaviyo Analyst skill
cp -r skills/klaviyo-analyst ~/.claude/skills/
# Example: Install AEO/GEO Optimizer
cp -r skills/aeo-geo-optimizer ~/.claude/skills/
```
### Install Skill Packs
Use setup scripts to install related skill groups:
```bash
# Paid Media Pack (Google Ads, Meta, Microsoft, LinkedIn, TikTok, cross-platform)
python skill-packs/scripts/setup-paid-media.py
# E-commerce Pack (Shopify, Klaviyo, product feeds, conversion tracking)
python skill-packs/scripts/setup-ecommerce.py
# Analytics Pack (GA4, Looker Studio, GTM, data visualization)
python skill-packs/scripts/setup-analytics.py
# SEO Pack (Technical SEO, AEO/GEO, schema, programmatic SEO)
python skill-packs/scripts/setup-seo.py
# Content Pack (content creation, brand voice, social strategy)
python skill-packs/scripts/setup-content.py
# Strategy Pack (market research, ICP, brand DNA, pricing)
python skill-packs/scripts/setup-strategy.py
```
## Project Structure
```
claude-marketing/
├── skills/ # 56 individual skills
│ ├── google-ads/
│ │ ├── SKILL.md # Framework and decision trees
│ │ ├── REFERENCE.md # Benchmarks, API schemas, rate limits
│ │ └── EXAMPLES.md # Worked prompts with output
│ ├── klaviyo-analyst/
│ ├── aeo-geo-optimizer/
│ └── ...
├── skill-packs/ # Grouped collections with setup wizards
│ ├── paid-media/
│ ├── ecommerce/
│ ├── analytics/
│ └── scripts/
├── agents/ # Standalone analysis agents
│ ├── competitor-analyst/
│ ├── content-strategist/
│ └── paid-media-auditor/
├── workflows/ # n8n autonomous pipelines
│ └── daily-performance-digest/
└── examples/ # Cross-skill workflow walkthroughs
```
## Skill Anatomy
Each skill contains three layers:
1. **SKILL.md** — Frameworks, decision trees, diagnostic checklists
2. **REFERENCE.md** — Industry benchmarks, API schemas, rate limits, platform-specific data
3. **EXAMPLES.md** — Worked prompts with expected output
## Key Skills by Category
### Paid Media
**google-ads** — Scored account audits (74 checks, A-F health grade), Quality Score optimization, Performance Max, wasted spend identification.
```bash
# Install
cp -r skills/google-ads ~/.claude/skills/
# Example usage
# "Run a scored audit of my Google Ads account"
# "Analyze Quality Score distribution and recommend optimizations"
# "Find wasted spend in search campaigns"
```
**meta-ads** — Scored audits (46 checks), creative fatigue diagnosis, pixel/CAPI health, iOS 14.5+ attribution.
**cross-platform-audit** — Unified multi-platform audit across Google, Meta, Microsoft with budget-weighted aggregate health score.
### E-commerce
**klaviyo-analyst** (MCP-first) — Full Klaviyo audit: 4-phase account review, flow gap analysis, segment health, deliverability diagnostics.
```bash
# Install
cp -r skills/klaviyo-analyst ~/.claude/skills/
# Requires Klaviyo MCP server
# Install: npx -y @klaviyo/mcp-server-klaviyo
# Example usage
# "Audit my Klaviyo flows and identify gaps"
# "Analyze email deliverability and segment health"
# "Generate revenue attribution report for Q1"
```
**shopify-analyst** — Store health audits, checkout optimization, product feed validation, app conflict detection.
### SEO & AI Search
**aeo-geo-optimizer** — AI search optimization for ChatGPT, Perplexity, Google AI Overviews. Content scoring, citation patterns, AI crawler management.
