mvp-validator

name	mvp-validator
description	Expert-level MVP product validation skill that performs deep analysis before committing development resources. This skill should be used when evaluating a new product idea, requirement document, or business concept before investing in development. It produces a structured 6-module validation framework: requirement analysis, technical feasibility, competitive landscape, open-source solution research, MVP progressive investment plan, and ROI prediction with risk matrix. Triggers include: MVP validation, product value proposition, validate before building, product feasibility, should I build this, MVP落地框架, 产品价值验证, 开发前评估, 竞品分析, 开源方案调研, ROI预测, or any request to evaluate whether a product idea is worth investing development resources into. Also triggers when a user provides a requirement document (PDF/Word/MD) and asks for assessment before development.
agent_created	true

MVP Validator

Overview

Validate product ideas before committing development resources. This skill produces a structured 6-module assessment that answers three questions: (1) Is this worth building? (2) Can it be built, and how? (3) What is the minimum viable investment path with the best risk-adjusted ROI?

When to Use

• A user provides a requirement document, product spec, or job description and asks for assessment before committing to development.
• A user asks "should I build this?" or "is this product idea viable?"
• A user wants competitive analysis, open-source solution research, or ROI prediction for a product concept.
• A user explicitly requests "MVP validation" or "product value proposition validation."

Workflow

Execute the following 6 modules in order. Each module builds on the previous one. After each module, present findings to the user before continuing. Load references/mvp-detailed-guide.md for detailed templates, search query patterns, and calculation tables.

Module 1: Requirement Interpretation

Goal: Understand what the product actually does, stripping away jargon.

1. If a document (PDF/Word/MD) is provided, read it fully first.
2. Summarize the core value chain in one sentence: "User does X -> System does Y -> User gets Z."
3. Identify explicit requirements vs. implicit assumptions.
4. List what the document does NOT ask for (negative space analysis) - these gaps often reveal product scope boundaries and future expansion potential.
5. Identify the single biggest technical risk.

Output: A concise requirement summary with core value chain, scope boundaries, and top technical risk.

Module 2: Technical Feasibility Assessment

Goal: Determine if the product can be built and with what technology.

1. Break down the product into functional layers (e.g., CV pipeline, backend API, frontend rendering).
2. For each layer, identify candidate technology stacks.
3. Assess build difficulty (Easy / Medium / Hard / Very Hard) for each layer.
4. Identify which layers can be AI-generated vs. which require human expertise.
5. If the user asks about a specific technology (e.g., "what is CV pipeline?", "why Docker?"), provide a plain-language explanation with visual diagrams.

Output: Technology stack recommendation with difficulty assessment and AI-vs-human work split.

Visual: Generate an architecture diagram using show_widget (load diagram module first via read_me).

Module 3: Competitive Analysis

Goal: Map the competitive landscape and identify market gaps.

1. Search the web for competing products using WebSearch. Use query_keyword_groups with 3-5 keyword variants (English + Chinese) to broaden coverage.
2. For each competitor found, fetch detailed info via WebFetch to extract: pricing, features, platform, accuracy/quality claims.
3. Position all competitors on a 2D matrix: X-axis = one capability dimension, Y-axis = another capability dimension specific to the product domain.
4. Identify the market gap: what combination of capabilities does NO existing product offer?
5. Assess whether the gap represents real unmet demand or just an empty niche.

Output: Competitor comparison table + market gap identification.

Visual: Generate a competitive landscape diagram using show_widget.

Search patterns: See references/mvp-detailed-guide.md Section 3 for domain-specific search keyword templates.

Module 4: Open-Source Solution Research

Goal: Find existing open-source tools that can be adapted instead of building from scratch.

