| name | mvp-launcher |
| description | Rapid MVP validation and launch framework following Y Combinator and lean startup principles. Use when user wants to validate a business idea, launch an MVP, define product strategy, calculate unit economics, or needs structured approach to test product-market fit. Includes monetization models, metrics frameworks, and execution templates. |
MVP Launcher
Framework for rapid MVP validation and launch using proven startup methodologies from Y Combinator, lean startup, and first-principles thinking.
Core Workflow
1. Problem-Solution Validation
Ask the forcing questions:
- What specific problem are you solving? (Not a solution in disguise)
- Who has this problem urgently enough to pay?
- How are they solving it now? (Current alternatives = market validation)
- Why will they switch to your solution?
Output: One-sentence value proposition that passes the "mom test"
2. Market Sizing (Быстрая оценка)
Use scripts/market_sizer.py for TAM/SAM/SOM calculation:
python scripts/market_sizer.py --total-market 1000000 --addressable-percent 10 --obtainable-percent 5
Reality checks:
- TAM > $1B = maybe real market
- SAM > $100M = venture-scale potential
- SOM (first year) = be honest, usually <1% of SAM
3. MVP Scope Definition
Принцип: Smallest thing that proves value hypothesis
Use assets/mvp_canvas.md template to define:
- Core value proposition (1 sentence)
- Primary user action (1 action)
- Success metric (1 number)
- Time to first value (<5 minutes ideal)
Anti-pattern: "But we also need..." → NO. Ship, measure, iterate.
4. Monetization Strategy
See references/monetization-models.md for quick selection guide.
Key decisions:
- Pricing model: subscription / usage-based / transaction / freemium
- Price point: value-based (not cost-plus)
- Payment psychology: annual discounts, tiered pricing, anchor pricing
Forcing function: If they won't pay $X for this, the problem isn't urgent enough.
5. Unit Economics
Use scripts/unit_economics.py for rapid calculation:
python scripts/unit_economics.py --ltv 1200 --cac 400 --churn-rate 5
Minimum viability:
- LTV:CAC ratio ≥ 3:1
- CAC payback ≤ 12 months
- Monthly churn ≤ 5% (for SaaS)
If numbers don't work at scale, pivot or kill the idea.
6. Build & Launch Strategy
Week 1-2: Core feature + payment Week 3: First 10 paying customers (manual sales, no automation yet) Week 4: Measure, learn, decide: iterate / pivot / kill
Tech principles:
- No-code/low-code first (n8n, Airtable, Webflow)
- Manual processes behind automated facade
- Don't build what you can integrate
- PostgreSQL + Python API = enough for 100k users
7. Metrics Framework
See references/metrics-framework.md for stage-specific KPIs.
MVP stage - track only:
- Activation rate (signup → first value)
- Retention (D1, D7, D30)
- Revenue per user
- NPS or qualitative feedback
Ignore: total users, page views, social followers (vanity metrics)
8. Go-to-Market
Before product-market fit:
- Do things that don't scale (Paul Graham)
- Talk to users every day
- Manual sales → learn messaging
- Early adopters ≠ mainstream market
Channel testing sequence:
- Direct outreach (LinkedIn, email, communities)
- Content/SEO (if long sales cycle)
- Paid ads (only after LTV:CAC proven)
- Partnerships/integrations
Decision Points
When to iterate:
- Users sign up but don't activate → onboarding problem
- Users try once then churn → not enough value
- Good engagement, won't pay → wrong audience or positioning
When to pivot:
- No organic growth after 3 months of trying
- LTV:CAC fundamentally broken
- Users love it, but market too small
When to kill:
- No one cares (not even early adopters)
- You've stopped believing in it
- Better opportunity identified
Reference Materials
Load as needed:
- Monetization models: See
references/monetization-models.md - Metrics frameworks: See
references/metrics-framework.md - YC startup playbook: See
references/yc-playbook.md
Templates
- MVP Canvas:
assets/mvp_canvas.md- One-page planning template - Unit economics sheet: Generate via scripts
- Weekly progress template: In assets/
Anti-Patterns to Avoid
- Building for 6 months before talking to users
- Perfecting features instead of testing core hypothesis
- Raising money before product-market fit
- Hiring before proving the model works
- Optimizing conversion before understanding why users stay
- "If we build it, they will come"