startup-validator

ailabs-393/ai-labs-claude-skills · updated May 25, 2026

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$npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill startup-validator
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summary

A comprehensive tool for analyzing startup ideas through systematic market research, competitive analysis, problem validation, and positioning strategy. This skill helps evaluate whether a startup idea has genuine market potential and how to position it effectively.

skill.md

Startup Validator

A comprehensive tool for analyzing startup ideas through systematic market research, competitive analysis, problem validation, and positioning strategy. This skill helps evaluate whether a startup idea has genuine market potential and how to position it effectively.

Core Workflow

When a user presents a startup idea, follow this systematic validation process:

1. Idea Clarification & Scoping (2-3 minutes)

Ensure complete understanding before research begins:

Extract key information:

  • Problem being solved
  • Target customer/market
  • Proposed solution
  • Business model (if mentioned)
  • Geographic focus (default: global/US)

Ask clarifying questions only if critical information is missing:

  • "Who specifically is your target customer?"
  • "What problem are they currently facing?"
  • "How are they solving this problem today?"

Do not ask for information you can research independently (market size, competitors, trends).

2. Research Plan Development (1 minute)

Based on the idea, create a research plan identifying:

  • Market size queries needed
  • Competitor research keywords
  • Problem validation searches
  • Trend analysis topics
  • Pricing/business model research

Use templates from references/research_templates.md for query formulation.

3. Comprehensive Market Research (10-15 tool calls minimum)

Execute systematic research across all dimensions. Always use at least 10-15 web searches to ensure thorough analysis.

A. Market Opportunity (3-5 searches)

Search for:

  • Market size and projections
  • Growth rates and trends
  • TAM/SAM calculations
  • Industry reports and forecasts

Query examples:

  • "[industry] market size 2025"
  • "global [product category] market forecast"
  • "[industry] growth rate CAGR"

B. Competitive Landscape (3-5 searches)

Search for:

  • Direct competitors
  • Alternative solutions
  • Market leaders
  • Recent funding/acquisitions

Query examples:

  • "[solution type] companies"
  • "[product category] alternatives"
  • "best [product type] 2025"
  • "[industry] startups funding"

C. Problem Validation (2-3 searches)

Search for:

  • Evidence of the problem
  • Current pain points
  • Customer behavior patterns
  • Existing budget allocation

Query examples:

  • "[target customer] challenges [industry]"
  • "why [target customer] need [solution]"
  • "[problem] statistics"

D. Market Trends (2-3 searches)

Search for:

  • Technology trends
  • Regulatory changes
  • Consumer behavior shifts
  • Investment patterns

Query examples:

  • "[industry] trends 2025"
  • "future of [technology/market]"
  • "[industry] investment report"

E. Business Model Research (1-2 searches)

Search for:

  • Pricing models in the space
  • Unit economics benchmarks
  • Customer acquisition strategies

Query examples:

  • "[product] pricing models"
  • "[industry] average customer acquisition cost"

CRITICAL: Use web_fetch to read full articles from authoritative sources (Gartner, McKinsey, Statista, Crunchbase, industry reports) to get detailed data, not just snippets.

4. Data Analysis & Synthesis

After gathering data, analyze using frameworks from references/frameworks.md:

Market Opportunity Assessment

  • Calculate/estimate TAM, SAM, SOM
  • Evaluate growth trajectory
  • Identify market trends (favorable/unfavorable)
  • Assess market maturity stage

Competitive Positioning

  • Map competitive landscape (direct/indirect/adjacent)
  • Identify market gaps
  • Evaluate barriers to entry
  • Assess competitive advantages needed

Problem-Solution Fit

  • Validate problem frequency and intensity
  • Assess willingness to pay
  • Evaluate current solutions and their limitations
  • Identify unique value proposition opportunities

Business Model Viability

  • Estimate unit economics potential
  • Assess scalability
  • Evaluate pricing power
  • Consider customer acquisition channels

Optional: If quantitative data is available, create a JSON file and use scripts/market_analyzer.py to calculate metrics and generate additional insights.

5. Risk & Opportunity Identification

Clearly articulate:

  • Critical Risks: Deal-breakers or major challenges
  • Manageable Risks: Solvable with strategy/execution
  • Key Opportunities: Market gaps, timing advantages, trends
  • Assumptions to Validate: Hypotheses needing testing

6. Positioning Strategy

Develop specific recommendations:

  • Target Market Segmentation: Primary beachhead market
  • Value Proposition: Core benefit statement
  • Differentiation Strategy: How to stand out
  • Go-to-Market Approach: Distribution and acquisition strategy
  • Positioning Statement: Concise market positioning

7. Report Generation

Create a comprehensive markdown report with:

# [Startup Idea] Validation Report

## Executive Summary
- One-paragraph overview
- Bottom-line recommendation: STRONG GO / PROCEED WITH VALIDATION / PIVOT RECOMMENDED / NOT VIABLE
- 3-5 key findings

## Market Analysis
### Market Size & Growth
- TAM/SAM/SOM estimates with sources
- Growth rate and trajectory
- Market maturity assessment

### Market Trends
- Key favorable trends
- Potential headwinds
- Timing considerations

## Competitive Landscape
### Direct Competitors
- List with brief descriptions
- Market share/position
- Strengths and weaknesses

### Indirect Competition
- Alternative solutions
- Substitutes

### Competitive Gaps
- Unmet needs
- Positioning opportunities

## Problem-Solution Fit
### Problem Validation
- Evidence of problem
- Frequency and intensity
- Current solutions and limitations

