30x-growth-marketing-panel

# 30x Growth Marketing Panel > Skill by [ara.so](https://ara.so) — Marketing Skills collection. An AI-powered expert panel of 11 world-class marketing experts distilled from 4,000+ YouTube videos. Get answers from the right expert(s) in their voice, using their actual frameworks. ## What It Does The 30x Growth Marketing Panel uses a dual-layer architecture to provide authentic expert advice: - **Layer 1 (Brain)**: NotebookLM retrieval from 4,000+ indexed YouTube videos - **Layer 2 (Soul)**: Persona Protocol with expert personality, frameworks, and anti-patterns - **Semantic routing**: Automatically matches your question to the right expert(s) - **Anti-hallucination**: Retrieve-first protocol ensures responses are grounded in actual expert content ## Installation ```bash npx skills add norahe0304-art/30x-growth-marketing-panel ``` Works with Claude Code, Cursor, Codex, and 45+ AI coding agents. ## The Expert Panel | Expert | Domain | Best For | |--------|--------|----------| | **Alex Hormozi** | Offer creation, pricing, sales, scaling | SaaS pricing, value propositions, sales frameworks | | **Greg Isenberg** | AI startups, community growth, vibe marketing | Community-led growth, AI product positioning | | **Neil Patel** | SEO, paid ads, CRO, social media | Traffic generation, conversion optimization | | **Nathan Gotch** | AI SEO, Search Everywhere Optimization | AI-powered SEO strategies, ranking tactics | | **Authority Hacker** | AI content at scale, affiliate marketing | Content automation, affiliate revenue | | **Sabrina Ramonov** | AI agents, automation workflows, MCP | Marketing automation, AI agent implementation | | **Liam Ottley** | AI automation agency, client acquisition | Agency model, service packaging | | **Julia McCoy** | AI writing, content strategy, brand building | Content creation, brand voice | | **Ryan Doser** | AI marketing tools, practical implementation | Tool stack, workflow optimization | | **Growth Tribe** | Growth hacking, experimentation, AARRR | Experimentation frameworks, funnel optimization | | **Dan Koe** | One-person business, writing, personal brand | Solopreneur strategy, personal branding | ## Usage Patterns ### Single Expert Consultation Ask focused questions to get advice from the most relevant expert: ```bash # Pricing question → routes to Alex Hormozi "How should I price my B2B SaaS product?" # SEO question → routes to Neil Patel or Nathan Gotch "What's the best AI SEO strategy for 2026?" # Content question → routes to Julia McCoy "How do I build a consistent content voice?" ``` ### Named Expert Request Explicitly request a specific expert: ```bash "Ask Alex Hormozi about my offer: [describe your offer]" "What would Neil Patel say about this landing page conversion issue?" "Get Greg Isenberg's take on community-led growth for an AI tool" ``` ### Multi-Expert Roundtable Broad strategic questions trigger multiple experts: ```bash "How should I go to market with a new AI marketing tool?" # Returns perspectives from Greg Isenberg, Neil Patel, Ryan Doser "What's the best growth strategy for a bootstrapped SaaS?" # Returns perspectives from Alex Hormozi, Dan Koe, Growth Tribe ``` ## Expert Knowledge Base Structure Each expert has two components: ### 1. NotebookLM Brain (Raw Retrieval) ```bash # 200-300 YouTube videos per expert # Indexed in NotebookLM Pro (300 sources/notebook) # Zero information loss from original content ``` ### 2. Persona Protocol (Personality) Located in `expert_kb.md` for each expert: ```markdown ## Role Who the expert is, their background, core expertise ## Thinking Models Frameworks they use (e.g., Hormozi's Value Equation, AARRR funnel) ## Tone & Communication How they speak, teaching style, personality markers ## Anti-Patterns What they avoid, common mistakes they call out ## Retrieval Logic How to search their NotebookLM notebook effectively ``` ## Anti-Hallucination Protocol The panel follows strict retrieval rules: 1. **Retrieve first**: Must search NotebookLM before generating responses 2. **Dual verification**: