What makes Lean Startup notable?

1K downloads. Trust evidence available

How should Lean Startup be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

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Lean Startup

Activate when: user says 'lean startup', 'build-measure-learn', 'MVP', 'validated learning', 'pivot or persevere', 'should we just build it?', 'we need to te... Skill: Lean Startup Owner: deciqai Summary: Activate when: user says 'lean startup', 'build-measure-learn', 'MVP', 'validated learning', 'pivot or persevere', 'should we just build it?', 'we need to te... Tags: latest:1.0.4 Version history: v1.0.4 | 2026-07-16T18:04:47.149Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/lean-startup.json) v1.0.3 | 2026-07-08T11:07:40.103Z | user F

OpenClaw

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Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.4

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.4release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:lean-startup

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-lean-startup/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

114,104 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: lean-startup
description: "Activate when: user says 'lean startup', 'build-measure-learn', 'MVP', 'validated learning', 'pivot or persevere', 'should we just build it?', 'we need to test this idea before building', or 'how do we know if anyone wants this?'; team is about to build something significant before testing demand; a pivot decision is on the table after early data.
Do NOT activate when: operating a known business model in known conditions (use execution frameworks instead); decision is below business-model level (button color, which CRM). More: deciqai.com/c/lean-startup"
---

# Lean Startup

## Overview

A startup is a **temporary organization searching for a repeatable, scalable business model under extreme uncertainty** (Steve Blank). Most early-stage failures are from building something no one wanted because the demand assumption was never tested.

**Eric Ries** (2011): name the riskiest assumption, build the smallest test (MVP), measure real behavior, decide to **pivot or persevere** — the **Build–Measure–Learn loop**, run as fast as possible.

**Compose:** first-principles to find what the model truly depends on; probabilistic-thinking to calibrate experiments; inversion before each Build phase; business-model-canvas to surface the riskiest assumption blocks.

## When to Use

Apply when: high uncertainty + limited capital; a team is about to build before testing demand; a pivot-or-persevere decision is on the table; you're building an AI feature on a foundation-model API and worried "the next model release will commoditize us" / "are we just a GPT wrapper?"; no clear answer to "what is the load-bearing assumption and how would we know if it's wrong?"

**When NOT to use:** known business model in known conditions (execution, not search); decision is not business-model-level; cannot ethically run a test with real customers; using "lean" as a schedule excuse to ship buggy software.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete hypothesis → run The Process directly.
- **Coach mode:** no concrete hypothesis or signals unfamiliarity → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. **One-line what-it-is.** Most startups fail by building before knowing if anyone wants it; lean startup names the riskiest assumption, tests it with the smallest MVP, measures real behavior, and decides pivot or persevere — fast.
2. **Check fit.** Match against When to Use / When NOT to use; if low uncertainty + known model, redirect.
3. **Elicit their real hypothesis.** Force them to name one load-bearing assumption — specific segment, specific value, specific willingness-to-pay.
> **[WAIT — do not advance until user responds]**
4. **Walk the loop step by step.** Name assumption → design MVP → define metric → set threshold. Pause at each.
> **[WAIT — do not advance until user responds]**
5. **Close by naming the next-w

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "lean-startup",
  "version": "1.0.4",
  "publishedAt": 1784225087149
}

references/sources.md

# Sources — lean-startup

> *Primary sources for the [lean-startup](../SKILL.md) skill.*

- **Ries, Eric.** *The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses*. Crown, 2011. **Canonical primary source** for Lean Startup and Build-Measure-Learn; verbatim Overview quote is p. 9; Dropbox case discussion pp. 99–101; Votizen pivot sequence opens ch. 8, "Pivot (or Persevere)".
- **Blank, Steve.** *The Four Steps to the Epiphany: Successful Strategies for Products That Win*. K&S Ranch, 2nd ed. 2013 (orig. self-published 2005). **Primary source** for the Customer Development methodology that grounds Lean Startup.
- **Blank, Steve.** "Why the Lean Start-Up Changes Everything." *Harvard Business Review*, May 2013. The methodology's founder synthesizing the framework for HBR. https://hbr.org/2013/05/why-the-lean-start-up-changes-everything
- **Houston, Drew.** Original 2007 Dropbox demo video (the MVP) — currently re-hosted at: https://www.youtube.com/watch?v=7QmCUDHpNzE
- **Sequoia Capital, Dropbox pitch deck, 2007** — primary-source artifact from the early Dropbox story.
- **OpenAI.** "Introducing ChatGPT." 30 November 2022. https://openai.com/index/chatgpt/ — public marker for the foundation-model API wave that made the *Build* phase nearly free for AI-native startups (2023–2026 example).
- **Reuters / Associated Press / Financial Times**, late January 2025 coverage of DeepSeek's low-cost open-weight model release and the ~27 January 2025 selloff in Nvidia and AI-linked equities — durable, widely-reported reminder that AI moat/cost assumptions can reset without warning (2023–2026 example). Exact intraday figures varied by source and are cited qualitatively.
- The popular framing "fail fast" is **not** cited here as a source — by this skill's own rule, an aphorism is not evidence. Lean Startup is more precisely "**test cheaply** the demand-side assumption *before* paying to build the supply-side capability"; failure speed is incidental.

examples/ai-native-lean-startups-2023-2026.md

# Method in Action: AI-Native Lean Startups (2023–2026)

> *Example for the [lean-startup](../SKILL.md) skill.*

A 2026 lens on the same loop. Not a prediction of which company wins — a worked example of how Build–Measure–Learn behaves when the *supply-side capability* is a rented foundation model that a competitor's next release can commoditize overnight.

