营销运营来源：GitHub 协议：MIT 协议已标注

ai-marketing-skills

把营销调研、内容实验和增长分析整理成可复用的 Agent 技能。

3.5K GitHub stars 2026-09-05 星标同步 2026-07-16 收录时间

#marketing#growth#research

快速判断

01这个 Skill 是干嘛的: 把营销调研、定位、内容实验和增长分析整理成可复用 skills。
02它能解决什么问题: 解决营销工作依赖临时灵感、实验无记录和研究到执行之间脱节的问题。
03它适合谁来用: 适合初创团队、增长负责人和需要 Agent 协助营销执行的独立创作者。

ai-marketing-skills

把营销调研、内容实验和增长分析整理成可复用的 Agent 技能。

主要能力

Run Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".

使用方式

仓库级安装会保留完整目录；本次核验的入口是 autoresearch/SKILL.md。使用前先阅读英文 README 与原始 SKILL.md，并按当前 Agent 的目录规范安装。

适用边界

营销实验必须结合真实渠道数据，不能把通用建议直接当成增长结论。

许可与来源

来源：GitHub 公开仓库。核验许可：MIT。本页是贴近原仓库结构的中文导读，具体参数、依赖和更新记录以英文 README 为准。

AI Marketing Skills

Open-source Claude Code skills for marketing and sales teams. Built by the team at Single Brain — 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.

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🗂️ 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
Revenue Intelligence	Prove content ROI and turn sales calls into strategy	Gong Insight Pipeline, Revenue Attribution, Client Report Generator
Conversion Ops	Score any landing page and turn survey data into lead magnets	CRO Audit, Survey-to-Lead-Magnet Engine
Podcast Ops	One episode → 20+ content pieces across every platform	Podcast-to-Everything Pipeline, Content Calendar
Team Ops	Ruthless performance audits and meeting intelligence	Elon Algorithm, Meeting-to-Action Extractor
Sales Playbook	Value-based pricing framework that turns $10K deals into $100K deals	Pre-Call Briefing, Tiered Packager, Call Analyzer, Pattern Library
Autoresearch	Karpathy-inspired optimization loops for conversion content — 50+ variants, expert scoring, evolved winners	Variant Generator, Expert Panel Scorer, Evolution Engine
Deck Generator	AI-generated slide decks with consistent visual styles in minutes	Image Generator, Google Slides Builder, Style Presets
YT Competitive Analysis	Find outlier videos and packaging patterns across any YouTube channels	Outlier Detector, Title Pattern Extractor, Channel Benchmarker
X Long-Form + Humanizer	Write X articles that sound human — with a 24-pattern AI slop detector	Post Writer, Humanizer Checklist, ASCII Diagram Builder

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🚀 Quick Start

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

# 1. Clone the repo
git clone https://github.com/ericosiu/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

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🧠 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.

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📊 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

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📁 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
│   └── ...
├── revenue-intelligence/  ← Sales call insights + attribution
│   ├── SKILL.md
│   ├── gong_insight_pipeline.py
│   ├── revenue_attribution.py
│   └── client_report_generator.py
├── conversion-ops/        ← CRO + lead magnet generation
│   ├── SKILL.md
│   ├── cro_audit.py
│   └── survey_lead_magnet.py
├── podcast-ops/           ← Podcast → content factory
│   ├── SKILL.md
│   └── podcast_pipeline.py
├── team-ops/              ← Performance audits + meeting intel
│   ├── SKILL.md
│   ├── team_performance_audit.py
│   └── meeting_action_extractor.py
└── sales-playbook/        ← Value-based pricing framework
    ├── SKILL.md
    ├── value_pricing_briefing.py
    ├── value_pricing_packager.py
    ├── call_analyzer.py
    └── pricing_pattern_library.py

---

🔒 Privacy & Security

Every skill is built with data privacy in mind:

PII Sanitizer scans code and data for sensitive information before commits (security/sanitizer.py)
Pre-commit hook blocks commits containing detected PII patterns
Configurable blocklists for company names, person names, and custom patterns
See security/README.md for setup

# Scan for sensitive data
python3 security/sanitizer.py --scan --dir . --recursive

# Install the pre-commit hook
cp security/pre-commit-hook.sh .git/hooks/pre-commit && chmod +x .git/hooks/pre-commit

---

📡 Telemetry (Opt-In)

Anonymous usage telemetry helps us understand which skills people actually use. Fully opt-in, privacy-first:

Local logging always — see your own usage stats in ~/.ai-marketing-skills/analytics/
Remote reporting optional — only if you explicitly opt in on first run
Data collected: skill name, duration, success/fail, version, OS. Nothing else. No code, no file paths, no repo content.
Version checks — get notified when new skills are available

# View your local usage stats
python3 telemetry/telemetry_report.py

# Check for updates
python3 telemetry/version_check.py

See telemetry/README.md for details.

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🤝 Contributing

Found a bug? Have an improvement? PRs welcome. Read CONTRIBUTING.md for guidelines.

Fork the repo
Create your feature branch (git checkout -b feature/better-scoring)
Run python3 security/sanitizer.py --scan before committing
Commit your changes
Push to the branch
Open a Pull Request

---

📄 License

MIT License. Use these however you want.

---

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

---

🧠 Want these built and managed for you? →

This is how we build agents at Single Brain for our clients.

Single Grain · our marketing agency

📬 Level up your marketing with 14,000+ marketers and founders → (free)

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安装

选择适合你当前 Agent 的方式

01 · 发给当前 Agent自动识别兼容方式与安装位置

请帮我安装「ai-marketing-skills」。它是一个 Agent Skill 或仓库级 Skill 包。
来源：https://github.com/ericosiu/ai-marketing-skills

请先阅读仓库的 README、SKILL.md 与安装说明，再按我当前使用的 Agent 的原生规范安装；如果仓库兼容 Agent Skills CLI，优先执行「npx skills add https://github.com/ericosiu/ai-marketing-skills」。不要写死为某一个 Agent 的专用目录，不覆盖已有同名文件。完成后告诉我实际安装位置、启用方式和验证结果。

在 GitHub 查看原文

本站按仓库级 package 收录，安装时克隆完整 ai-marketing-skills 仓库；已核验的 skill 入口为 autoresearch/SKILL.md。

运行前阅读 README、依赖说明和 MIT 许可；涉及账号、外部写入或高风险领域时保留人工确认。

安装即视为你已阅读并遵守该 skill 的开源协议。内容版权归原作者所有。