Lean Ux Canvas
Fill an eight-box Lean UX Canvas so solo builders turn vague product ideas into testable hypotheses and small experiments before building.
Overview
Lean UX Canvas is an agent skill most often used in Validate (also Idea research and Build PM) that structures problems, outcomes, and experiments on an eight-box canvas before you commit engineering time.
Install
npx skills add https://github.com/deanpeters/product-manager-skills --skill lean-ux-canvasWhat is this skill?
- Eight-box Lean UX Canvas structure (problem, outcomes, users, benefits, solutions, hypotheses, riskiest assumption, expe
- Worked e-commerce mobile checkout example with quantified conversion gaps
- Hypothesis sentence template tying outcomes to user segments and solutions
- Wizard-of-Oz and experiment framing for cheap validation
- Surfaces riskiest assumption to test first instead of building full features
- 8-box Lean UX Canvas structure
- Example tracks mobile checkout conversion from 45% toward 60%
Adoption & trust: 1.2k installs on skills.sh; 5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You see a business gap—like weaker mobile conversion—but lack a shared, testable picture of users, outcomes, and what to try first.
Who is it for?
Solo founders and PMs scoping a feature or funnel fix who want one page of alignment and a cheap test before implementation.
Skip if: Teams that already have an approved PRD with signed metrics and a scheduled build—skip repeating the canvas unless assumptions changed.
When should I use this skill?
You need structured lean UX framing with hypotheses and a minimal experiment before design or code.
What do I get? / Deliverables
You leave with a filled canvas, a written hypothesis, a flagged riskiest assumption, and a concrete experiment plan ready for prototype or landing validation.
- Completed eight-box Lean UX Canvas
- Hypothesis statement and riskiest assumption
- Experiment description (e.g., wizard-of-oz or usability test)
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Canonical shelf is Validate because the canvas outputs hypotheses, riskiest assumptions, and experiments—the core proof-before-build artifacts. Scope subphase fits framing business problems, outcomes, users, and solution bets before prototype or landing work.
Where it fits
Compare mobile vs desktop conversion anecdotes into a single business-problem box before choosing what to validate.
Lock business outcomes and user segments for a checkout optimization initiative.
Design a wizard-of-oz one-tap checkout UI test from Box 8 experiment notes.
Reconcile engineering backlog items with the hypothesis and riskiest assumption from the canvas.
How it compares
Use instead of unstructured brainstorming notes when you need measurable outcomes and a named experiment, not a generic feature wishlist.
Common Questions / FAQ
Who is lean-ux-canvas for?
Solo builders, indie PMs, and technical founders who need to align problem, users, and validation experiments without a full product org.
When should I use lean-ux-canvas?
In Idea research to frame opportunity, in Validate scope when conversion or retention gaps need hypotheses, and in Build PM when re-scoping a bet mid-sprint.
Is lean-ux-canvas safe to install?
It is documentation-style procedural knowledge with no built-in network or shell hooks; review the Security Audits panel on this page before installing any skill from the registry.
SKILL.md
READMESKILL.md - Lean Ux Canvas
# Lean UX Canvas Examples ### ✅ Good: Mobile Checkout Optimization **Context:** E-commerce company sees mobile traffic surpass desktop, but mobile conversion rate is 15% lower. **Box 1 (Business Problem):** "Mobile traffic now represents 60% of site visits, but mobile checkout conversion rate (45%) is 15% lower than desktop (60%). Our checkout flow wasn't designed for mobile—6 form fields, manual address entry, and 3-step payment. Competitors (Amazon, Shopify) offer one-tap checkout. We're losing revenue." **Box 2 (Business Outcomes):** - Increase mobile checkout conversion rate from 45% to 60% within 3 months **Box 3 (Users):** - Mobile-first millennials (25-35) who order 3+ times per week **Box 4 (User Outcomes & Benefits):** - Complete checkout in <30 seconds without typing (avoid frustration of fat-finger errors on mobile keyboard) **Box 5 (Solutions):** 1. One-tap checkout (Apple Pay, Google Pay) 2. Auto-fill address from device location 3. Save payment method for returning customers **Box 6 (Hypotheses):** - "We believe increasing mobile checkout conversion rate from 45% to 60% will be achieved if mobile-first millennials (25-35) attain faster, friction-free checkout with one-tap Apple Pay integration." **Box 7 (Riskiest Assumption):** - Users will trust one-tap checkout without seeing itemized charges before confirming purchase **Box 8 (Experiment):** - Wizard-of-Oz test: Show one-tap checkout UI, but secretly process payment with existing flow. Measure: Do users click "Pay with Apple Pay"? Do they abandon after seeing the Apple Pay modal? **Why This Works:** - Clear business problem (mobile conversion gap) - Measurable outcome (45% → 60%) - Specific user segment - Testable hypothesis - Smallest experiment (Wizard-of-Oz, not full build) --- ### ❌ Bad: Feature-First Canvas (Solution-Driven) **Box 1 (Business Problem):** "We need to build a recommendation engine." **Why This Fails:** This is a solution, not a problem. What changed? Why does a recommendation engine matter? **Box 2 (Business Outcomes):** "Increase revenue." **Why This Fails:** Too vague. How will you measure? What behavior change indicates success? **Box 5 (Solutions):** "Recommendation engine." **Why This Fails:** Only one solution (the one someone already decided on). No exploration of alternatives. **Box 6 (Hypotheses):** "We believe users will like recommendations." **Why This Fails:** Not testable. Doesn't use the hypothesis template. Doesn't connect business outcome to user benefit. **What Should Have Been Done:** - Start with **what changed** in Box 1 (e.g., "Average order value dropped 20% after we removed upsell banners") - Define **measurable outcome** in Box 2 (e.g., "Increase average order value from $50 to $75") - List **multiple solutions** in Box 5 (e.g., manual upsell banners, AI recommendations, bundle discounts) - Test each solution with a hypothesis --- ### ✅ Good: Enterprise Onboarding Friction **Box 1 (Business Problem):** "Enterprise customers churn after 6 months because onboarding requires 3+ weeks of manual configuration (SSO, permissions, user imports). Competitors offer self-service onboarding. Our CS team spends 40 hours per customer on setup, limiting our ability to scale." **Box 7 (Riskiest Assumption):** "Enterprise IT admins can configure SSO without human support." **Box 8 (Experiment):** "Concierge test: Manually guide 5 enterprise customers through a self-service onboarding wizard prototype (Figma mockup + Loom walkthrough). Measure: Can they complete setup in <3 days without calling support?" **Why This Works:** - Clear problem (manual onboarding blocks scale) - Falsifiable assumption (admins can self-serve) - Minimal experiment (concierge test before building automation) --- --- name: lean-ux-canvas description: Guide teams through Lean UX Canvas v2. Use when framing a business problem, surfacing assumptions, and defining what to learn next. intent: >- Guide product managers through creat