Mmcp.market

lean-startup skill

by wondelai·wondelai/skills·2.3k stars·MIT

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", "test assumptions", "innovation accounting", "build-measure-learn", "minimum viable experiment", "should we pivot", "test a business idea cheaply", or "build the smallest version first". Also trigger when deciding what to include in a first version, measuring startup progress, or evaluating whether to change direction on a product bet. Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done.

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Install the lean-startup skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/wondelai/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -r /tmp/skills/lean-startup ~/.claude/skills/lean-startup
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Lean Startup Methodology

A systematic approach to building startups and launching new products that shortens development cycles and rapidly discovers whether 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. Most startups fail not because they couldn't build what they planned, but because they built the wrong thing: treat every plan as a set of hypotheses to falsify, and spend effort to eliminate waste and accelerate validated learning, not to execute a fixed roadmap.

Scoring

Goal: 10/10. Score a plan, experiment, or metric set by the five Quick Diagnostic rows—1 point each when the answer is yes, 2 points when it is also backed by evidence on the Validation Ladder (Level 3+):

  • 9-10: every leap-of-faith assumption named and ranked by risk, the riskiest tested by a real MVP, actionable metrics defined, and explicit pivot criteria set before building.
  • 5-6: a hypothesis and some MVP exist, but metrics are vanity or pivot criteria are undefined—decisions can't be made from the data.
  • ≤3: waterfall thinking—building the full product first, asking customers what they want, or scaling before product/market fit.

State the current score and the lowest-scoring diagnostic row to fix next.

The Build-Measure-Learn Loop

The fundamental cycle: IDEAS → BUILD (product) → MEASURE (data) → LEARN (knowledge) → back to IDEAS.

Critical insight: Plan the loop backward:

  1. What do we want to learn? (hypothesis to test)
  2. How will we know if we learned it? (metrics)
  3. What's the minimum we can build? (MVP)

Goal: Minimize total time through the loop.

See references/build-measure-learn.md when planning an experiment—reverse-planning sequence, an experiment-design template, per-product-type loop examples, and the build/vanity-metric loop traps.

Validated Learning

Learning what customers really want through experiments on real behavior—not feature requests, surveys, or focus groups (people mispredict their own behavior). Measure what customers do, not what they say, and run experiments that could falsify your assumptions. Vanity wins (downloads, signups without engagement) are not learning.

The Validation Ladder:

Target: Level 4-5 before building at scale.

Minimum Viable Product (MVP)

The version of a new product that allows maximum validated learning with the least effort. Not a prototype (technical feasibility), not a beta (quality), not a minimum marketable product—a learning vehicle, often embarrassingly small and low quality, and usually much smaller than you think.

MVP Types:

Design questions: What's the riskiest assumption? What's the minimum that tests it? How do we measure whether it was validated?

See references/mvp-design.md when choosing and sizing an MVP—seven types in depth, a type-selection decision matrix, lower/upper sizing bounds, and the MVP Design Canvas.

Leap-of-Faith Assumptions

The assumptions that, if wrong, will cause your business to fail. Identify them, prioritize by risk (which failure would be fatal?), and test the riskiest first—never in order of ease.

Example—Dropbox: Leap of faith: "people will download and use a file sync tool." Test: explainer video before building scale infrastructure. Result: beta list grew from 5,000 to 75,000 overnight—demand validated.

See references/assumptions.md when mapping and ranking assumptions—the Impact-Uncertainty matrix, a prioritization scoring template, test methods per assumption type, and industry-specific assumption lists.

Innovation Accounting

Measuring progress when traditional metrics fail: revenue and customers start at zero, and vanity metrics look good without driving decisions.

1. Establish the Baseline

Measure current reality precisely, even if it's zero or embarrassing: conversion funnel (signup → active → retained → paying), engagement (DAU/MAU, session length, features used), economics (CAC, LTV, churn).

2. Tune the Engine

Run experiments to improve baseline metrics: A/B test pricing ($9 vs. $19/mo), onboarding completion rates, acquisition channels (SEO vs. paid vs. referral). Each experiment targets a measurable improvement through validated learning.

3. Pivot or Persevere

When tuning stalls, make the evidence-based call (criteria and pivot types below in Pivot or Persevere).

See references/innovation-accounting.md when building the baseline dashboard—funnel, cohort, and economics metric frameworks.

Actionable vs. Vanity Metrics

Vanity metrics make you feel good but don't change behavior; actionable metrics drive decisions and clarify cause and effect.

Three characteristics of actionable metrics: actionable (clear cause-and-effect, reproducible), accessible (simple, understood by everyone), auditable (underlying data can be checked).

Example: Vanity: "We have 100,000 users!" Actionable: "Channel X users retain 2x better than channel Y—double down on X."

Cohort analysis: Group users by signup date and track behavior over time—the only way to see whether the product is actually improving.

See references/metrics.md when building a cohort table or choosing what to track—a five-step cohort walkthrough and AARRR (Pirate Metrics) aligned with Lean Startup stages.

Pivot or Persevere

A pivot is a structured course correction designed to test a new hypothesis about the product, strategy, or engine of growth.

Pivot when: experiments repeatedly fail to validate hypotheses, metrics stay flat despite iterations, customer feedback contradicts the vision, or progress is too slow for the runway. Persevere when: metrics are improving (even slowly), clear learning is happening, and adjustments move the right direction.

Pivot Types:

Cadence: Successful startups commonly pivot 1-5 times before product-market fit. Anti-pattern: "pivoting" without validating that the new direction solves the core problem.

See references/pivots.md when the data suggests a pivot—the data-driven pivot signals, a structured pivot-meeting agenda, leading indicators, and the Instagram/Slack/YouTube pivot stories.

The Three Engines of Growth

How a startup acquires and retains customers sustainably. Pick one engine, optimize it, then consider adding others—running multiple engines simultaneously dilutes focus and learning.

1. Sticky Engine of Growth

Retention-driven: growth rate = new customer acquisition rate − churn rate. Track churn rate, retention cohorts (30/60/90 days), and DAU/MAU. Fits SaaS, subscriptions, social networks. Strategy: improve the product until natural growth exceeds churn.

2. Viral Engine of Growth

Customers bring customers: viral coefficient = (% who invite) × (invites sent) × (% who join); above 1.0 means exponential, self-sustaining growth. Track the coefficient, viral cycle time, and referral attribution. Fits Dropbox, Hotmail, WhatsApp. Strategy: build virality into the product itself.

3. Paid Engine of Growth

Spend to acquire: requires LTV > CAC (target LTV/CAC > 3x). Track CAC, LTV, and payback period. Fits e-commerce and traditional businesses. Strategy: optimize until each customer's profit funds acquiring more.

See references/growth-engines.md when picking or tuning an engine—churn-reduction tactics, the K-factor and viral-loop design, LTV/CAC optimization, a channel-economics table, and the product-to-engine matching framework.

The Five Whys

Root cause analysis: when a problem occurs, ask "why?" five times, then invest proportionally at every level—not just the symptom.

Example—website went down:

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