What is the Lean UX skill for Claude Code?

It is a specialized capability that helps designers and developers apply the Lean UX methodology to validate design decisions through structured hypothesis testing and experiments.

What kind of experiments can I design with this skill?

It facilitates the design of various MVP experiments including A/B tests, fake door tests, concierge MVPs, Wizard of Oz prototypes, and paper prototype tests.

When should I use Lean UX vs. Heuristic Evaluation?

Use Lean UX for forward-looking hypothesis testing of new concepts or changes. Use Heuristic Evaluation (ux-heuristic-eval) for auditing the usability of existing interfaces against established standards.

How does assumption mapping work in this skill?

The skill identifies and prioritizes product unknowns based on risk and certainty, helping your team focus on testing the most critical design assumptions first.

Lean UX Hypothesis & Design

Name: Lean UX Hypothesis & Design
Author: geekatron

bygeekatron

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Herramientas de Diseño

Facilitates hypothesis-driven design cycles using Lean UX methodology to validate product assumptions through Build-Measure-Learn experiments.

The ux-lean-ux skill empowers product teams to move beyond opinion-based design by implementing the Jeff Gothelf and Josh Seiden Lean UX framework within Claude Code. It provides structured guidance for creating hypothesis backlogs, mapping high-risk assumptions, and designing MVP experiments such as A/B tests or concierge prototypes. By automating the documentation of validated learning, this skill helps teams reduce development waste and ensure that design iterations are driven by measurable evidence and user behavior.

Características Principales

01MVP experiment design with defined success criteria

02Four-quadrant assumption mapping for risk prioritization

0316 GitHub stars

04Full Build-Measure-Learn cycle tracking and facilitation

05Validated learning log management for knowledge accrual

06Hypothesis-driven design structuring and backlogs

Casos de Uso

01Prioritizing product unknowns to reduce engineering waste

02Validating high-risk design changes before full implementation

03Building an organizational learning repository from UX experiments

Características Principales

01MVP experiment design with defined success criteria

02Four-quadrant assumption mapping for risk prioritization

0316 GitHub stars

04Full Build-Measure-Learn cycle tracking and facilitation

05Validated learning log management for knowledge accrual

06Hypothesis-driven design structuring and backlogs

Casos de Uso

01Prioritizing product unknowns to reduce engineering waste

02Validating high-risk design changes before full implementation

03Building an organizational learning repository from UX experiments