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    Growth Experiment Engine — Build, Prioritize & Learn

    1

    Create growth tests with causal hypotheses, prioritization, randomization, metric definitions, guardrails and learning plans.

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    Growth Experiment Engine — Build, Prioritize & Learn

    Growth Experiment Engine — Build, Prioritize & Learn

    Example session with this skill installed

    Fictional example: A SaaS signup flow has a long introduction. The team hypothesizes a shorter intro improves completed activation. Baseline and sample size are unknown. Produce a complete first-pass deliverable, separate facts from assumptions and give practical next steps. Do not take external actions.

    • Read your context and instructions
    • Compiled the growth experiment engine

    Growth Experiment Engine: worked example

    Scope: this fictional brief, using only supplied information.

    A SaaS signup flow has a long introduction. The team hypothesizes a shorter intro improves completed activation. Baseline and sample size are unknown.

    Deliverable

    WorkstreamFinding / decisionRecommended action
    HypothesisLess introductory friction may helpCompare existing intro with a shorter version while keeping the remaining flow unchanged.
    MeasurementActivation must be defined before launchSpecify the first meaningful product action and window; randomize consistently by eligible user and avoid cross-group contamination.
    GuardrailsFast completion can reduce understandingTrack support confusion, retention and qualified activation; choose sample-size and stopping rules from actual baseline data before exposure.

    Decision and next steps

    Instrument and validate events first. Run the controlled test only after an approved analysis plan; report effect estimates and uncertainty rather than declaring a winner from early clicks.

    Evidence boundaries

    Conclusions apply to the supplied scenario. Missing facts remain unverified; recommendations are not completed actions. Validate source data and approvals before implementation. Track results against the objective and adjust only when new evidence supports change.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Growth Experiment Engine

    Create growth tests with causal hypotheses, prioritization, randomization, metric definitions, guardrails and learning plans.

    Workflow and deliverables

    • Compare existing intro with a shorter version while keeping the remaining flow unchanged.
    • Specify the first meaningful product action and window; randomize consistently by eligible user and avoid cross-group contamination.
    • Track support confusion, retention and qualified activation; choose sample-size and stopping rules from actual baseline data before exposure.

    Getting started

    Provide your goal, relevant source material, constraints and context for growth experiment engine. Include dates, existing decisions and any restrictions that must be preserved.

    Receive a structured deliverable with source facts, explicit assumptions, priorities, open questions and next steps. Adapt the depth to your available evidence; the sample below demonstrates the workflow with fictional inputs.

    Boundaries

    Uses supplied evidence and explicit assumptions. No guaranteed outcomes or continuous monitoring. Live research and external actions require supported tools and authorization.

    Reusable agent instructions, with optional tool use depending on your host. External actions require supported tools and explicit authorization.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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