lean-startup
Audited by Runlayer on Feb 23, 2026
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Tool: SKILL.md [1/3] Description: --- name: lean-startup description: 'Lean Startup methodology based on Eric Ries'' "The Lean Startup". Use when you need to: (1) design MVP scope for new product ideas, (2) define validated learning experiments, (3) create innovation accounting frameworks, (4) decide when to pivot vs.
Tool: SKILL.md [2/3] Description: - 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?
Tool: SKILL.md [3/3] Description: ## Small Batches **Principle:** Work in small batches to accelerate learning and reduce waste.
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Tool: references/applications.md [1/3] Description: # Lean Startup Applications by Context The Lean Startup was born in Silicon Valley software startups, but its principles apply wherever there is uncertainty about what to build, who to build it for, or how to build a sustainable business.
Tool: references/applications.md [2/3] Description: Y weeks of availability - [ ] No measurable impact on target metric after full rollout - [ ] Increases support tickets without corresponding engagement increase - [ ] Negative impact on core metrics (retention, activation, revenue) - [ ] Maintenance cost exceeds value delivered Most teams add features but never remove them. This creates bloat.
Tool: references/applications.md [3/3] Description: cycles but still experimental | | Long sales cycles (enterprise B2B) | Use letters of intent and paid pilots as validation signals; monthly instead of weekly loops | | Physical products (hardware, CPG) | Front-load demand validation; use rapid prototyping for solution validation | | Two-sided markets (marketplaces) | Validate each side separately; often need concierge for supply side first | | Network-dependent products (social) | Test with sma
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Tool: references/assumptions.md [1/3] Description: # Leap-of-Faith Assumptions Every startup is built on a stack of unproven assumptions.
Tool: references/assumptions.md [2/3] Description: usability test | 1-2 weeks | Low | Medium | | A/B test (existing product) | 1-3 weeks | Low | High | | Competitor product teardown | 1 week | Low | Low-Medium | ### Business Model Assumptions | Method | Duration | Cost | Signal Strength | |--------|----------|------|-----------------| | Pre-sell / pre-order | 2-4 weeks | Medium | Very High | | Pricing page test (before product) | 1-2 weeks | Low | High | | Willingness-to-pay interviews | 1-2 wee
Tool: references/assumptions.md [3/3] Description: not exist or the customer does not care, nothing else matters.
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Tool: references/build-measure-learn.md [1/2] Description: # Build-Measure-Learn Loop Execution Guide The Build-Measure-Learn feedback loop is the core operating system of the Lean Startup. It transforms uncertainty into validated learning through rapid experimentation. The key insight most teams miss: you plan the loop in reverse (Learn-Measure-Build) but execute it forward (Build-Measure-Learn).
Tool: references/build-measure-learn.md [2/2] Description: HYPOTHESIS What we believe: _______________ For whom: _______________ Because: _______________ METRIC Primary metric: _______________ Current baseline: _______________ Success threshold: _______________ Failure threshold: _______________ BUILD What we will build/create: _______________ Maximum time to build: _______________ Resources needed: _______________ MEASURE How we collect data: _______________ Sample size needed: _______________
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Tool: references/case-studies.md [1/3] Description: # Lean Startup Case Studies These case studies illustrate how lean principles work in practice. Each follows a consistent structure: the situation before lean methods were applied, the specific lean approach used, the experiments conducted, the results achieved, and the lessons that generalize beyond the specific company. The final section examines companies that failed by ignoring lean principles, and cross-cutting patterns that emerge across
Tool: references/case-studies.md [2/3] Description: investment. ### Results Zappos scaled to $1 billion in annual revenue and was acquired by Amazon for $1.2 billion. The Wizard of Oz MVP phase cost almost nothing in technology but generated definitive evidence of demand. ### Lessons 1.
Tool: references/case-studies.md [3/3] Description: on question types the system could not handle well. ### Lessons 1.
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Tool: references/five-whys.md [1/3] Description: # Five Whys Root Cause Analysis The Five Whys is a root cause analysis technique adapted from the Toyota Production System for use in startups. When something goes wrong, most teams fix the symptom and move on.
Tool: references/five-whys.md [2/3] Description: Why 2 | Why did only 8% see the announcement? | The announcement was an in-app banner that appeared only on first login after release, and 92% of active users did not log in that day.
