Skill UI

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.

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 guide provides a comprehensive framework for designing statistically rigorous A/B tests, split tests, and multivariate experiments. It covers everything from formulating strong hypotheses and calculating sample sizes to selecting appropriate primary, secondary, and guardrail metrics for various business contexts.

This expert framework guides users through the systematic process of conducting rigorous A/B tests and growth experiments. It covers all stages: formulating strong hypotheses, selecting appropriate metrics (primary, secondary, guardrail), calculating necessary sample sizes, and choosing the best implementation method (client-side vs. server-side). Use this when planning to compare two versions or build a systematic growth experimentation program.

This template guides the process of turning raw A/B test results, growth experiments, or product feature data into a clear, actionable decision report. It structures the analysis beyond mere metrics by forcing the inclusion of hypothesis, setup details, result interpretation, and a final, justified decision (Ship/Stop/Iterate). Essential for Product Managers and Growth Teams.