Synthetic User Research Skill

AI personas are the most misused research tool of the decade — and genuinely useful inside a narrow lane. The difference is the question you ask them. Synthetic panels can catch comprehension failures, confusing flows, and survey defects before you spend real participants on them; they cannot tell you what people will pay for, feel, or do. This skill enforces the lane, then runs the method properly.

What This Skill Produces

• A fit verdict: is this question answerable synthetically at all? (Sometimes the deliverable is "no — here's the human study instead")
• A persona-panel design grounded in real data you already have, with provenance per persona
• Findings, labelled synthetic throughout, with confidence calibrated to the method's floor
• The human follow-up plan — what the synthetic pass earned you the right to test properly

The Lane (checked before anything runs)

Synthetic methods CAN usefully probe — because the answer lives in the artifact, not in human hearts:

• Comprehension: is this copy/onboarding/explanation understandable? Where does a reader stumble?
• Instrument defects: leading questions, double-barrelled items, missing answer options in a survey before fielding it
• Information architecture: can a goal-holder find the thing? Where does the nav mislead?
• Message differentiation: do these three positionings even read as different?
• Edge-case generation: what user situations did the design forget? (Personas as brainstorm, not oracle)

Synthetic methods CANNOT establish — refuse these, and say why:

• Willingness to pay, purchase intent, or price sensitivity (models have no budget and infinite agreeableness)
• Emotional response, delight, trust (simulated feeling is fluent and empty)
• Discovery of unknown needs (personas remix known data; discovery is precisely the unknown)
• Behavioural prediction (what people is already unreliable; what a model says they'd say is worse)