foundations-validation

Validate assumptions and design experiments to de-risk product decisions.

37|5|Updated Nov 18, 2025

Search Tags:#learning #experiment design #assumptions #risk reduction #hypothesis #MVP validation

Ready to run

npx skills add https://github.com/BellaBe/lean-os --skill foundations-validation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: foundations-validation
Source: https://github.com/BellaBe/lean-os/tree/main/.claude/skills/foundations-validation
Command: npx skills add https://github.com/BellaBe/lean-os --skill foundations-validation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams map assumptions, design experiments, and test MVPs to minimize risk and accelerate learning.

Core Features & Use Cases

Assumption mapping: identify critical bets, rank by risk, and define kill criteria.
Experiment design: craft rigorous, testable experiments with success criteria.
MVP validation: validate MVP assumptions before heavy investment.

Quick Start

Example: "Create an assumption map for our problem X and outline a 2-week experiment plan to test the top risky assumption."

Frequently Asked Questions about foundations-validation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I validate product assumptions before building?▼

Assumption validation identifies your critical bets upfront, ranks them by risk, and defines kill criteria to stop wasting resources on unproven ideas. Map assumptions early, then design experiments with clear success metrics to test the riskiest ones first.

What's the best way to design an experiment for MVP validation?▼

Experiment design for MVP validation requires defining testable hypotheses, success criteria, and measurable outcomes before launch. Structure experiments with clear kill criteria so you learn whether to pivot, persevere, or stop before heavy investment.

How do I prioritize which assumptions to test first?▼

Risk-ranked assumption mapping identifies which bets pose the greatest threat to your product's success. Prioritize high-impact, high-uncertainty assumptions and test those before lower-risk bets to accelerate learning and reduce wasted effort.

Can I use assumption mapping for iterative product learning?▼

Yes. Assumption mapping, experiment design, and MVP validation work across discovery, validation, and iterative learning phases. As you test and learn, update your assumption register and design new experiments based on results to continuously de-risk decisions.

What outputs do I get from structuring an assumption and experiment plan?▼

Structured outputs include assumption registers that catalog your bets, kill criteria that define failure thresholds, success metrics that measure learning, and experiment portfolios that organize your test plan. These artifacts create accountability and track progress.