Cross-reference retrieval with KB persona 3. **Explicit marking**: Extrapolations from core principles marked with ⚠️ 4. **Never fabricate**: If an expert hasn't covered a topic, say so Example output structure: ```markdown **Alex Hormozi's Perspective:** [Retrieved content from NotebookLM] Framework: Value Equation - Dream Outcome: [specific to your question] - Perceived Likelihood: [specific analysis] - Time Delay: [specific analysis] - Effort & Sacrifice: [specific analysis] ⚠️ *Extrapolating from core principles:* [only if needed] ``` ## Distilling Your Own Expert Use the `distill_anyone.md` prompt template: ```bash # 1. Copy the prompt from distill_anyone.md # 2. Change 3 variables: # - Expert name # - YouTube channel/playlist URL # - Domain expertise # 3. Run in Claude Code # The pipeline automatically: # - Collects YouTube URLs with yt-dlp # - Creates NotebookLM notebook # - Bulk adds videos with notebooklm-py # - Generates Persona Protocol KB # - Creates semantic routing rules ``` ### Variables to Configure ```bash EXPERT_NAME="Your Expert" YOUTUBE_SOURCE="https://youtube.com/@channel or playlist URL" DOMAIN="their core expertise area" ``` ## Key Commands ### Query the Panel ```bash # General query (auto-routes) "[Your marketing question]" # Named expert "Ask [Expert Name] about [topic]" # Multi-expert "Get a roundtable on [strategic question]" ``` ### Inspect Expert Knowledge ```bash # List all experts and domains "Show me the expert panel" # View expert frameworks "What frameworks does Alex Hormozi use?" # Check NotebookLM coverage "How many videos are indexed for Neil Patel?" ``` ## Configuration ### Language Support Responses automatically match your query language. Framework names stay in English: ```bash # Query in Spanish → Response in Spanish "¿Cómo debería fijar el precio de mi SaaS?" # Query in French → Response in French "Quelle est la meilleure stratégie SEO?" ``` ### Retrieval Depth Adjust how many NotebookLM sources to search: ```bash # Default: 5-10 most relevant sources # Deep research: 20-30 sources (slower, more comprehensive) # Quick answer: 3-5 sources (faster, still accurate) ``` ## Real Code Examples ### Example 1: Pricing Strategy (Alex Hormozi) **Query:** ``` "How should I price my B2B SaaS with a $10k LTV and 6-month sales cycle?" ``` **Expected Response:** ```markdown **Alex Hormozi's Pricing Framework:** [Retrieved from NotebookLM: Hormozi's pricing videos] **Value Equation Analysis:** 1. Dream Outcome: Enterprise efficiency gain (high) 2. Perceived Likelihood: 6-month cycle = trust-building needed 3. Time Delay: 6 months is long → charge more upfront 4. Effort & Sacrifice: Implementation complexity matters **Pricing Recommendation:** - Annual contract: $5,000-$7,500/year - Upfront discount: 20% off for annual vs monthly - Reduce time delay perception: 30-day quick wins - Stack value: Implementation support included **Hormozi's Principle:** "Charge for the dream outcome, not your cost" ``` ### Example 2: SEO Strategy (Neil Patel + Nathan Gotch) **Query:** ``` "What's the best AI SEO strategy for a new marketing tool in 2026?" ``` **Expected Response:** ```markdown **Neil Patel's Take:** [Retrieved content about AI content + E-E-A-T] - Focus on helpful content, not just keywords - Build topical authority clusters - Optimize for Google's AI Overviews **Nathan Gotch's SEO Approach:** [Retrieved content about Search Everywhere Optimization] - SEO = Search Everywhere Optimization now - Target YouTube, LinkedIn, Reddit, not just Google - Use AI to create platform-specific content variants **Combined Strategy:** 1. Build content cluster on "AI marketing automation" 2. Create YouTube tutorials (ranks in Google + YouTube) 3. Repurpose to LinkedIn thought leadership 4. Use Reddit for community validation signals ``` ### Example 3: Community-Led Growth (Greg Isenberg) **Query:** ``` "How do I build community around my AI productivity tool?" ``` **Expected Response:** ```markdown **Greg Isenberg's Community Framework:** [Retrieved from NotebookLM: Community-building