After the November 2022 release of ChatGPT and the 2023–2024 arrival of capable foundation-model APIs (OpenAI, Anthropic, Google), a wave of small teams built products as thin layers over these models. The economics inverted the classic build cost: a two-person team could ship a working AI feature in days by calling an API, rather than spending months training a model. This made the *Build* phase almost free — and moved the real risk somewhere the Lean Startup framework anticipates but that demo culture ignores.

The recurring 2023–2026 failure pattern: a startup demos an impressive AI feature, raises on the demo, and then a subsequent model release from the underlying provider (or an open-weight model) absorbs that feature into the base capability — the "GPT-wrapper gets wrapped" problem. The dramatic public reminder came in **January 2025**, when the Chinese lab **DeepSeek** released a strong, low-cost open-weight reasoning model; the reaction rippled through markets and, on **27 January 2025**, Nvidia's share price fell sharply in a single session — a widely reported signal that the cost and moat assumptions underpinning many AI plans could shift without warning.

The Lean Startup correction: in an AI-native startup, the load-bearing assumption is almost never "can we build the feature?" (you can — cheaply). It is **"does a specific customer keep using and paying for the workflow *after* the underlying model capability becomes a commodity available to everyone?"** That is a retention-and-willingness-to-pay assumption, and it is exactly what a demo does not test.

Walk the Experiment Card on a representative AI-native workflow product (a small team building an AI tool for a specific professional segment):

- **Load-bearing assumption (Step 1):** *A defined professional segment (say, mid-market legal or support teams) will adopt an AI-drafting workflow, retain it past day-30, and pay a per-seat fee — where the retained value comes from our proprietary data/workflow/integration, not from raw model capability any competitor can also call.* Specific segment, specific value, specific willingness-to-pay, specific timeframe — not "people want AI."
- **Pre-commit to a threshold (Step 2):** Write the pivot bar before building — e.g. *persevere only if paid day-30 retention clears a stated bar and the value survives a hypothetical "the base model now does the naive version for free" test.* Pre-committing matters more here because a slick demo tempts you to rationalize any signal into a persevere.
- **Design the smallest MVP (Step 3):** Because the model is rented, the MVP is not "train a model." It is

examples/dropboxs-video-mvp-2007.md

# Method in Action: Dropbox's Video MVP (2007)

> *Example for the [lean-startup](../SKILL.md) skill.*

A worked example. Not founder hagiography — primary-source documented.

In **2007**, **Drew Houston** had built an early prototype of what would become Dropbox: cloud file synchronization with conflict-free updates across devices. The space was crowded with incumbents (Microsoft Live Mesh, FolderShare, Mozy, Carbonite, Apple's planned iDisk), and the prototype's killer feature — a kernel-level filesystem driver that just-worked on Mac, Windows, and Linux — was hard to demonstrate without a working installer on every OS.

Houston faced the classic Lean Startup question: **before committing months of engineering to a polished cross-platform release, was the load-bearing demand assumption true?** Specifically: would early-adopter tech users, given a frictionless sync experience, be willing to share email addresses and try the product in numbers large enough to validate a freemium-to-paid funnel?

His MVP was a **3-minute screencast video** posted to Hacker News and Digg in late 2007, demonstrating the working prototype: drag a file into a folder, watch it appear on another machine seconds later, watch conflict resolution work cleanly. The video referenced specific tech-community in-jokes (XKCD references, music chosen for the audience) — Drew explicitly designed it for the *Hacker News* / *Digg* segment most likely to convert.

The result: **the beta waiting list jumped from roughly 5,000 to 75,000 overnight** — a measurable, actionable spike directly attributable to a single MVP costing only the time to film the video. Eric Ries described this exact case in *The Lean Startup* as a textbook example of a video MVP testing demand before building.

Walk the Experiment Card on Dropbox's 2007 video MVP:

- **Load-bearing assumption (Step 1):** *Early-adopter tech users (HN/Digg-readers) will sign up for a waitlist for frictionless cross-device file sync in numbers large enough to suggest a viable freemium-to-paid funnel.*
- **Pre-committed threshold (Step 2):** Houston has not published the exact pre-committed number, but the 15× jump in beta waitlist signups would have cleared almost any reasonable pre-committed threshold.
- **MVP design (Step 3):** A 3-minute screencast video, not a working installer. The minimum thing that could elicit signup behavior. Time-box: days, not months.
- **Measurement (Step 4):** Waitlist signups in the 72 hours post-publication, segmented by referrer source (HN vs. Digg vs. organic).
- **Result vs. threshold (Step 5):** Signup spike from ~5K to ~75K. Strongly above any reasonable threshold for "demand exists."
- **Decision (Step 6):** **Persevere.** Build the cross-platform client; the demand assumption holds. (Houston did exactly this through 2008; Dropbox launched publicly in September 2008.)
- **Validated learning (Step 7):** *Tech-segment early-adopters will give email addresses for a frictionless-sync promise; the f

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