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deciqAIdeciqAI/knowledge-skills/mvp/SKILL.md

mvp

Skill13 starsChanged 4 months agoMITView on GitHub

Activate when: someone says 'MVP', 'minimum viable product', 'smoke test', 'concierge MVP', or 'Wizard of Oz'; a team is scoping a build and hasn't named the assumption they're testing; engineers are about to build for months on an unvalidated idea; someone asks 'what's the smallest thing we can ship?'.

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  • slavingiaSkill

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    Evaluate business decisions through the lens of sustainable, profitable growth. Use when someone is making decisions about spending, hiring, fundraising, or scaling their business.

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  • slavingiaSkill

    processize

    Turn a product idea into a manual-first process you can start delivering today. Use when you have an idea and want to figure out how to deliver value by hand before writing any code.

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The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 9 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Coach mode: no assumption or signals unfamiliarity → guide step by step.

    line 28
  2. Distinctive:

    Check fit against When to Use / When NOT to use.

    line 33
  3. Distinctive:

    Build, measure, throw away: If assumption holds, rebuild for production from scratch.

    line 55
  4. Distinctive:

    Test instrument, not small product.

    line 88

“Common” is measured: kept by at least 6 in 10 of the 9 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Minimum Viable Product

Overview

An MVP is not a small version of your product — it is a test instrument: the smallest artifact that produces trustworthy evidence about one specific demand-side assumption, with the least possible investment. Success criterion is learning per dollar, not features per dollar.

Coined by Frank Robinson (SyncDev, 2001); operationalized by Eric Ries (The Lean Startup, 2011, p. 77) — sometimes the artifact has no software at all. Compose: lean-startup defines Build-Measure-Learn — this skill covers the Build phase. Use business-model-canvas to identify the assumption; inversion to stress-test it.

When to Use

  • Scoping a build and need to decide what minimum produces trustworthy evidence
  • "MVP" is being used to mean "small version of the real product" (this skill corrects that)
  • Engineers are building for months on an unvalidated assumption
  • Choosing between concierge / Wizard-of-Oz / smoke test / video / single-feature / paid pilot
  • AI made building cheap and you're about to "just build the whole thing" — vibe-coded a prototype in a weekend but can't name the assumption it tests

When NOT to use: already have PMF (question is scaling); delivering a contractual obligation; already ran the right MVP and got a clear result.

Coaching Novices (Adaptive Front Door)

  • Engine mode: specific assumption → pick type, set metric, scope the build.
  • Coach mode: no assumption 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: an MVP is the smallest test instrument for one specific assumption, not a small product.
  2. Check fit against When to Use / When NOT to use. If scaling a validated product, redirect.
  3. Elicit their real assumption. Without one, "MVP" is just "small version."

[WAIT — do not advance until user responds]

  1. Walk through MVP type selection, metric specification, time-box — one step at a time with their input.

[WAIT — do not advance until user responds]

  1. Close by naming the time-box (1–4 weeks), the pre-committed metric, and the disposability commitment.

[WAIT — do not advance until user responds]

The Process

  1. Name the single assumption being tested. One load-bearing claim with segment, behavior, rate, and value.
  2. Pick the MVP type that fits the assumption.
    • Smoke test (landing + email): tests awareness-to-interest. Tests nothing about retention or pay.
    • Landing + pre-order (credit card captured): tests willingness-to-pay at a price point.
    • Video demo (no working product): tests demand at scale. Famous case: Dropbox 2007.
    • Concierge (manual delivery): tests whether service has perceived value — no product built.
    • Wizard-of-Oz (automated front, human back-end): tests willingness-to-use before automation investment.
    • Single-feature (one feature polished, rest absent): tests whether one feature drives the use case.
    • Paid pilot (B2B, 3–5 design partners paying real money): tests enterprise WTP and integration complexity.
  3. Specify the actionable metric in advance. Measurable behavior: conversion %, day-N retention, paid signup count.
  4. Time-box aggressively. 1–4 weeks. Exceeding means scope is wrong — cut scope, never extend time.
  5. Strip every feature not load-bearing. Auth, accounts, polish — all out unless the test requires it.
  6. Build, measure, throw away. If assumption holds, rebuild for production from scratch.
  7. Document validated learning in one sentence. Artifact is disposable; learning is durable.