videos] **Phase 1: Vibe Check (Month 1-2)** - Find where your ideal users already hang out - Join conversations, don't pitch - Identify the "energy" — what gets them excited? **Phase 2: Minimum Viable Community (Month 3)** - Discord or Slack, not Facebook - 1 channel: #wins (social proof flywheel) - You post first 20 wins yourself if needed **Phase 3: Community-Led Product** - Ship features the community requests - Give power users early access - Make them feel like co-creators **Greg's Key Insight:** "Community isn't a channel, it's a moat" ``` ## Common Patterns ### Pattern 1: Multi-Stage Funnel Question ```bash # Question spans multiple domains "I need help with my SaaS go-to-market: offer, SEO, and community" # Response includes: # - Alex Hormozi: Offer positioning # - Neil Patel: SEO strategy # - Greg Isenberg: Community layer ``` ### Pattern 2: Framework Deep-Dive ```bash # Request specific framework "Explain Alex Hormozi's Value Equation for my use case" # Response: # - Retrieves original explanation from NotebookLM # - Maps framework to your specific scenario # - Includes anti-patterns from KB ``` ### Pattern 3: Comparative Analysis ```bash # Compare expert approaches "How would Dan Koe vs Alex Hormozi approach pricing a course?" # Response: # - Dan Koe: Personal brand, premium positioning, audience relationship # - Alex Hormozi: Value equation, enterprise pricing, sales frameworks # - Synthesis: When to use each approach ``` ## Troubleshooting ### Issue: Generic or Vague Response **Problem:** Response doesn't sound like the expert **Solution:** - Check if question is in expert's domain - Request named expert explicitly - Ask for specific framework by name ```bash # Instead of: "How do I market?" # Try: "Ask Alex Hormozi: How should I position my offer using the Value Equation?" ``` ### Issue: No Retrieval Evidence **Problem:** Response lacks [Retrieved from NotebookLM] markers **Solution:** - Expert may not have covered this topic - Reframe question to match expert's known content areas - Check expert domain table above ### Issue: Multi-Expert Overload **Problem:** Too many perspectives for a simple question **Solution:** - Ask for single expert - Rephrase as focused question ```bash # Instead of: "How do I grow?" # Try: "What's Neil Patel's SEO strategy for [specific use case]?" ``` ### Issue: Outdated Framework **Problem:** Expert's content is from 2023-2024 **Solution:** - Ask for principles, not tactics - Request ⚠️ extrapolation for 2026 context ```bash "What would Neil Patel's SEO principles be for 2026, given AI Overviews?" ``` ## Advanced Usage ### Combine with Your Context ```bash # Provide your specific situation "Here's my SaaS: [details]. Ask Alex Hormozi how to price it." # Attach data "My conversion rate is 2%. Ask Neil Patel to audit my funnel." ``` ### Sequential Expert Consultation ```bash # Step 1: Offer with Hormozi "Alex Hormozi: Review my offer" # Step 2: Traffic with Neil Patel "Neil Patel: Now how do I drive traffic to this offer?" # Step 3: Community with Greg Isenberg "Greg Isenberg: Should I add a community layer?" ``` ### Export Expert Advice ```bash # Generate structured output "Create a marketing strategy doc consulting: - Alex Hormozi for offer - Neil Patel for SEO - Greg Isenberg for community" # Output: Markdown doc with all expert perspectives organized ``` ## Tools Used Internally The panel is built with: - **yt-dlp**: YouTube URL batch collection - **notebooklm-py**: Programmatic NotebookLM access - **NotebookLM Pro**: 300 sources/notebook indexing - **Claude Code Skills**: Persona Protocol + dual-layer fusion You don't need to install these separately — they're embedded in the skill. ## Best Practices 1. **Be specific**: "How do I price?" → "How do I price a B2B SaaS at $10k ACV?" 2. **Name the expert**: When you know who you want 3. **Provide context**: Share your industry, stage, constraints 4. **Request frameworks**: Ask for specific models by name 5. **Iterate**: Start with one expert, then consult others ## License MIT — Free to use, modify, and distribute.