Experiment Design Expert

Experiment Design Expert

Overview

Design fast, low-cost experiments to validate product hypotheses before committing to full development. This skill applies Alberto Savoia's pretotyping philosophy ("Make sure you are building The Right It before you build It right") alongside lean experimentation methods for both new and existing products.

Core Capabilities

• XYZ hypotheses — frame every test as "At least X% of Y will do Z" with a pre-set pass/fail threshold.
• SITG + YODA discipline — prefer skin-in-the-game signals (money, time, reputation) and Your Own Data over surveys and benchmarks.
• Method selection — landing page, explainer video, pre-order, concierge MVP (new products); fake door, feature stub, A/B test, Wizard of Oz, in-app survey (existing).
• 5-step process — hypothesis, method, metric/threshold, timeboxed run, evaluate (pass/fail/inconclusive).
• Automated design — experiment_designer.py suggests 2-3 experiments per hypothesis with metric, threshold, effort, and duration.

When to Use

• You have a product idea or feature hypothesis and need to validate it cheaply.
• You want to test willingness to pay or genuine user interest, not just stated preference.
• You need to choose the right experiment method for your context (new vs. existing product).

Clarify First

Before designing the experiment, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Hypothesis to test — the specific belief stated as "At least X% of Y will do Z" (drives hypothesis_text and the pass/fail threshold)
• Product type — new vs existing (selects the method catalog: landing page / pre-order / concierge vs fake door / feature stub / A-B test)
• Target segment — who "Y" is in the hypothesis (drives the metric and who you expose the test to)
• Available SITG signal — what skin-in-the-game you can capture (money, time, reputation) given budget/tooling (narrows realistic methods)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

python3 scripts/experiment_designer.py --demo            # built-in sample (3 hypotheses)
python3 scripts/experiment_designer.py input.json        # design experiments for your hypotheses
python3 scripts/experiment_designer.py input.json --format json

Each hypothesis needs hypothesis_text, target_segment, and product_type (new/existing). Document each experiment with assets/experiment_plan_template.md.