Tool: references/five-whys.md [3/3] Description: Root cause remains hidden | Push for specific, verifiable answers at each level | | Skipping the investment step | Analysis without action; problems recur | Require at least one investment per level before ending the session | | Not following up | Investments are forgotten; trust erodes | Track investments in a shared system; review in next session | | Running sessions without affected parties | Analysis is speculative; investments miss the mark |
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Tool: references/growth-engines.md [1/3] Description: # Engines of Growth Every startup that grows sustainably does so through one of three engines of growth.
Tool: references/growth-engines.md [2/3] Description: files viewable by anyone with link | 0.6-0.8 | ## The Paid Engine of Growth The paid engine grows by investing money to acquire customers profitably.
Tool: references/growth-engines.md [3/3]
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Tool: references/innovation-accounting.md [1/2] Description: # Innovation Accounting Traditional accounting measures revenue, profit, and ROI. These metrics are meaningless for a startup operating under extreme uncertainty because the numbers are too small, too noisy, and too lagging to guide decisions.
Tool: references/innovation-accounting.md [2/2] Description: analysis, the team might celebrate growth while the product is actually deteriorating. **Same data viewed by cohort:** | Cohort | Month 1 | Month 2 | Month 3 | |--------|---------|---------|---------| | January (100 users) | 40% | 25% | 15% | | February (150 users) | 35% | 20% | - | | March (250 users) | 30% | - | - | Now the story is clear: retention is dropping, and each new cohort performs worse than the last.
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Tool: references/metrics.md [1/3] Description: # Actionable Metrics Guide Metrics are the language of validated learning.
Tool: references/metrics.md [2/3] Description: | Product or acquisition quality is declining | Investigate recent changes; audit acquisition channels | | Retention flattens at a certain week | Product has a natural engagement ceiling | Focus on deepening value for retained users | | Retention drops sharply in Week 1 | Onboarding or first-use experience is broken | Redesign activation flow | | Later cohorts are larger but retain worse | Growth is outpacing product quality | Slow growth; fix reten
Tool: references/metrics.md [3/3] Description: month | | Optimizing a metric that does not connect to business outcomes | Local optimization without global impact | Map every metric to a business outcome (retention to LTV, activation to retention, etc.) | | Changing metric definitions mid-experiment | Results become incomparable | Lock definitions before experiments start; create new metrics if needed | Good metrics create honest conversations.
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Tool: references/mvp-design.md [1/2] Description: # MVP Design Guide A Minimum Viable Product is not a minimal product. It is the smallest experiment that lets you collect the maximum amount of validated learning about customers with the least effort. The purpose of an MVP is to test a fundamental business hypothesis, not to satisfy customers or generate revenue (though both may happen). Every design decision about the MVP should flow from a single question: what do we need to learn, and what is
Tool: references/mvp-design.md [2/2] Description: automating | | Consumer mobile app | Single Feature, Video, Smoke Test | Consumer attention is scarce; test one hook | | Marketplace | Concierge (supply side), Smoke Test (demand side) | Must validate both sides separately | | Hardware product | Video, Pre-Order, Wizard of Oz | Physical prototyping is expensive; validate demand first | | API / developer tool | Single Feature, Piecemeal | Developers want working tools, not promises | ## MVP Sizing
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Tool: references/pivots.md [1/3] Description: # Pivots: When and How to Change Direction A pivot is a structured course correction designed to test a new fundamental hypothesis about the product, strategy, or engine of growth. It is not a random change, a rebrand, or giving up. A pivot preserves what has been learned while changing what has not worked. The ability to pivot is the essential difference between startups that succeed and those that run out of runway pursuing a flawed plan.
Tool: references/pivots.md [2/3] Description: diminishing returns? - What have we learned that changes our original assumptions? 4.
Tool: references/pivots.md [3/3] Description: cause another pivot) - [ ] Runway is recalculated and pivot capacity is updated - [ ] Learnings from the pre-pivot phase are documented and accessible A pivot is not an admission of failure.
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Tool: references/small-batches.md [1/3] Description: # Small Batches and Continuous Deployment The power of small batches is counterintuitive. Most people believe working in large batches is more efficient because it minimizes setup time and context switching.
Tool: references/small-batches.md [2/3] Description: schedule - [ ] Customer support team is briefed on the new feature ## Small Batch Thinking for Non-Technical Contexts Small batches apply far beyond software deployment.
Tool: references/small-batches.md [3/3]