Skip to main content Accueil Créateurs comeonoliver skillshub lean-startup
lean-startup Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", or "test assumptions". Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done. Trigger with 'lean', 'startup'.
Aller à l'installation Skills Marketplace Découvrez et explorez les compétences IA créées par la communauté.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Afficher les détails du prompt Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
npx skills add https://github.com/ComeOnOliver/skillshub --skill lean-startupLa commande reste sur une seule ligne. Faites défiler horizontalement pour la vérifier avant de la copier.
Vous préférez une copie locale ? Téléchargez les fichiers actuellement disponibles dans SkillsMP.
Télécharger Zip Téléchargement... Plus depuis ce dépôt Review product and feature risk before an AI coding agent starts implementation.
Use Xquik for X data and confirmation-gated X actions: tweet search, user lookup, follower export, media download, monitors, webhooks, MCP, and SDK workflows.
Canton Network open-source ecosystem guide covering DAML SDK, Canton runtime, and Splice applications. Use when working with Canton Network, DAML smart contracts, or building decentralized applications.
Métiers associés SOC
Basé sur la classification professionnelle SOC
name lean-startup description Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", or "test assumptions". Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done. Trigger with 'lean', 'startup'.
allowed-tools Read, Glob, Grep license MIT author Wondelai <hello@wondelai.com> version 1.0.1 compatible-with claude-code, codex, openclaw tags ["productivity","testing","lean-startup"]
Lean Startup Methodology
A systematic approach to building startups and launching new products that shortens development cycles and rapidly discovers if a business model is viable.
Core Principle
Entrepreneurship is a form of management. Success doesn't require a perfect plan or brilliant insight—it requires a systematic process for testing assumptions, learning from customers, and iterating rapidly.
The foundation: Most startups fail not because they couldn't build what they planned, but because they built the wrong thing. The Lean Startup methodology applies scientific experimentation to eliminate waste and accelerate validated learning.
Scoring
Goal: 10/10. When reviewing or creating product development plans, experiments, or metrics, rate them 0-10 based on adherence to Lean Startup principles. A 10/10 means full application of Build-Measure-Learn, validated learning, and evidence-based decisions; lower scores indicate waterfall thinking or waste. Always provide the current score and specific improvements needed to reach 10/10.
The Build-Measure-Learn Loop
The fundamental cycle of Lean Startup:
IDEAS
↓
BUILD → Product
↓
MEASURE → Data
↓
LEARN → Knowledge
↓
(back to IDEAS)
Critical insight: The loop is actually backward. Start with what you want to learn, determine metrics that will inform that learning, then build the minimum product to collect those metrics.
Reverse planning:
What do we want to learn? (hypothesis to test)How will we know if we learned it? (metrics)What's the minimum we can build? (MVP)
Goal: Minimize total time through the loop.
See: references/build-measure-learn.md for detailed loop execution.
Validated Learning
Definition: Learning what customers really want through validated experiments, not opinion or anecdotes.
Validated learning is not:
Building features customers request (they don't know what they want)
Achieving vanity metrics (downloads, signups without engagement)
Doing surveys or focus groups (people lie/mispredict behavior)
Validated learning is:
Testing hypotheses with real behavior
Measuring what customers do , not what they say
Running experiments that could falsify your assumptions
Learning = when your predictions were wrong Level Evidence Strength 1 "I think customers want this" Weakest (opinion) 2 "Customers said they want this" Weak (stated preference) 3 "Customers signed up for early access" Medium (low commitment) 4 "Customers paid a deposit" Strong (real commitment) 5 "Customers are actively using it" Strongest (revealed preference)
Target: Level 4-5 before building at scale.
Minimum Viable Product (MVP) Definition: The version of a new product that allows a team to collect the maximum amount of validated learning with the least effort.
