ai-marketing-skills-automation

# AI Marketing Skills Automation > Skill by [ara.so](https://ara.so) — Marketing Skills collection. This project provides **battle-tested marketing automation workflows** — not prompts, but complete Python scripts with scoring algorithms, expert panels, and automation pipelines. Built for Claude Code and other AI coding agents to execute real marketing operations. ## What It Does AI Marketing Skills gives you 15+ categories of marketing automation: - **Growth Engine**: Autonomous experiments with statistical testing - **Sales Pipeline**: Website visitor → qualified pipeline automation - **Content Ops**: Quality scoring and production workflows - **Outbound Engine**: ICP definition to cold emails - **SEO Ops**: Content gap analysis and keyword research - **Finance Ops**: AI CFO for cost analysis - **Revenue Intelligence**: Sales call insights and attribution - **Conversion Ops**: CRO audits and lead magnet generation - **Podcast Ops**: Episode → multi-platform content - **Sales Playbook**: Value-based pricing frameworks - **Autoresearch**: Evolutionary content optimization - **Deck Generator**: AI slide deck creation - **YT Competitive Analysis**: YouTube outlier detection - **X Long-Form**: Human-sounding X/Twitter posts ## Installation ```bash # Clone the repository git clone https://github.com/ericosiu/ai-marketing-skills.git cd ai-marketing-skills # Navigate to a specific skill category cd growth-engine # or sales-pipeline, content-ops, etc. # Install dependencies for that category pip install -r requirements.txt # Set up environment variables cp .env.example .env ``` ## Configuration Each category uses a `.env` file for API keys and configuration: ```bash # Common environment variables across skills ANTHROPIC_API_KEY=your_anthropic_key_here OPENAI_API_KEY=your_openai_key_here # Growth Engine specific GOOGLE_ANALYTICS_KEY=your_ga_key LINKEDIN_API_KEY=your_linkedin_key # Sales Pipeline specific RB2B_API_KEY=your_rb2b_key INSTANTLY_API_KEY=your_instantly_key APOLLO_API_KEY=your_apollo_key # SEO Ops specific GOOGLE_SEARCH_CONSOLE_CREDENTIALS=path/to/credentials.json # Revenue Intelligence specific GONG_API_KEY=your_gong_key SALESFORCE_API_KEY=your_salesforce_key ``` ## Growth Engine Run autonomous marketing experiments with statistical rigor. ### Experiment Engine ```python from experiment_engine import ExperimentEngine # Initialize the engine engine = ExperimentEngine( api_key=os.getenv("ANTHROPIC_API_KEY"), data_source="google_analytics" ) # Create an experiment experiment = engine.create_experiment( hypothesis="Thread posts get 2x engagement vs single posts", variable="format", variants=["thread", "single"], metric="impressions", duration_days=14, traffic_split=0.5 ) # Run the experiment results = engine.run_experiment(experiment.id) # Get statistical significance analysis = engine.analyze_results( experiment.id, confidence_level=0.95, test_method="mann_whitney" ) print(f"Winner: {analysis['winner']}") print(f"P-value: {analysis['p_value']}") print(f"Confidence: {analysis['confidence_interval']}") ``` ### Pacing Alerts ```python from pacing_alert import PacingMonitor monitor = PacingMonitor( budget_monthly=10000, platform="linkedin" ) # Check daily pacing alert = monitor.check_pacing( spend_to_date=3500, days_elapsed=8, days_in_month=30 ) if alert['status'] == 'overpacing': print(f"Alert: Overpacing by {alert['variance_percent']}%") print(f"Recommended daily spend: ${alert['recommended_daily']}") ``` ### CLI Usage ```bash # Create experiment python experiment-engine.py create \ --hypothesis "Carousel posts outperform static images" \ --variable post_type \ --variants '["carousel", "static"]' \ --metric engagement_rate \ --duration 14 # Check pacing python pacing-alert.py check \ --budget 10000 \ --spend 3500 \ --days-elapsed 8 # Generate weekly scorecard python autogrowth-weekly-scorecard.py generate \ --start-date 2026-05-01 \ --end-date 2026-05-07 ``` ## Sales Pipeline Turn anonymous website visitors into qualified pipeline. ### RB2B Router ```python from rb2b_instantly_router import RB2BRouter router = RB2BRouter( rb2b_key=os.getenv("RB2B_API_KEY"), instantly_key=os.getenv("INSTANTLY_API_KEY") ) # Fetch website visitors from RB2B visitors = router.fetch_visitors( lookback_hours=24, min_intent_score=7 ) # Route to Instantly campaigns for visitor in visitors: # Score and enrich enriched = router.enrich_visitor(visitor) # Route based on criteria if enriched['seniority'] in ['C-Level', 'VP', 'Director']: router.add_to_campaign( email=enriched['email'], campaign_id="high-intent-vp", personalization={ 'company': enriched['company'], 'trigger': enriched['page_visited'] } ) ``` ### Deal Resurrector ```python from deal_resurrector import DealResurrector resurrector = DealResurrector( crm_api_key=os.getenv("SALESFORCE_API_KEY") ) # Find stale deals with departed champions stale_deals = resurrector.find_stale_deals( days_inactive=90, min_deal_value=10000 ) # Track champions to new companies for deal in stale_deals: champion_moves = resurrector.track_champion( champion_email=deal['primary_contact'], linkedin_api_key=os.getenv("LINKEDIN_API_KEY") ) if champion_moves['new_company']: resurrector.create_new_opportunity( company=champion_moves['new_company'], contact=champion_moves['new_email'], context=deal['previous_context'] ) ``` ### ICP