```bash
# Install
cp -r skills/aeo-geo-optimizer ~/.claude/skills/
# Example usage
# "Optimize this page for AI search citations"
# "Audit content for ChatGPT and Perplexity visibility"
# "Generate llms.txt for my documentation site"
```
**technical-seo-audit** — Deep crawl analysis, Core Web Vitals, indexation health, structured data validation.
**schema-markup-generator** — JSON-LD structured data for Article, FAQ, HowTo, Product, Review, LocalBusiness.
```python
# Example: Generate product schema
from skills.schema_markup_generator import generate_schema
product_data = {
"name": "Organic Cotton T-Shirt",
"description": "100% organic cotton, fair trade certified",
"price": 29.99,
"currency": "USD",
"availability": "InStock",
"brand": "EcoWear",
"image": "https://example.com/images/tshirt.jpg",
"sku": "ECO-TS-001",
"gtin": "00012345678905"
}
schema_json = generate_schema("Product", product_data)
print(schema_json)
```
### Content & Strategy
**brand-dna** — Extract brand identity from URL: voice, colors, typography, imagery, values → `brand-profile.json`.
```bash
# Install
cp -r skills/brand-dna ~/.claude/skills/
# Example usage
# "Extract brand DNA from https://example.com"
# "Analyze brand voice and create brand-profile.json"
```
**content-creator** — Brand voice analysis, SEO optimization, content calendar planning, multi-platform strategy.
**market-research** — Consulting-grade reports (50+ pages): Porter's Five Forces, PESTLE, SWOT, TAM/SAM/SOM.
### Conversion & Growth
**cro-auditor** — CRO audits using LIFT model, ICE/PIE prioritization, A/B test hypothesis generation.
```bash
# Install
cp -r skills/cro-auditor ~/.claude/skills/
# Example usage
# "Run a CRO audit on my landing page"
# "Generate A/B test hypotheses prioritized by ICE score"
# "Analyze form drop-off and recommend fixes"
```
**landing-page-optimizer** — Page audit: above-the-fold, value props, CTAs, social proof, mobile optimization.
## Configuration
### Environment Variables
Skills that connect to external platforms require API credentials:
```bash
# Google Ads
export GOOGLE_ADS_DEVELOPER_TOKEN=your_dev_token
export GOOGLE_ADS_CLIENT_ID=your_client_id
export GOOGLE_ADS_CLIENT_SECRET=your_client_secret
export GOOGLE_ADS_REFRESH_TOKEN=your_refresh_token
# Meta Ads
export META_ACCESS_TOKEN=your_access_token
export META_AD_ACCOUNT_ID=act_1234567890
# Klaviyo
export KLAVIYO_API_KEY=your_api_key
export KLAVIYO_PRIVATE_KEY=your_private_key
# Google Analytics 4
export GA4_PROPERTY_ID=12345678
export GA4_CREDENTIALS_PATH=/path/to/service-account.json
# Shopify
export SHOPIFY_STORE_URL=your-store.myshopify.com
export SHOPIFY_ACCESS_TOKEN=your_access_token
```
### MCP Server Configuration
For MCP-enabled skills (Klaviyo, Google Ads, Shopify):
```json
// ~/.claude/mcp_settings.json
{
"mcpServers": {
"klaviyo": {
"command": "npx",
"args": ["-y", "@klaviyo/mcp-server-klaviyo"],
"env": {
"KLAVIYO_API_KEY": "${KLAVIYO_API_KEY}"
}
},
"google-ads": {
"command": "npx",
"args": ["-y", "@google-ads/mcp-server"],
"env": {
"GOOGLE_ADS_DEVELOPER_TOKEN": "${GOOGLE_ADS_DEVELOPER_TOKEN}",
"GOOGLE_ADS_CLIENT_ID": "${GOOGLE_ADS_CLIENT_ID}",
"GOOGLE_ADS_CLIENT_SECRET": "${GOOGLE_ADS_CLIENT_SECRET}",
"GOOGLE_ADS_REFRESH_TOKEN": "${GOOGLE_ADS_REFRESH_TOKEN}"
}
}
}
}
```
## Common Workflows
### Cross-Platform Paid Media Audit
```bash
# Install required skills
python skill-packs/scripts/setup-paid-media.py
# Run audit
# "Run a cross-platform audit of my Google Ads, Meta, and Microsoft Ads accounts"
# Output: Budget-weighted aggregate health score (A-F), platform-specific recommendations
```
### E-commerce Email Audit
```bash
# Install Klaviyo skill
cp -r skills/klaviyo-analyst ~/.claude/skills/
# Run audit
# "Audit my Klaviyo account and identify flow gaps"
# "Compare my email performance to industry benchmarks"
# "Generate revenue attribution report for abandoned cart flows"
```
### AI Search Optimization
```bash
# Install AEO/GEO skill
cp -r skills/aeo-geo-optimizer ~/.claude/skills/
# Optimize content
# "Audit this blog post for AI search citations"
# "Generate llms.txt for my developer documentation"
# "Optimize product pages for ChatGPT and Perplexity visibility"
```
### Landing Page Conversion Audit
```bash
# Install CRO skills
cp -r skills/cro-auditor ~/.claude/skills/
cp -r skills/landing-page-optimizer ~/.claude/skills/
# Run audit
# "Run a CRO audit on https://example.com/landing-page"
# "Prioritize conversion optimizations using ICE scoring"
# "Generate A/B test hypotheses for the hero section"
```
## Working with Agents
Agents are standalone analysis tools that can run independently or within n8n workflows.
### Competitor Analyst Agent
```python
# agents/competitor-analyst/competitor_analyst.py
from competitor_analyst import CompetitorAnalyst
analyst = CompetitorAnalyst()
# Analyze competitor ads from public ad libraries
report = analyst.analyze_competitor(
competitor_domain="competitor.com",
platforms=["google", "meta", "linkedin"],
date_range="last_90_days"
)
print(report.messaging_patterns)
print(report.creative_formats)
print(report.positioning_gaps)
```
### Content Strategist Agent
```python
# agents/content-strategist/content_strategist.py
from content_strategist import ContentStrategist
strategist = ContentStrategist()
# Generate content strategy from research feed
strategy = strategist.generate_strategy(
topic="AI search optimization",
sources=["rss_feeds", "web_scraping"],
output_format="notion"
)
print(strategy.content_angles)
print(strategy.keyword_clusters)
print(strategy.content_calendar)
```
## Autonomous Workflows
### Daily Performance Digest (n8n)
```bash
# Install workflow
cp -r workflows/daily-performance-digest ~/.n8n/workflows/
# Configure in n8n:
# 1. Set schedule trigger (daily at 8am)
# 2. Add Slack webhook for notifications
# 3. Configure platform credentials (Google Ads, Meta, GA4)
# 4. Activate workflow
```
**What it does:**
- Pulls performance data from Google Ads, Meta, GA4
- Runs anomaly detection on key metrics
- Generates Looker Studio snapshot
- Posts digest to Slack with priority alerts
## Code Examples
### Generate Schema Markup
```python
from skills.schema_markup_generator import SchemaGenerator
generator = SchemaGenerator()
# Product schema
product_schema = generator.generate(
schema_type="Product",
data={
"name": "Wireless Headphones",
"description": "Noise-cancelling wireless headphones",
"price": 199.99,
"currency": "USD",
"availability": "InStock",
"brand": "AudioCo",
"aggregateRating": {
"ratingValue": 4.5,
"reviewCount": 127
}
}
)
print(product_schema)
```
### Run Google Ads Audit
```python
from skills.google_ads import GoogleAdsAuditor
auditor = GoogleAdsAuditor(
developer_token=os.getenv("GOOGLE_ADS_DEVELOPER_TOKEN"),
client_id=os.getenv("GOOGLE_ADS_CLIENT_ID"),
client_secret=os.getenv("GOOGLE_ADS_CLIENT_SECRET"),
refresh_token=os.getenv("GOOGLE_ADS_REFRESH_TOKEN")
)
# Run scored audit (74 checks)
audit_report = auditor.run_audit(customer_id="1234567890")
print(f"Overall Health Grade: {audit_report.health_grade}")
print(f"Score: {audit_report.score}/100")
print("\nTop Issues:")
for issue in audit_report.top_issues:
print(f"- {issue.title} (Impact: {issue.impact})")
```
### Extract Brand DNA
```python
from skills.brand_dna import BrandDNAExtractor
extractor = BrandDNAExtractor()
# Extract from URL
brand_profile = extractor.extract(url="https://example.com")
# Save to JSON
brand_profile.save("brand-profile.json")
print(f"Brand Voice: {brand_profile.voice.tone}")
print(f"Primary Colors: {brand_profile.colors.primary}")
print(f"Target Audience: {brand_profile.audience.demographics}")
```
### Optimize for AI Search
```python
from skills.aeo_geo_optimizer import AEOOptimizer
optimizer = AEOOptimizer()
# Analyze content
content = """
Your blog post or documentation content here...
"""
analysis = optimizer.analyze(content)
print(f"AI Search Score: {analysis.score}/100")
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