A prototype (not about proving technical feasibility)
A beta version (not about quality or features)
A minimum marketable product (it might be embarrassing)
A learning vehicle
The smallest experiment to test a hypothesis
Often much smaller than you think
Type What It Is When to Use Example Concierge Manual service pretending to be automated Test if solution is valuable Food on the Table (manual meal planning) Wizard of Oz Fake automation, manual backend Test if automation is needed Zappos (no inventory, bought shoes retail) Smoke test Landing page + signup, no product Test demand before building Dropbox video (explained concept, measured signups) Single feature One core feature only Test which feature is most valuable Twitter (just status updates) Piecemeal Combine existing tools Test workflow before custom build Groupon (WordPress + email)
What's the riskiest assumption to test first?
What's the minimum to test that assumption?
How do we measure if the assumption was validated?
Building too much (overestimate MVP size)
Optimizing for scale prematurely
Confusing quality with learning (MVP can be low quality)
Skipping the experiment (building without hypothesis)
Leap-of-Faith Assumptions Definition: The assumptions that, if wrong, will cause your business to fail.
Identify your business model's critical assumptions Prioritize by risk (which failure would be fatal?)Test the riskiest assumption first Common leap-of-faith assumptions:
Assumption Type Question Test Method Value hypothesis Do customers care about this problem? Smoke test, concierge MVP Growth hypothesis How will customers discover us? Channel tests, referral experiments Retention hypothesis Will customers come back? Cohort analysis, engagement metrics Monetization hypothesis Will customers pay? Pre-orders, pricing tests
Leap-of-faith: "People will download and use a file sync tool"Test: Explainer video showing product (before building full version)Metric: Beta signup list grew from 5,000 to 75,000 overnightLearning: Validated demand before building scale infrastructure Anti-pattern: Testing assumptions in order of ease rather than risk.
Innovation Accounting Definition: Measuring progress when traditional accounting doesn't apply.
The problem with traditional metrics:
Revenue (startups start at $0)
Customers (startups start at 0)
Vanity metrics (look good but don't drive decisions)
Innovation accounting framework:
1. Establish the Baseline Question: Where are we today?
Measure current reality, even if it's zero or embarrassing.
Conversion funnel (signup → active → retained → paying)
Engagement (DAU/MAU, session length, features used)
Economics (CAC, LTV, churn rate)
Goal: Know your starting point precisely.
2. Tune the Engine Question: What can we improve to move toward our goal?
Run experiments to improve baseline metrics.
A/B test pricing ($9/mo vs. $19/mo)
Test onboarding flows (% who complete setup)
Experiment with channels (SEO vs. paid vs. referral)
Goal: Systematically improve metrics through validated learning.
3. Pivot or Persevere Question: Are we making sufficient progress, or do we need to change strategy?
Based on data, decide whether to continue or pivot.
Are metrics moving in the right direction?
Is the rate of improvement acceptable?
Are we learning what we expected?
Goal: Make evidence-based strategic decisions.
Actionable vs. Vanity Metrics Vanity metrics: Make you feel good but don't change behavior.
Actionable metrics: Drive decisions and clarify cause and effect.
Vanity Why It's Bad Actionable Alternative Total signups Always goes up, no context % signup → active (conversion rate)Page views Doesn't indicate value Time on page , bounce rate Total users Includes inactive/churned Active users (DAU, WAU, MAU)Downloads Doesn't mean usage DAU/downloads (activation rate)Revenue Without context Revenue per cohort , LTV/CAC
Three characteristics of actionable metrics:
Actionable: Clear cause-and-effect (can reproduce)Accessible: Simple, understandable by everyoneAuditable: Can check the underlying data (not a black box)
Vanity: "We have 100,000 users!"Actionable: "Users from channel X have 2x retention vs. channel Y. Let's double down on X." Cohort analysis: Group users by signup date and track behavior over time. Reveals if product is actually improving.
Pivot or Persevere Pivot: A structured course correction designed to test a new hypothesis about the product, strategy, or engine of growth.
