synthetic-user-research
Use AI personas for early-stage research signal — with hard guardrails on what synthetic methods can and cannot validate. Use when asked to run synthetic user testing, simulate user reactions with AI personas, pretest a survey or message before fielding it, or decide whether synthetic research is appropriate at all. Produces a fit verdict for the question at hand, a persona-panel design grounded in real data, the findings labelled as synthetic throughout, and the follow-up plan with real humans. Never a substitute for discovery interviews — see discovery-interview-guide and user-research-synth
Profile →npx skills add mohitagw15856/pm-claude-skills --skill synthetic-user-research --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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-barr
- What This Skill Produces
- The Lane (checked before anything runs)
- Required Inputs
- Method (when the lane check passes)
- Output Format
- Synthetic Research Pass: [artifact] — ⚠️ SYNTHETIC SIGNAL, NOT USER EVIDENCE
- Quality Checks
- Anti-Patterns
What does the synthetic-user-research skill do?
Use AI personas for early-stage research signal — with hard guardrails on what synthetic methods can and cannot validate. Use when asked to run synthetic user testing, simulate user reactions with AI personas, pretest a survey or message before fielding it, or decide whether synthetic research is appropriate at all. Produces a fit verdict for the question at hand, a persona-panel design grounded in real data, the findings labelled as synthetic throughout, and the follow-up plan with real humans. Never a substitute for discovery interviews — see discovery-interview-guide and user-research-synth
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill synthetic-user-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.