### Solution Differentiation
- Unique value proposition
- Competitive advantages
- Potential moats

## Business Model Assessment
### Revenue Model
- Pricing strategy alignment
- Unit economics potential
- Scalability factors

### Customer Acquisition
- Primary channels
- CAC considerations
- Sales cycle estimates

## Risk Analysis
### Critical Risks
- Deal-breakers
- Major challenges

### Manageable Risks
- Addressable concerns
- Mitigation strategies

## Positioning Recommendations
### Target Market
- Primary customer segment
- Beachhead market strategy

### Value Proposition
- Core benefit statement
- Key differentiators

### Go-to-Market Strategy
- Distribution approach
- Partnership opportunities
- Initial traction strategy

## Validation Next Steps
1. Immediate actions to validate assumptions
2. Customer interviews needed
3. MVPs or prototypes to test
4. Metrics to track

## Sources
[List all key sources with links]

Formatting Guidelines:

  • Use clear headers and subheaders
  • Bold key metrics and findings
  • Include specific numbers with sources
  • Use bullet points for scannability
  • Cite sources inline with links
  • Keep executive summary under 200 words

Quality Standards

Research Thoroughness

  • Minimum 10-15 web searches across all dimensions
  • Use authoritative sources (prioritize: Gartner, Forrester, McKinsey, Statista, Crunchbase, industry analysts)
  • Cross-validate data from multiple sources
  • Fetch full articles for detailed analysis, not just snippets

Analysis Depth

  • Apply multiple frameworks from references/frameworks.md
  • Provide specific numbers and estimates (not vague statements)
  • Identify both opportunities AND risks
  • Include actionable recommendations

Report Quality

  • Clear executive summary with definitive recommendation
  • Well-structured with logical flow
  • Specific and actionable insights
  • Properly cited sources
  • Honest about data limitations and assumptions

Bundled Resources

references/frameworks.md

Comprehensive market analysis frameworks including:

  • TAM/SAM/SOM analysis methodology
  • Porter's Five Forces
  • Problem-solution fit criteria
  • Business model assessment frameworks
  • Risk assessment categories
  • Positioning frameworks

When to use: Reference throughout analysis to ensure comprehensive evaluation across all dimensions.

references/research_templates.md

Search query templates and reliable data sources including:

  • Market size research queries
  • Competitive analysis searches
  • Problem validation queries
  • Trend analysis keywords
  • Recommended data sources by category
  • Source quality hierarchy

When to use: During research planning and execution to formulate effective searches and identify authoritative sources.

scripts/market_analyzer.py

Python script for quantitative market analysis:

  • Market metric calculations (TAM/SAM/SOM percentages, growth projections)
  • Unit economics analysis (LTV:CAC, payback period, margins)
  • Viability scoring algorithm
  • Automated report generation

When to use: When quantitative data is available and calculations would strengthen the analysis. Input data via JSON file, outputs calculated metrics and markdown report sections.

Example usage:

python scripts/market_analyzer.py analysis_data.json

Input format:

{
  "startup_name": "Example Startup",
  "market_data": {
    "tam": 10000000000,
    "sam": 2000000000,
    "som": 200000000,
    "current_market_size": 5000000000,
    "growth_rate": 15,
    "years": 5,
    "competition_level": "medium",
    "market_maturity": "growing"
  },
  "business_data": {
    "cac": 500,
    "ltv": 2000,
    "monthly_revenue": 50,
    "revenue": 1000,
    "cost": 300
  }
}

Common Pitfalls to Avoid

  1. Insufficient research: Do not rely on 1-3 searches. Always conduct 10-15+ searches minimum.

  2. Vague conclusions: Avoid statements like "the market is large" without specific numbers.

  3. Missing critical dimensions: Ensure analysis covers market opportunity, competition, problem validation, trends, and business model.

  4. Over-optimism: Present balanced view including real risks and challenges.

  5. Poor source quality: Prioritize primary sources and reputable analysts over blog posts and promotional content.

  6. Ignoring timing: Market readiness and trend timing are critical factors.

  7. No actionable recommendations: Always provide specific next steps for validation.

Example Trigger Phrases

Users may request validation using phrases like:

  • "Validate my startup idea about..."
  • "Is there a market for..."
  • "Analyze the opportunity for..."
  • "Research if people need..."
  • "Check competition for..."
  • "See if my business idea is viable..."
  • "Evaluate this concept..."
  • "Do market research on..."
  • "What's the potential for..."
how to use startup-validator

How to use startup-validator on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add startup-validator
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill startup-validator

The skills CLI fetches startup-validator from GitHub repository ailabs-393/ai-labs-claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/startup-validator

Reload or restart Cursor to activate startup-validator. Access the skill through slash commands (e.g., /startup-validator) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.857 reviews
  • Ira Khan· Dec 24, 2024

    startup-validator is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Chaitanya Patil· Dec 20, 2024

    startup-validator fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ishan Gill· Dec 16, 2024

    startup-validator has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Soo Reddy· Dec 16, 2024

    startup-validator reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Soo Agarwal· Dec 8, 2024

    startup-validator is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ishan Gupta· Nov 27, 2024

    Solid pick for teams standardizing on skills: startup-validator is focused, and the summary matches what you get after install.

  • Noah Sethi· Nov 15, 2024

    Solid pick for teams standardizing on skills: startup-validator is focused, and the summary matches what you get after install.

  • Piyush G· Nov 11, 2024

    Registry listing for startup-validator matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Soo Thomas· Nov 7, 2024

    Keeps context tight: startup-validator is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kaira Verma· Nov 7, 2024

    We added startup-validator from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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