Agent skill

startup-idea-validation

Systematic 9-dimension validation machine for testing ideas before building. Covers problem severity, market sizing, timing, competitive moats, unit economics, founder-market fit, technical feasibility, GTM clarity, and risk profile. Makes GO/NO-GO decisions based on evidence, not assumptions.

Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/startup-idea-validation-vasilyu1983-ai-agents-public

Metadata

Additional technical details for this skill

globs
**/*.md **/validation/** **/ideas/** **/hypothesis/**

SKILL.md

Startup Idea Validation

Systematic validation machine for testing ideas BEFORE building. Validate hypotheses, score opportunities, and make evidence-based GO/NO-GO decisions.

Modern Best Practices (Dec 2025):

  • Run a validation ladder (interviews → smoke test → concierge/MVP → paid pilots) before building.
  • Pre-register decision thresholds (avoid “moving goalposts” after seeing data).
  • Separate evidence quality (strong/medium/weak) from confidence and excitement.
  • Test willingness-to-pay early (pricing pages, pilots, LOIs) and treat time-to-value as a constraint.
  • Handle customer/market data with purpose limitation, retention, and access controls.

When to Use This Skill

Trigger Action
"Validate this idea" Run full 9-dimension validation
"Is this worth building?" Run validation scorecard
"Test my hypothesis" Run hypothesis canvas
"Market size for X" Run market sizing
"Should I build X or Y?" Run comparative validation
"What's the riskiest assumption?" Run RAT analysis

Validation Ladder (Dec 2025)

Step Goal Strong signal Weak signal Output
Customer interviews Validate problem + context Repeated pain + real workarounds Hypothetical enthusiasm Notes + quotes + JTBD
Smoke test Validate demand Clicks/signups with clear intent Survey-only interest Landing page metrics
Concierge / Wizard-of-Oz Validate workflow value Users complete job and return One-off curiosity Learning report
Paid pilot Validate willingness-to-pay Paid, renewed, or expanded “Will pay later” Pilot results + pricing

Use templates/validation-experiment-planner.md for experiment design and decision thresholds.

9-Dimension Validation Framework

Quick Reference

Dimension Weight Key Question Score Range
Problem Severity 15% Hair on fire or nice to have? 0-10
Market Size 12% Big enough to matter? 0-10
Market Timing 10% Why now? 0-10
Competitive Moat 12% Defensible advantage? 0-10
Unit Economics 15% Can this be profitable? 0-10
Founder-Market Fit 8% Right team for this? 0-10
Technical Feasibility 10% Can we actually build it? 0-10
GTM Clarity 10% Know how to reach customers? 0-10
Risk Profile 8% Manageable risk level? 0-10

Verdict Thresholds

Score Verdict Action
80-100 Strong GO Proceed to build
60-79 Conditional GO Validate riskiest assumptions first
40-59 PIVOT Core hypothesis needs rework
<40 NO-GO Fundamental issues, don't build

Dimension Deep Dives

1. Problem Severity (15%)

Question: Is this a "hair on fire" problem or a "nice to have"?

Signal Strength Indicators:

Score Description Evidence
9-10 Hair on fire Customers actively seeking solutions, willing to pay premium
7-8 Significant pain Multiple workarounds in use, clear cost of problem
5-6 Real but manageable Occasional complaints, spreadsheet solutions exist
3-4 Nice to have Would be good but not urgent
1-2 No real pain Solution looking for a problem

Evidence Sources:

  • Review mining (G2, Capterra, Reddit) → startup-review-mining
  • Customer interviews (5-10 minimum)
  • Support ticket analysis
  • Search volume for solutions

2. Market Size (12%)

Question: Is this market big enough to build a venture-scale business?