1. Search GitHub, PyPI, npm for relevant open-source projects:
   • WebSearch with queries like "open source [domain] [technology] github"
   • WebFetch GitHub repo pages to extract: stars, license, last update, language, installation method, accuracy/precision metrics.
   • WebFetch PyPI/npm pages for package details.
2. For each candidate, evaluate:
   • Precision/Quality: Does it meet the product's quality bar?
   • License: AGPL/GPL = risk for commercial use; MIT/Apache/BSD = safe.
   • Maintainability: Can it be called as a library, or does it need modification?
   • Integration effort: How much glue code is needed?
3. Propose a combination architecture: which open-source tools to use for each layer, plus what custom code must be written.
4. Calculate effort savings: self-build vs. adapt open-source (time, cost, code volume, risk reduction).

Output: Open-source tool comparison table + recommended combination architecture + savings analysis.

Visual: Generate a 3-layer combination architecture diagram using show_widget.

License risk guide: See references/mvp-detailed-guide.md Section 4.

Module 5: MVP Progressive Investment Framework

Goal: Design a phased investment plan with Go/No-Go gates.

Design 4 phases with clear gates:

Phase	Goal	Typical Cost	Duration	Gate Decision
Phase 0: Tech Verification	Prove core tech works	~0 (AI code)	1 week	Does the core pipeline produce acceptable results?
Phase 1: User Validation	Confirm demand exists	~0	1-2 weeks	Do 20+ target users say they would pay?
Phase 2: MVP Product	Build usable product	Low	4-5 weeks	Do early users retain after 1 month?
Phase 3: Full Product	Scale to commercial grade	Medium-High	8-12 weeks	Is unit economics positive?

Key principle: Maximum loss if killed at each gate should decrease exponentially. Phase 0-1 cost should be near zero.

For each phase, specify:

• What to keep (core functionality needed for validation)
• What to cut (features that don't affect the validation conclusion)
• Go/No-Go criteria (specific, measurable)
• If killed, what was lost (time + money)

Output: 4-phase investment plan with Go/No-Go gates and keep/cut lists.

Visual: Generate a progressive investment flow diagram using show_widget.

Module 6: ROI Prediction and Risk Matrix

Goal: Quantify the financial upside and downside.

1. Cost model: Break down costs by category (human resources, cloud infrastructure, other). Provide 3 tiers: Conservative / Standard / Premium.
2. Revenue model: Define 3 scenarios: Pessimistic / Realistic / Optimistic. For each, specify: user count, pricing, annual revenue.
3. Break-even analysis: Calculate months to break-even for each scenario.
4. ROI calculation: 3-year and 5-year ROI for each scenario.
5. Risk matrix: List top 4-5 risks with probability, impact, and mitigation.
6. MVP risk-hedging value: Compare "all-in" vs "phased" expected loss.

Key insight to highlight: If the product is a single feature (e.g., "coloring only"), ROI tends toward pessimistic. If the core technology serves as platform infrastructure enabling multiple features, ROI can jump 5-10x. Always call out this distinction.

Output: Cost-revenue table, break-even analysis, risk matrix, and strategic recommendation.

Visual: Generate an ROI chart showing cumulative investment vs. revenue lines for 3 scenarios using show_widget.

Output Format

After completing all 6 modules, produce a final summary with:

1. One-sentence verdict: "This product is worth / not worth / conditionally worth investing in, because..."
2. Top 3 actionable next steps.
3. Single biggest risk and its mitigation.
4. Recommended investment tier (Conservative / Standard / Premium) and expected timeline.

If the user provided a document, offer to generate a complete Word report covering all 6 modules.

Important Notes

• Always search the web for competitors and open-source tools - do not rely on training data alone, as the landscape changes rapidly.
• Use query_keyword_groups in WebSearch to cover multiple angles in a single call.
• Always check license compatibility when recommending open-source tools for commercial products.
• Be honest about AI's capabilities: AI can write 70-80% of code, but precision tuning, real-world testing, and domain-specific validation require human engineers.
• Tailor all cost estimates to the user's market context (China vs. international, seniority levels).
• Present visual diagrams (show_widget) at key decision points, not just text.