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Startup Validator

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ailabs-393
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.

Overview

Publisherailabs-393
Repositoryai-labs-claude-skills
Skill namestartup-validator
Stars
447
Forks
115
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ailabs-393 on GitHub. Read the source before you install it.

Installation

Install the Startup Validator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ailabs-393/ai-labs-claude-skills.git /tmp/ai-labs-claude-skills
mkdir -p .claude/skills
cp -r /tmp/ai-labs-claude-skills/packages/skills/startup-validator .claude/skills/startup-validator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Startup Validator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Startup Validator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Startup Validator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

markdown
# [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:

bash
python scripts/market_analyzer.py analysis_data.json

Input format:

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
  }
}

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..."

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Startup Validator AI skill do?

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.

Why use Startup Validator on TypingMind?

Because you install it once and use it with any model. Startup Validator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Startup Validator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ailabs-393/ai-labs-claude-skills/tree/main/packages/skills/startup-validator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Startup Validator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Startup Validator?

As many as you like. As long as a model supports skills, you can use Startup Validator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Startup Validator AI skill free?

Yes. It is published on GitHub by ailabs-393 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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