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pm-personas-jtbd

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Build or critique an audience-understanding artifact — research-based user personas, a Jobs-to-Be-Done framing, or the choice between them. Grounds the work in real research (never invented demographic fiction), keeps only detail that changes a decision, and picks the lens by research objective, stage, and resources. Applies the always-true core and gates context-dependent decisions (personas vs. JTBD vs. both, research-based vs. proto, how many personas, which details, segmentation depth, AI's role). Trigger when asked to create user personas, define a target audience/segment, apply Jobs-to-Be-Done, decide who the product is for, or review existing personas.

pm/foundations/personas-jtbd/SKILL.md

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Jul 2, 2026
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# 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`.
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