Design an A/B experiment — hypothesis, variants, primary metric, and sample size. Use when a change can be measured quantitatively at scale. For observing behaviour qualitatively, use `test-scenario`.
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Build several genuinely different solutions to the same problem at once, spread across what the user does rather than how it looks. Use when one direction is on the table and the team is about to refine it by default. For choosing between the concepts afterwards, use `concept-selection`.
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You are an expert in divergent exploration — holding multiple competing solutions to one problem before committing to any of them.
You take a problem that already has a proposed solution and construct a set of genuinely different solutions to the same problem, held at equal effort until there is evidence to choose. You decide how wide the set should be and which dimension the concepts must differ on. You do not rank or eliminate them — that is concept-selection.
Iteration and exploration buy different things. Refining one concept improves that concept. Building several in parallel improves your model of the solution space — you learn which of your assumptions were load-bearing and which were arbitrary. Stanford's parallel prototyping research (Dow, Glienke and Klemmer, 2010) found designers who produced concepts in parallel outperformed those who iterated serially on a single design for the same total effort, measured on real audience response rather than preference. Two secondary effects matter as much as the result:
A set is only informative if its members differ on the dimension the decision turns on. The test is behavioural, not visual: does the user do something different?
The count is a consequence of cost and stakes, not a target to hit:
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Design an A/B experiment — hypothesis, variants, primary metric, and sample size. Use when a change can be measured quantitatively at scale. For observing behaviour qualitatively, use `test-scenario`.
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Audit an existing interface against WCAG, producing findings with severity ratings and remediation steps. Use when you have a design or build to assess now. Not for planning future sessions with assistive-technology users — use `accessibility-test-plan` (prototyping-testing).
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Plan accessibility testing — assistive technologies, participant criteria, WCAG coverage, and session protocol. Use when scheduling testing with real AT users. Not for evaluating a design yourself — use `accessibility-audit` (design-systems).
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Apply the Aesthetic-Usability Effect — polished, consistent interfaces are perceived as more usable and forgive minor friction. Use when justifying visual polish or diagnosing why a functional design tests badly. For emotional resonance specifically, use `interfaces-that-feel` (interaction-design).
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Cluster many qualitative data points into themes and insight statements. Use when synthesising across multiple sessions or sources. For a single transcript use `summarize-interview`; for one segment's inner state use `empathy-map`.
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Apply animation principles — easing, staging, follow-through — to one specific UI motion. Use when tuning how an animation feels. For product-wide duration and easing tokens use `motion-system` (design-systems); for a full interaction spec use `micro-interaction-spec`.
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