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Product & Design Blueprint

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Validate Startup Ideas With AI Research

0ferdinandobonsMay 2026

Four independent skills covering startup strategy, competitive analysis, positioning, and investor pitches. Works standalone or together.

Design & ValidationCompetitive IntelligencePositioning & Market StrategyInvestor Pitch

Install Prompt

Copy this prompt and paste it into your AI agent to install.

What problem does this solve?

Gives your AI agent the ability to run startup validation from raw idea to investor-ready pitch. Each skill handles a distinct phase: design validates the core idea with research and customer discovery, competitors maps the competitive landscape with real data, positioning builds a defensible market position using established frameworks, and pitch constructs investor narratives in multiple formats. Because the skills share reference materials and can read each other's output, you can run them in any order or all together to build a complete startup assessment.

How does it work?

  1. Describe your startup idea or pick a skill based on what you need (validation, competitive analysis, positioning, or pitch). 2. The skill runs research using web search and structured workflows. Agent-based operation in Claude Code runs waves in parallel; sequential operation in Claude.ai runs them one at a time. 3. It produces markdown deliverables with research findings, strategic frameworks, and actionable outputs. 4. If running multiple skills, each can import prior output and layer deeper analysis on top. Output: a complete validation package or isolated analysis, depending on scope.

Included Skill Groups

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What's the biggest win?

A raw startup idea becomes a validated strategy with research evidence, positioning rationale, and investor-ready pitch. All grounded in real market data, not guessing.

What's required to run this?

Requires Agent tool availability (Claude Code) for parallel research waves, or executes sequentially in Claude.ai. Web search needed for real data; knowledge-based fallback reduces confidence ratings and requires independent verification. Token budgets scale with research depth tier (Light/Standard/Deep). Session state preserved in PROGRESS.md for resuming interrupted workflows.

What should I watch out for?

High token consumption because the skill runs multiple research agents per workflow and produces large documents. Claude Max 5x recommended. Web search must be enabled for real-time data (falls back to knowledge-based mode if unavailable). Customer interviews in startup-design require actual calendar time (1-2 weeks). Some reference materials are lengthy; expect 10+ files per complete session.

Key Features

Multi-format research

Web search agents (or sequential research in Claude.ai) that validate claims across 2-4 research waves per skill

Complete workflows

Each skill produces 4-6 markdown deliverables: research synthesis, strategy docs, battle cards, positioning statements, or pitch scripts

Interoperable design

Skills can read each other's output and build on prior work; run one or all four for increasing depth

Structured frameworks

Lean Canvas, April Dunford positioning, Moore statements, Neumeier Onliness test, JTBD analysis

Radical honesty

Built-in protocols for data labeling, confidence ratings, red flags, and clear verdicts instead of cheerleading

Tools in this Blueprint

Claude logo
4.4(68 reviews)
Claude Code (Agent tool for parallel research)
WebSearch (for live market data)
WebSearch

About This Blueprint

License
MIT
Industry
Technology
Skills
0 workflows, 0 sub-skills, 4 standalone
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