Sign in

Growth Experiment Sprint

Run a batch of prioritized experiments in one window and walk away with validated learning instead of more opinions.

Pro simple Service Offers

What it adds

The builder mines your experiment logs, hypothesis backlogs, and results archives to build an offer where the deliverable is tested knowledge: a set of experiments designed, run, measured, and concluded within a defined window. It converts by selling reduced uncertainty rather than promised outcomes, which makes it honest and durable regardless of which experiments win.

What your builder is told to do

8

The actual instructions, in order.
  1. 1

    Read the experiment logs and results archives in the source files, and count how many experiments were genuinely concluded — designed, run, and read out — within a comparable window. That count is the maximum this offer may promise.

  2. 2

    State the promise as a number of concluded experiments and the resulting decisions they inform, explicitly not as a promised performance improvement.

  3. 3

    Publish the hypothesis scoring method: the factors weighed when prioritizing which experiments run first, so the buyer sees the selection is systematic.

  4. 4

    Define what "concluded" means: the pre-declared success criterion, the minimum sample or observation period, and the requirement that the result be recorded whether positive, negative, or inconclusive.

  5. 5

    Describe the sprint rhythm: hypothesis workshop, design and instrumentation, launch, observation, and readout, with the buyer's involvement named at each point.

  6. 6

    Frame pricing around the cost of untested assumptions — structurally, without figures — and be explicit that the fee buys the experimentation process and its findings, not a guaranteed lift.

  7. 7

    Apply layout: on mobile show the hypothesis backlog as a scored, sortable card list with a sticky sprint-booking action; on tablet show the backlog beside the sprint rhythm timeline; on desktop render a scored backlog table alongside an experiment-status board with the readout format shown beneath.

  8. 8

    Close with one action — book the experiment sprint — and state the minimum traffic, volume, or sample conditions the buyer must meet to participate.

Edge cases it handles

5

The things an agent skips when you only say "build a growth experiment sprint".
  • Volume is too low for any experiment to reach a readable result — state the qualification threshold and recommend an alternative below it.
  • All experiments in the window return negative results — state in advance that negative findings are a full deliverable and explain their value.
  • The buyer wants to stop an experiment early because it looks bad — publish the pre-declared observation period and the rule against early stopping.
  • Multiple experiments interfere with one another — describe the isolation or sequencing approach used.
  • Instrumentation is missing for the chosen metric — define the setup step and how it consumes sprint capacity.

Definition of done

7

Your builder is required to check every one of these before reporting the work finished.
  • The promised number of concluded experiments is grounded in real historical throughput.
  • No performance improvement is promised anywhere on the page.
  • The hypothesis scoring method is published.
  • "Concluded" is defined with success criteria and minimum observation period, including inconclusive outcomes.
  • Minimum volume or sample qualification conditions are stated before the call to action.
  • Mobile card list, tablet backlog-plus-timeline, and desktop table-plus-board layouts are each implemented.
  • The builder reports which source files it used for throughput and scoring, and lists unresolved assumptions about volume.

Related offer files

How it works

  1. 1

    Copy the link

    Grab the Markdown blueprint URL for this offer type.

  2. 2

    Give it to your builder

    Paste it into Claude Code, Cursor, Codex, or whatever AI builder is already working in your app.

  3. 3

    It maps, then builds

    Your agent reads the blueprint first, then builds the offer into your app around the product, audience, and stack.

Works with your stack

These blueprints are written to adapt. They tell the agent to detect your framework, match your existing design system, and use your source files instead of guessing the offer.

Need it tighter than that? Customize the offer file and tell it exactly which source docs and stack to use.