Experiments consistently fail to validate hypotheses
Metrics are flat despite multiple iterations
Customer feedback contradicts your vision
Progress is too slow given runway
Metrics are improving (even if slowly)
Clear learning is happening
Adjustments are moving in right direction
Pivot Type What Changes Example Zoom-in pivot Single feature becomes the whole product Instagram (photo filters from Burbn check-in app) Zoom-out pivot Product becomes a single feature Flickr (photo-sharing from Game Neverending) Customer segment Same problem, different customer Groupon (activism platform → local deals) Customer need Same customer, different problem Potbelly Sandwich (antique store → sandwiches) Platform App → Platform or Platform → App YouTube (dating site → video platform) Business architecture High margin, low volume ↔ Low margin, high volume Salesforce (software → SaaS) Value capture Monetization model change Android (paid → free + app revenue) Engine of growth Viral, sticky, or paid growth model Facebook (viral within colleges → paid advertising) Channel How you reach customers Salesforce (direct sales → self-service) Technology Different technology, same solution Apple (Intel → ARM chips)
Pivot cadence: Many successful startups pivot 1-5 times before finding product-market fit.
Anti-pattern: "Pivot" without validating that the new direction solves the core problem.
The Three Engines of Growth Growth engine: How your startup acquires and retains customers sustainably.
Choose one engine to focus on:
1. Sticky Engine of Growth Mechanism: High retention, low churn
Formula: Growth rate = New customer acquisition rate - Churn rate
Focus: Keep customers coming back
Churn rate (% who stop using per month)
Retention cohorts (% still active after 30/60/90 days)
Engagement (DAU/MAU ratio)
Examples: SaaS, subscription services, social networks
Strategy: Improve product until churn rate is low enough that natural growth exceeds churn.
2. Viral Engine of Growth Mechanism: Customers bring other customers
Formula: Viral coefficient = (% who invite) × (invites sent) × (% who join)
Focus: Viral coefficient > 1.0 = exponential growth
Viral coefficient (invites → signups)
Viral cycle time (how long until referred user invites others)
Referral source attribution
Examples: Dropbox, Hotmail, WhatsApp
Strategy: Build virality into the product. Must be > 1.0 to be self-sustaining.
3. Paid Engine of Growth Mechanism: Spend money to acquire customers
Formula: LTV (Lifetime Value) > CAC (Customer Acquisition Cost)
Focus: Unit economics that allow reinvestment
CAC (cost per acquisition)
LTV (average revenue per customer)
LTV/CAC ratio (target: > 3x)
Payback period (how long to recoup CAC)
Examples: E-commerce, traditional businesses
Strategy: Optimize until each customer generates enough profit to acquire more customers.
Warning: Don't use multiple engines simultaneously. Pick one, optimize it, then consider adding others.
The Five Whys Purpose: Root cause analysis to prevent problems from recurring.
A problem occurs (bug, outage, customer complaint)
Ask "Why did this happen?" → Answer
Ask "Why?" about that answer → Second answer
Repeat 5 times until you reach the root cause
Make proportional investments at each level
Problem: Website went down
Why? Server ran out of memoryWhy? Memory leak in new featureWhy? Code wasn't reviewed for memory managementWhy? No code review process for infrastructure changesWhy? Team is moving too fast to create processes Proportional investments:
Fix the immediate bug (level 1)
Add memory monitoring (level 2)
Implement code review (level 3-4)
Slow down to build quality processes (level 5)
Anti-pattern: Stop at level 1 (just fix the symptom).
Small Batches Principle: Work in small batches to accelerate learning and reduce waste.
Faster feedback loops
Easier to pivot
Less waste when you're wrong
Faster time to market
Large Batch Small Batch Build entire product, then launch Launch landing page, then build Release quarterly Release weekly or daily Plan 12-month roadmap Plan 6-week cycles Big bang rewrite Incremental refactoring
Continuous deployment: The ultimate small batch = deploy every code commit.