TAM/SAM/SOM Framework:

Metric Definition Minimum Threshold
TAM Total Addressable Market $1B+
SAM Serviceable Addressable Market $100M+
SOM Serviceable Obtainable Market (3yr) $10M+

Sizing Methods:

  • Top-down: Industry reports, analyst estimates
  • Bottom-up: Customer count × ACV
  • Comparable: Similar company revenue extrapolation

3. Market Timing (10%)

Question: Why now? What's changed that makes this possible/necessary?

Timing Signal Matrix:

Signal Strong Weak
Technology enabler Just became viable Has existed for years
Regulatory change New opportunity No change
Behavior shift COVID/platform shifts changed habits Status quo
Cost curve 10x cheaper now Same cost
Competition Market forming Saturated

Integration: Use startup-trend-prediction for timing analysis.

4. Competitive Moat (12%)

Question: What will make this defensible over time?

Moat Type Assessment:

Moat Type Strength Build Time Example
Network Effects Very Strong 18-24mo Marketplace, social
Switching Costs Strong 12-18mo Workflow integration
Data Moats Medium 6-12mo Proprietary datasets
Brand Medium 24mo+ Trust, reputation
Regulatory Strong Variable Licenses, compliance
Technology Weak <6mo Can be copied

5. Unit Economics (15%)

Question: Can this be a profitable business?

Key Metrics:

Metric Target Red Flag
LTV:CAC >3:1 <2:1
Payback Period <12 months >24 months
Gross Margin >70% (SaaS) <50%
NRR >100% <80%

Formula Basics:

LTV = (ARPU × Gross Margin) / Monthly Churn
CAC = Total Sales & Marketing / New Customers

6. Founder-Market Fit (8%)

Question: Are you the right person/team to solve this?

Assessment Criteria:

Factor Strong Weak
Domain Expertise 5+ years in space No experience
Network Direct access to buyers Cold outreach only
Insight Unique perspective Generic understanding
Passion Personal connection Pure opportunity
Ability to Execute Built similar before First attempt

7. Technical Feasibility (10%)

Question: Can we actually build this?

Feasibility Matrix:

Factor Score
Core technology exists +3
Similar products exist +2
Team has built similar +2
<6 month MVP possible +2
No regulatory blockers +1

8. GTM Clarity (10%)

Question: Do we know how to reach and convert customers?

GTM Readiness:

Element Clear Unclear
ICP definition Specific persona "Everyone"
Acquisition channel Tested, CAC known Guessing
Sales motion PLG/Sales/Hybrid decided TBD
Pricing Market-validated Assumed
First 10 customers Identified Unknown

9. Risk Profile (8%)

Question: What could kill this and how likely?

Risk Categories:

Risk Type Example Mitigation
Market Demand doesn't materialize Validate with pre-sales
Technical Can't build at scale Prototype early
Execution Team can't deliver Start small
Regulatory Law changes Legal review
Funding Can't raise Bootstrap path
Competition Incumbent pivots Speed, niche

Validation Workflow

START
  │
  ▼
┌─────────────────────────────────────┐
│ 1. PROBLEM VALIDATION               │
│    - Review mining (10+ sources)    │
│    - Customer interviews (5-10)     │
│    - Pain severity scoring          │
└─────────────────────────────────────┘
  │
  ▼
┌─────────────────────────────────────┐
│ 2. MARKET VALIDATION                │
│    - TAM/SAM/SOM calculation        │
│    - Timing analysis (trends)       │
│    - Competitive landscape          │
└─────────────────────────────────────┘
  │
  ▼
┌─────────────────────────────────────┐
│ 3. SOLUTION VALIDATION              │
│    - Technical feasibility          │
│    - Moat assessment                │
│    - Unit economics modeling        │
└─────────────────────────────────────┘
  │
  ▼
┌─────────────────────────────────────┐
│ 4. EXECUTION VALIDATION             │
│    - Founder-market fit             │
│    - GTM clarity                    │
│    - Risk assessment                │
└─────────────────────────────────────┘
  │
  ▼
┌─────────────────────────────────────┐
│ 5. SCORECARD & DECISION             │
│    - 9-dimension scoring            │
│    - GO/NO-GO verdict               │
│    - RAT identification             │
└─────────────────────────────────────┘
  │
  ▼
GO / CONDITIONAL GO / PIVOT / NO-GO