Learner ```python from icp_learning_analyzer import ICPLearner learner = ICPLearner() # Analyze win/loss patterns deals = learner.fetch_closed_deals(months_back=12) patterns = learner.analyze_patterns(deals) # Update ICP definition new_icp = learner.update_icp( current_icp=""" Company size: 50-500 employees Industry: SaaS, E-commerce Tech stack: React, Python """, win_loss_data=patterns ) print(new_icp) ``` ## Content Ops Ship content that scores 90+ every time. ### Expert Panel ```python from expert_panel import ExpertPanel panel = ExpertPanel( api_key=os.getenv("ANTHROPIC_API_KEY") ) # Load expert personas panel.load_experts([ 'experts/seo_expert.json', 'experts/conversion_expert.json', 'experts/content_strategist.json' ]) # Score content content = """ Your blog post content here... """ scores = panel.score_content( content=content, rubric='scoring-rubrics/blog_post.json', min_score=90 ) # Recursive improvement while scores['average'] < 90: feedback = panel.get_improvement_suggestions(scores) content = panel.improve_content(content, feedback) scores = panel.score_content(content, 'scoring-rubrics/blog_post.json') print(f"Final score: {scores['average']}") print(f"Expert breakdown: {scores['by_expert']}") ``` ### Quality Gate ```bash # CLI quality gate python quality-gate.py check \ --file blog-post.md \ --rubric scoring-rubrics/blog_post.json \ --min-score 90 \ --experts seo conversion content-strategy ``` ## Outbound Engine ICP to inbox automation. ### Cold Outbound Optimizer ```python from cold_outbound_optimizer import OutboundEngine engine = OutboundEngine( apollo_key=os.getenv("APOLLO_API_KEY"), instantly_key=os.getenv("INSTANTLY_API_KEY") ) # Define ICP icp = { 'titles': ['VP Marketing', 'CMO', 'Head of Growth'], 'company_size': [50, 500], 'industries': ['SaaS', 'E-commerce'], 'technologies': ['HubSpot', 'Salesforce'] } # Build lead list leads = engine.build_lead_list( icp=icp, limit=1000, exclude_domains=['competitor1.com', 'competitor2.com'] ) # Generate personalized emails for lead in leads: email = engine.generate_email( lead=lead, template='references/cold_email_template.md', personalization_depth='high' ) engine.add_to_sequence( email=lead['email'], campaign='q2-outbound', message=email ) ``` ## SEO Ops Find keywords your competitors missed. ### Content Attack Brief ```python from content_attack_brief import SEOBrief brief = SEOBrief( gsc_credentials=os.getenv("GOOGLE_SEARCH_CONSOLE_CREDENTIALS") ) # Analyze content gaps gaps = brief.find_content_gaps( target_domain='yoursite.com', competitor_domains=['competitor1.com', 'competitor2.com'], topic='marketing automation' ) # Generate brief content_brief = brief.generate_brief( keyword=gaps[0]['keyword'], search_intent=gaps[0]['intent'], top_ranking_urls=gaps[0]['serp_results'] ) print(content_brief) ``` ### GSC Optimizer ```bash # CLI GSC optimization python gsc_client.py analyze \ --domain yoursite.com \ --lookback-days 90 \ --min-impressions 1000 \ --position-range 11-20 ``` ## Finance Ops AI CFO for cost analysis. ### CFO Briefing ```python from cfo_briefing import FinanceAnalyzer analyzer = FinanceAnalyzer() # Upload financial data analyzer.load_data( expenses='data/expenses_q1.csv', revenue='data/revenue_q1.csv' ) # Generate CFO briefing briefing = analyzer.generate_briefing( focus_areas=['hidden_costs', 'vendor_optimization', 'budget_variance'] ) # Get cost-saving recommendations recommendations = analyzer.find_savings_opportunities( min_impact=5000 # Minimum $5k annual savings ) print(briefing) for rec in recommendations: print(f"{rec['category']}: Save ${rec['annual_savings']:,.0f}") ``` ## Revenue Intelligence Sales call insights and attribution. ### Gong Insight Pipeline ```python from gong_insight_pipeline import GongAnalyzer analyzer = GongAnalyzer( gong_api_key=os.getenv("GONG_API_KEY") ) # Fetch recent calls calls = analyzer.fetch_calls( date_range='last_7_days', min_duration_minutes=20 ) # Extract insights for call in calls: insights = analyzer.extract_insights(call['id']) # Key patterns print(f"Objections: {insights['objections']}") print(f"Competitor mentions: {insights['competitors']}") print(f"Next steps: {insights['next_steps']}") # Update CRM analyzer.sync_to_crm( call_id=call['id'], insights=insights, crm='salesforce' ) ``` ## Troubleshooting ### API Rate Limits ```python # All scripts include retry logic with exponential backoff from utils import retry_with_backoff @retry_with_backoff(max_retries=5, base_delay=2) def api_call(): return client.make_request() ``` ### PII Sanitization ```bash # Scan for sensitive data before commits python3 security/sanitizer.py --scan --dir . --recursive # Install pre-commit hook cp security/pre-commit-hook.sh .git/hooks/pre-commit chmod +x .git/hooks/pre-commit ``` ### Dependencies Issues ```bash # Each category has isolated dependencies cd growth-engine pip install --upgrade -r requirements.txt # If conflicts, use virtual environment python -m venv venv source venv/bin/activate # or venv\Scripts\activate on Windows pip install -r requirements.txt ``` ### Data Privacy All scripts sanitize PII by default: ```python from security.sanitizer import sanitize_output # Automatically removes emails, phone numbers, API keys safe_data = sanitize_output(raw_data) ``` ## Common Patterns ### Chain Multiple Skills