Output: MVP Design Card (copy-paste)

FieldFill in
Single assumption"<segment> will <behavior> at <rate> for <value>"
MVP typesmoke / pre-order / video / concierge / WoZ / single-feature / paid pilot
Why this type fits1–2 sentences on evidence produced vs. alternatives
Actionable metric + thresholde.g., day-7 retention ≥ 40% within 14 days
Build time-boxe.g., 2 weeks — cut scope if exceeded, never extend
Features IN (load-bearing only)list + reason each is required for the test
Features OUTlist + reason each is deferred
Disposability commitment☐ MVP code thrown away; production rebuilt from validated learning
Validated learning (post-test)one sentence

→ Method in Action: Zappos's Concierge MVP (1999) · Airbnb's Air-Mattress MVP (2007) → 2026 lens: The AI-Era MVP — when building got cheap and validation didn't (2023–2026) — the bottleneck moved from engineering to evidence.

MVP Type Packs

DomainFirst-choice MVP typeWhy it fits the assumptionCommon failure
Consumer appssmoke test, then single-feature buildsmoke test screens awareness-to-interest cheaply; single-feature tests whether one feature drives day-N retention; concierge rarely reaches consumer scalepolish creep turns the single-feature build into v1 before the retention data is in
B2B SaaSpaid pilot with 3–5 design partnersenterprise WTP and integration complexity only surface when real budget moves; a smoke test can't reach the budget ownerfree pilots treated as validated demand — everyone says yes to free
Marketplacesconcierge in one ZIP code or one eventmatching value is testable with the founders as the manual back-end before any platform exists (Airbnb 2007)building both sides of the platform before proving anyone wants the match
Hardwarevideo demo + pre-order (credit card captured)tests demand and price point with zero tooling cost; the pre-order forces a payment decision, not a clickconflating pre-order revenue with retention or repeat-purchase signal

Contribute a pack for your domain — see the template at the repo root.

Applying It Well

  • Test instrument, not small product. If you'd be embarrassed to show it outside the test cohort, you built it right.
  • Pick the cheapest type that tests the load-bearing assumption; pre-commit the metric before launch.
  • Throw the MVP away — code optimized for the experiment is wrong for the product.

→ Primary sources: references/sources.md

Common Rationalizations

[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Fake moveReality
[D] "MVP" = small version of the real productMVP is a test instrument. A six-month polished build is not an MVP.
[D] No assumption statedNo assumption = no learning. "Test the MVP" is just "ship a product."
[D] No pre-committed metricDeciding success after seeing data means you've already cheated.
[D] Type chosen without assumption-fit reasoningWrong type = wrong evidence, regardless of build quality.
[D] Time-box ignoredAn MVP taking six months is product development with optimistic naming. Cut scope.
[D] Polish creep"One more week of polish" turns MVP into v1, delaying learning by months.
[D] Keeping MVP code as production basisMVP code is optimized for the test, not the product — inheriting it inherits all shortcuts.
[D] Free pilots treated as validated B2B demandFree pilots produce false positives. Paid pilots test WTP.
[D] Traffic / signups counted as validationVanity metrics. Conversion, retention, and WTP test the assumption.
→ Add [O] entries here after each real use — paste the actual failure patternWhat went wrong and why

Red Flags / Verification

Red flags: MVP described as "smaller version of product" · time-box exceeds 4–6 weeks · no testable assumption named · no pre-committed metric · type chosen by engineering preference · plan to iterate MVP into final product.

  • Single specific testable assumption named
  • MVP type is one of the 7 canonical types; fit to assumption explicitly reasoned
  • Actionable metric + threshold pre-committed before launch
  • Build time-boxed (1–4 weeks); scope cut if exceeded — never extend
  • Excluded features listed with reasoning
  • Disposability commitment made — MVP code will not be the production basis
  • Validated learning captured in one sentence post-test

Part of deciqAI Knowledge Skills — 253 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/mvp · Built by deciqAI · github.com/deciqAI · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/mvp.json

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