Navigation

Resources (Deep Dives)

Resource Purpose
validation-methodology.md 9-dimension scoring system details
hypothesis-testing-guide.md How to run validation experiments
market-sizing-patterns.md TAM/SAM/SOM calculation methods
moat-assessment-framework.md Competitive barrier analysis

Templates (Outputs)

Template Use For
validation-scorecard.md Full 9-dimension scoring
hypothesis-canvas.md Hypothesis testing template
validation-experiment-planner.md Hypothesis → method → metric → decision
riskiest-assumption-test.md RAT experiment design
market-sizing-worksheet.md TAM/SAM/SOM calculation
go-no-go-decision.md Final decision template

Data

File Contents
sources.json Validation resources (YC, a16z, SVPG, etc.)

Integration Points

Receives From

  • startup-review-mining - Pain point evidence
  • startup-trend-prediction - Market timing data
  • startup-competitive-analysis - Competitor data

Feeds Into

  • router-startup - Validation results
  • product-management - Validated requirements
  • startup-business-models - Monetization decisions

Quick Start

Minimum Viable Validation

For rapid first-pass validation:

  1. Pain Check (15 min)

    • Search G2/Capterra for competitor complaints
    • Search Reddit for problem discussions
    • Score: Is this severe enough to trigger action (not “nice to have”)?
  2. Size Check (15 min)

    • How many potential customers?
    • What would they pay?
    • Is the market plausibly large enough for your target outcome? [Inference]
  3. Timing Check (10 min)

    • Why now vs 2 years ago?
    • What changed?
  4. Competition Check (15 min)

    • Who else is doing this?
    • What's wrong with existing solutions?

If all 4 checks pass: Proceed to full validation scorecard. If any fail: Pivot or abandon.


Key Principles

Evidence Over Opinion

Every score must have evidence:

  • GOOD: "Pain score 8/10: 47 reviews mention this specific complaint (sources linked)"
  • BAD: "Pain score 8/10: I think this is a real problem"

Invalidate Fast

Goal is to find reasons NOT to build:

  • Cheap to kill ideas, expensive to build
  • Seek disconfirming evidence
  • Run the riskiest assumption test first

Iterate the Idea, Not Just the Validation

If validation reveals issues:

  • Don't just re-score, adjust the idea
  • Pivot to stronger position
  • Find the version that scores 80+

Do / Avoid (Dec 2025)

Do

  • Validate the riskiest assumption first (RAT), not the easiest to test.
  • Use the ladder: interviews → smoke → concierge → paid pilots.
  • Treat willingness-to-pay as a primary signal, not an afterthought.
  • Write decision thresholds before running experiments.

Avoid

  • Survey-only validation and hypothetical questions (“Would you use this?”).
  • Sampling bias (friends, one subreddit, one review site) without triangulation.
  • Building an MVP as “validation” without falsifiable hypotheses.

What Good Looks Like

  • ICP: one narrow segment with a clear job, pain severity, and buying trigger.
  • Evidence: 10+ direct conversations in the ICP with repeatable pain patterns (not one-off anecdotes).
  • WTP: explicit pricing tests and at least one “paid” signal (deposit, pilot fee, LOI with price).
  • Experiments: hypotheses + success metrics + stop rules written before execution.
  • Decision: a documented go/no-go with the next smallest reversible step.

Optional: AI / Automation

Use only when explicitly requested and policy-compliant.

  • Summarization/clustering: speed up synthesis, but keep raw notes + spot-checks.
  • Copy drafting: generate landing page variants; humans verify claims and compliance.

Expand your agent's capabilities with these related and highly-rated skills.

Didn't find tool you were looking for?

Be as detailed as possible for better results