Bugs are caught immediately
Learning happens continuously
Reduced risk per deployment
Lean Startup Applied
SaaS Startup
Smoke test: Landing page + email list (validate demand)Concierge MVP: Manually deliver service to 10 customers (validate value)Single-feature MVP: Build one core workflow (validate engagement)Measure: Retention, NPS, feature usagePivot or scale: Based on cohort data
Corporate Innovation
Innovation accounting: Separate metrics from core businessProtected teams: Shield from quarterly revenue pressureMetered funding: Unlock funding based on validated learning milestonesInternal entrepreneurship: Treat team as startup within company
Product Features
Feature flags: Deploy behind flag, test with small cohortA/B test: Measure impact on core metricsKill, iterate, or scale: Based on data
Common Mistakes Mistake Why It Fails Fix Building too much Waste before validation Test with smoke test or concierge first Asking customers People don't know/mispredict Observe behavior, not opinions Vanity metrics Feel-good numbers, no decisions Track cohorts, conversion, retention No hypothesis Can't learn if you don't predict Write hypothesis before each experiment Pivot too slow Waste runway Set clear pivot criteria upfront Skip innovation accounting Can't tell if you're improving Establish baseline, measure tuning efforts
Quick Diagnostic Audit any product development plan:
Question If No Action What's the riskiest assumption? You're building on shaky ground Map leap-of-faith assumptions How will you test it? You're guessing Design MVP to test assumption What metric will validate/invalidate? You won't learn Define actionable metrics Can you test with less than this? You're over-building Shrink MVP further What will you do if the experiment fails? No pivot criteria Define pivot triggers upfront
The Lean Startup Applied: From Idea to Scale Phase 1: Problem/Solution Fit
Goal: Validate the problem exists and customers careMethod: Customer discovery, smoke tests, concierge MVPMetric: Customers willing to pay or commit Phase 2: Product/Market Fit
Goal: Build something people wantMethod: Build MVP, iterate based on usage dataMetric: High retention, organic growth, strong engagement
Goal: Grow efficientlyMethod: Optimize growth engine, improve unit economicsMetric: Sustainable, profitable growth Anti-pattern: Skipping Phase 1-2 and jumping straight to scale.
Reference Files
build-measure-learn.md : Detailed loop execution, reverse planningmvp-design.md : MVP types, design patterns, sizingassumptions.md : Leap-of-faith assumption mappinginnovation-accounting.md : Metric frameworks, dashboardsmetrics.md : Actionable vs. vanity, cohort analysis, metric selectionpivots.md : Pivot types, decision frameworks, case studiesgrowth-engines.md : Sticky, viral, paid engines in depthfive-whys.md : Root cause analysis, facilitation guidessmall-batches.md : Batch size reduction, continuous deploymentapplications.md : SaaS, corporate innovation, featurescase-studies.md : Dropbox, IMVU, Zappos, Groupon, and failures
Further Reading This skill is based on Eric Ries' Lean Startup methodology. For the complete framework, research, and case studies:
Overview A systematic approach to building startups and products that shortens development cycles by applying the Build-Measure-Learn loop, validated learning, and innovation accounting to eliminate waste.
Prerequisites
A product idea or business hypothesis to validate
Access to target customers for experiments (even a small initial group)
Ability to define and measure actionable metrics (conversion, retention, engagement)
Instructions
Identify leap-of-faith assumptions — use the Quick Diagnostic table to audit your plan
Design the smallest MVP to test the riskiest assumption first
Define actionable metrics and run the Build-Measure-Learn loop
Use innovation accounting to evaluate progress and decide pivot-or-persevere
Output
Hypothesis card : Assumption, test method, success metric, and pivot criteriaMVP specification : Type (concierge, smoke test, single-feature, etc.) and scopeExperiment results : Baseline metrics, tuning outcomes, and pivot-or-persevere recommendation
Error Handling Error Cause Resolution No clear hypothesis Building without a testable assumption Map leap-of-faith assumptions before designing MVP Vanity metrics only Tracking totals instead of cohorts Switch to actionable metrics (conversion, retention, LTV/CAC) MVP too large Over-engineering before validation Apply the "can you test with less?" filter from Quick Diagnostic
Examples
Smoke test : Landing page with signup form to validate demand before building any product (Dropbox pattern).Concierge MVP : Manually deliver the service to 10 customers to validate the value hypothesis before automating.
Resources 