Skill UI

Guides teams through planning, designing, and running statistically sound A/B tests with hypothesis framing, sample sizing, metric selection, implementation strategies, and analysis checkpoints for systematic growth experimentation.

Guides PMs through a full discovery cycle—from framing a hypothesis to validating solutions—by combining problem definition, customer interviews, synthesis, and experiments so teams can test assumptions before investing in build work.

Guides product leaders through Jeff Gothelf's Lean UX Canvas v2 to turn business problems into aligned experiments, surface assumptions, and document learning goals before investing in features.

Guides product managers from stakeholder requests to structured Opportunity Solution Trees by extracting outcomes, enumerating opportunities, mapping solutions, and evaluating experiments to select the best proof of concept before building.

Defines and documents Proof of Life probes—lightweight, disposable experiments that test a single risky hypothesis fast so teams learn harsh truth before building production software.

An expert guide for planning, designing, and implementing statistically rigorous A/B tests and product experiments. It covers core principles like formulating strong hypotheses, selecting primary/guardrail metrics, calculating sample sizes, and analyzing results to drive actionable business insights.

A systematic framework for launching and validating new products and business models. It guides users through the Build-Measure-Learn loop, emphasizing validated learning over vanity metrics, and structuring Minimum Viable Product (MVP) experiments to test core assumptions and determine if a pivot or perseverance is necessary.

Guides product teams through hypothesis writing, metric definition, sample-size planning, ICE prioritization, and statistical interpretation so experiments stay practical, defensible, and aligned with business decision thresholds.

A structured methodology for de-risking product ideas and validating market opportunities. This framework guides teams through defining measurable outcomes, building Opportunity Solution Trees (OST), mapping critical assumptions (Desirability, Viability, Feasibility), and planning rigorous experiments (prototyping, interviews, usability tests) before committing to full development resources. Ideal for discovery sprints and pre-build validation.

Framework for launching new products from idea to first loyal customers, covering direct outreach, launch diagnostics, and short experiment cycles to break through early adoption hurdles.

Guides teams through testing messaging and word choice to find defensible market positioning, using a phased Crawl-Walk-Run rollout plus word-change experiments to accelerate enterprise deals.

This structured mode acts as an experiment design partner for Product Managers. It guides users through formulating testable, falsifiable hypotheses, designing rigorous A/B tests (including sample size, duration, and guardrails), and interpreting results honestly. It helps PMs move from vague beliefs to clear, data-backed decisions: Ship, Iterate, or Kill.