npx skills add ...
npx skills add ailabs-393/ai-labs-claude-skills --skill startup-validator
npx skills add ailabs-393/ai-labs-claude-skills --skill startup-validator
Comprehensive startup idea validation and market analysis tool. Use when users need to evaluate a startup idea, assess market fit, analyze competition, validate problem-solution fit, or determine market positioning. Triggers include requests to "validate my startup idea", "analyze market opportunity", "check if there's demand for", "research competition for", "evaluate business idea", or "see if my idea is viable". Provides data-driven analysis using web search, market frameworks, competitive research, and positioning recommendations.
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.
When a user presents a startup idea, follow this systematic validation process:
Ensure complete understanding before research begins:
Extract key information:
Ask clarifying questions only if critical information is missing:
Do not ask for information you can research independently (market size, competitors, trends).
Based on the idea, create a research plan identifying:
Use templates from references/research_templates.md for query formulation.
Execute systematic research across all dimensions. Always use at least 10-15 web searches to ensure thorough analysis.
Search for:
Query examples:
Search for:
Query examples:
Search for:
Query examples:
Search for:
Query examples:
Search for:
Query examples:
CRITICAL: Use web_fetch to read full articles from authoritative sources (Gartner, McKinsey, Statista, Crunchbase, industry reports) to get detailed data, not just snippets.
After gathering data, analyze using frameworks from references/frameworks.md:
Optional: If quantitative data is available, create a JSON file and use scripts/market_analyzer.py to calculate metrics and generate additional insights.
Clearly articulate:
Develop specific recommendations:
Create a comprehensive markdown report with:
Formatting Guidelines:
references/frameworks.mdreferences/frameworks.mdComprehensive market analysis frameworks including:
When to use: Reference throughout analysis to ensure comprehensive evaluation across all dimensions.
references/research_templates.mdSearch query templates and reliable data sources including:
When to use: During research planning and execution to formulate effective searches and identify authoritative sources.
scripts/market_analyzer.pyPython script for quantitative market analysis:
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:
Input format:
Insufficient research: Do not rely on 1-3 searches. Always conduct 10-15+ searches minimum.
Vague conclusions: Avoid statements like "the market is large" without specific numbers.
Missing critical dimensions: Ensure analysis covers market opportunity, competition, problem validation, trends, and business model.
Over-optimism: Present balanced view including real risks and challenges.
Poor source quality: Prioritize primary sources and reputable analysts over blog posts and promotional content.
Ignoring timing: Market readiness and trend timing are critical factors.
No actionable recommendations: Always provide specific next steps for validation.
Users may request validation using phrases like:
python scripts/market_analyzer.py analysis_data.json{
"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
}
}