This source did not publish a separate summary. Review SKILL.md before using the skill.
SKILL.md
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 say is already unreliable; what a model says they'd say is worse)
Validation for a launch/investment decision (synthetic evidence is not evidence of demand)
Required Inputs
Ask for (if not already provided):
The research question (runs through the lane check first — verdict before method)
Real data to ground personas: interview notes, support tickets, reviews, analytics segments. No real data → no panel: ungrounded personas are the model's stereotypes wearing name tags
The artifact under test (the copy, flow, survey, IA)
What decision this feeds — and its stakes (higher stakes shrink the lane)
Method (when the lane check passes)
Build personas from data, with provenance. Each persona cites its sources ("from the 14 churn interviews: SMB admin, low technical confidence, evaluates in <10 min"). 4-6 personas spanning the real segment axes, including at least one hostile/low-attention profile — synthetic panels skew cooperative unless you force otherwise.
Fight the agreeableness. Instruct personas to struggle where their profile would struggle; ask for failure ("where do you stop reading? what would make you give up?") rather than opinions ("do you like this?"); never ask satisfaction or intent questions — the lane forbids the questions models answer most fluently.
Run artifact-grounded tasks. Give the persona the actual artifact and a goal; capture where it misreads, stalls, or takes the wrong path. Quote the artifact in every finding.
Triangulate across personas and runs. A stumble that appears across 4/6 personas and repeated runs is a signal; a single eloquent complaint is noise wearing insight's clothes.
Label relentlessly and hand off. Every output says SYNTHETIC at the top and per-finding. Findings convert to: fixes to the artifact (cheap, do now) and hypotheses for the human study (the follow-up plan names method, n, and what would confirm/refute).
Output Format
Synthetic Research Pass: [artifact] — ⚠️ SYNTHETIC SIGNAL, NOT USER EVIDENCE
Lane check: [question] → [in-lane ✅ / out-of-lane 🔴 with the human method to use instead]
Panel: [persona → grounded in → key traits] (provenance per persona)
