From bette-think
Generates PMF surveys and analyzes results using Rahul Vohra's Superhuman framework. Measures 'very disappointed' score against 40% benchmark for product-market fit.
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Create and analyze a **Product-Market Fit survey** using Rahul Vohra's Superhuman framework.
Assesses product-market fit using Sean Ellis survey, retention curves, leading indicators, segment analysis, and post-PMF scaling strategies. Useful for PMF validation, engagement measurement, surveys, and scaling decisions.
Measures product-market fit with Sean Ellis surveys (40% very disappointed rule), Superhuman engine, retention curves, and scaling indicators. For validating products, diagnosing retention, and planning growth.
Guides product-market fit (PMF) via measurement frameworks, retention analysis, Sean Ellis surveys, segment strategies, pre/post-PMF navigation. Activates on PMF, retention mentions.
Share bugs, ideas, or general feedback.
Create and analyze a Product-Market Fit survey using Rahul Vohra's Superhuman framework.
When this skill is invoked, start with:
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PMF SURVEY
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The magic number: >40% "very disappointed" = PMF
What do you want to do?
1. Create a PMF survey
→ Generate the four questions + implementation guide
2. Analyze existing results
→ Paste your data, get PMF score + action plan
3. Understand the framework
→ Learn when and how to use this
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Guides you through running Sean Ellis's PMF survey with Rahul Vohra's systematic approach:
/pmf-survey
Optional parameters:
/pmf-survey --create - Generate survey questions/pmf-survey --analyze [data] - Analyze existing survey results/pmf-survey --export - Export survey to Typeform/Google Forms formatValidates readiness:
Generates four-question survey:
Provides implementation guidance:
Calculates PMF score:
Segments users:
Identifies high-expectation customers:
Generates improvement plan:
📊 PMF Survey Analysis
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🎯 PMF SCORE: 58%
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✅ STRONG PMF (>40% very disappointed)
Responses: 250 total
- 145 (58%) - Very disappointed 🟢
- 72 (29%) - Somewhat disappointed 🟡
- 33 (13%) - Not disappointed 🔴
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👥 HIGH-EXPECTATION CUSTOMERS
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From "What type of people benefit most?":
1. Technical founders at early-stage startups (mentioned 89 times)
2. Solo developers building side projects (mentioned 67 times)
3. Remote engineering teams (mentioned 54 times)
Focus: Technical founders at early-stage startups
Why: Most discerning, strongest advocates, willing to pay
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💡 MAIN BENEFITS (from supporters)
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Top themes:
1. "Saves me 10+ hours/week on documentation" (92 mentions)
2. "Makes code review actually enjoyable" (78 mentions)
3. "Onboards new devs 3x faster" (65 mentions)
🎯 Your core value prop: Time-saving documentation + code review
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🔧 IMPROVEMENTS NEEDED
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From supporters (PRIORITIZE):
1. "Better GitHub integration" (45 mentions) → Build this
2. "Slack notifications" (38 mentions) → Build this
3. "Team analytics dashboard" (31 mentions) → Build this
From neutrals (CONSIDER):
1. "More customization options" (22 mentions) → Maybe
2. "Mobile app" (18 mentions) → Later
From detractors (IGNORE):
1. "Should be totally free" (12 mentions) → Wrong segment
2. "Too focused on documentation" (8 mentions) → Not our market
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📋 ACTION PLAN
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✅ DOUBLE DOWN (you have PMF, now optimize):
1. Focus on high-expectation customer:
"Technical founders at early-stage startups"
2. Amplify core benefits:
- Time-saving documentation
- Code review improvements
- Faster onboarding
3. Build supporter requests:
Priority 1: Better GitHub integration
Priority 2: Slack notifications
Priority 3: Team analytics
4. Ignore detractor feedback:
- Wrong pricing expectations = wrong segment
- "Too focused" = they're not your market
🎯 GROWTH STRATEGY
Now that you have PMF (58% > 40%):
- Segment marketing to "technical founders at early-stage startups"
- Lead with "Save 10+ hours/week on documentation"
- Build supporter-requested features
- Survey again in 3 months to measure improvement
See the full PMF Survey framework at:
frameworks/measurement/pmf-survey.md
Framework: Rahul Vohra (Superhuman) based on Sean Ellis Best for: Measuring PMF, prioritizing improvements, identifying target market Magic number: >40% "very disappointed" = PMF