Personas & Jobs-to-Be-Done Skill

How this skill behaves (read first)

This is a generative skill, and personas are where an AI assistant is most tempted to do exactly the wrong thing. Asked to "make some personas," the default is to invent plausible demographic fiction — "Sarah, 32, marketing manager, loves yoga and oat-milk lattes" — with no research basis, needs inferred from demographics, and a pile of irrelevant lifestyle detail. Uxcel's own source is blunt about it: LLM-generated personas reflect generic internet stereotypes and underrepresent edge cases and minority users. A useful persona instead synthesizes real research into a memorable character whose every detail could change a design decision — or, when what matters is the progress a user is trying to make, a Job-to-Be-Done is the better lens. So this skill gates:

1. Establish the research objective, the stage/data you have, and the resources — these decide whether to use personas, JTBD, or both, and whether you even have the evidence to build one yet.
2. Apply the always-true core — ground in real research, keep only decision-relevant detail, use JTBD for the job and personas for the who, keep the set small, build as a team, and keep it alive.
3. Surface the context-dependent decisions (lens choice, research-based vs. proto, count, which details, segmentation depth, AI's role) with trade-offs.

Then it hands off to pm-assumption-rigor-audit — the check that the persona's claimed needs and behaviors rest on evidence, not assumption.

Scope: this skill owns the audience-understanding artifact. It defers the research process that feeds it to pm-discovery (including ethical/inclusive recruitment), framing the user problem to pm-problem-statement, turning a persona + job into stories to pm-user-story, and behavioral/cohort analytics depth to pm-analytics.

Step 0 — Establish context before building

Ask if not known; state the assumption if proceeding without an answer: