Alfred Claw a96d0d8889 Initial commit: 6 AI marketing skill categories

- growth-engine: Autonomous experiment engine (Karpathy autoresearch for marketing)
- sales-pipeline: RB2B router, deal resurrector, trigger prospector, ICP learner
- content-ops: Expert panel, quality gate, editorial brain, quote miner
- outbound-engine: Cold outbound optimizer, lead pipeline, competitive monitor
- seo-ops: Content attack briefs, GSC optimizer, trend scout
- finance-ops: CFO briefing, cost estimate, scenario modeler

79 files, all sanitized - zero hardcoded credentials or internal references.

2026-03-27 20:14:52 -07:00

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AI Marketing Skills

Open-source Claude Code skills for B2B marketing and sales teams. Built by the team at Single Grain — battle-tested on real pipelines generating millions in revenue.

These aren't prompts. They're complete workflows — scripts, scoring algorithms, expert panels, and automation pipelines you can plug into Claude Code (or any AI coding agent) and run today.

🗂️ Skills

Category	What It Does	Key Skills
Growth Engine	Autonomous marketing experiments that run, measure, and optimize themselves	Experiment Engine, Pacing Alerts, Weekly Scorecard
Sales Pipeline	Turn anonymous website visitors into qualified pipeline	RB2B Router, Deal Resurrector, Trigger Prospector, ICP Learner
Content Ops	Ship content that scores 90+ every time	Expert Panel, Quality Gate, Editorial Brain, Quote Miner
Outbound Engine	ICP definition to emails in inbox — fully automated	Cold Outbound Optimizer, Lead Pipeline, Competitive Monitor
SEO Ops	Find the keywords your competitors missed	Content Attack Briefs, GSC Optimizer, Trend Scout
Finance Ops	Your AI CFO that finds hidden costs in 30 minutes	CFO Briefing, Cost Estimate, Scenario Modeler

🚀 Quick Start

Each skill category has its own README with setup instructions. The general pattern:

# 1. Clone the repo
git clone https://github.com/singlegrain/ai-marketing-skills.git
cd ai-marketing-skills

# 2. Pick a category
cd growth-engine  # or sales-pipeline, content-ops, etc.

# 3. Install dependencies
pip install -r requirements.txt

# 4. Set up environment variables
cp .env.example .env
# Edit .env with your API keys

# 5. Run
python experiment-engine.py create \
  --hypothesis "Thread posts get 2x engagement vs single posts" \
  --variable format \
  --variants '["thread", "single"]' \
  --metric impressions

🧠 How These Work with Claude Code

Every category includes a SKILL.md file. Drop it into your Claude Code project and the AI agent knows how to use the tools:

# In your project directory
cp ai-marketing-skills/growth-engine/SKILL.md .claude/skills/growth-engine.md

Then ask Claude Code: "Run an experiment testing carousel vs. static posts on LinkedIn" — it handles the rest.

📊 What Makes These Different

These aren't toy demos. Each skill was built to run real business operations:

Growth Engine uses bootstrap confidence intervals and Mann-Whitney U tests — real statistics, not vibes
Deal Resurrector has three intelligence layers including "follow the champion" — tracking departed contacts to their new companies
ICP Learner rewrites your ideal customer profile based on actual win/loss data — your targeting improves automatically
Expert Panel recursively scores content with domain-specific expert personas until quality hits 90+
RB2B Router does intent scoring, seniority-based company dedup, and agency classification before routing to outbound sequences

📁 Repository Structure

ai-marketing-skills/
├── README.md              ← You are here
├── growth-engine/         ← Autonomous experiments
│   ├── SKILL.md
│   ├── experiment-engine.py
│   ├── pacing-alert.py
│   ├── autogrowth-weekly-scorecard.py
│   └── ...
├── sales-pipeline/        ← Visitor → pipeline automation
│   ├── SKILL.md
│   ├── rb2b_instantly_router.py
│   ├── deal_resurrector.py
│   ├── trigger_prospector.py
│   ├── icp_learning_analyzer.py
│   └── ...
├── content-ops/           ← Quality scoring & production
│   ├── SKILL.md
│   ├── scripts/
│   ├── experts/           ← 9 expert panel definitions
│   ├── scoring-rubrics/   ← 5 scoring rubric templates
│   └── ...
├── outbound-engine/       ← Cold outbound automation
│   ├── SKILL.md
│   ├── scripts/
│   ├── references/        ← ICP template, copy rules
│   └── ...
├── seo-ops/               ← SEO intelligence
│   ├── SKILL.md
│   ├── content_attack_brief.py
│   ├── gsc_client.py
│   ├── trend_scout.py
│   └── ...
└── finance-ops/           ← Financial analysis
    ├── SKILL.md
    ├── scripts/
    ├── references/        ← Metrics, rates, ROI models
    └── ...

🤝 Contributing

Found a bug? Have an improvement? PRs welcome.

Fork the repo
Create your feature branch (git checkout -b feature/better-scoring)
Commit your changes
Push to the branch
Open a Pull Request

📄 License

MIT License. Use these however you want.

🏢 About

Built by the marketing engineering team at Single Grain. We help B2B companies grow with AI-powered marketing and sales operations.

Want these skills managed for you? Talk to us — we run these systems for companies doing $10M-$500M in revenue.

Star this repo if you find it useful. It helps others discover these tools.

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