skills/marketing/startup-validator/SKILL.md
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.
npx skillsauth add pedronauck/skills startup-validatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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:
# [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:
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:
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
}
}
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:
tools
Plans real-user QA deliverables: personas, journey maps, exploratory charters, persona/journey/tour/CFR test cases, regression suites, Figma validation checks, automation intent, and user-impact bug reports. Writes artifacts under <qa-output-path>/qa/ for qa-execution to consume. Use when planning QA before execution, documenting journey-driven test strategy, marking flows that need E2E follow-up, or filing structured bug reports. Do not use for live execution, AI implementation audits, CI gate ownership, or technical integration/security/performance suites; use qa-execution or agent-output-audit instead.
development
Executes real-user QA sessions through public interfaces using personas, journeys, exploratory charters, test tours, edge-case probes, CFR checks, and browser evidence. Reads qa-report artifacts from <qa-output-path>/qa/ when present, captures issues/screenshots/reports under the same output tree, and classifies bugs by user impact. Use when validating a release candidate, migration, refactor, or user-facing change against production-like behavior. Do not use for AI implementation audits, task-status reconciliation, CI gate runs, integration/security/performance templates, or flaky-test triage; use agent-output-audit for those.
development
Transform outside-of-diff review files into properly formatted issue files for a given PR. Use when converting review files from ai-docs/reviews-pr-<PR>/outside/ into issue format in ai-docs/reviews-pr-<PR>/issues/. Automatically determines starting issue number and preserves all metadata (file path, date, status) from original review files. Don't use for inline-diff review files, non-PR review artifacts, or creating GitHub issues directly.
development
Enforce root-cause fixes over workarounds, hacks, and symptom patches in all software engineering tasks. Use when debugging issues, fixing bugs, resolving test failures, planning solutions, making architectural decisions, or reviewing code changes. Activates gate functions that detect and reject common workaround patterns such as type assertions, lint suppressions, error swallowing, timing hacks, and monkey patches. Don't use for trivial formatting changes or documentation-only edits.