Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Uxcel-Lab/product-skills --skill personas-jtbdgit clone --depth 1 https://github.com/Uxcel-Lab/product-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/uxcel-lab/product-skills/personas-jtbd)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/personas-jtbd"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/personas-jtbd/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/personas-jtbd"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/personas-jtbd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00144 | $0.02621 |
| Opus 5 | $0.00072 | $0.01311 |
| Sonnet 5 | $0.00029 | $0.00524 |
| Haiku 4.5 | $0.00014 | $0.00262 |
Grade A, and why
pm-personas-jtbd scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- 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.
- 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.
- 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:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 100 lines · 144 tokens per session scan A 5d0e956a11e1
pm-personas-jtbd is a skill published in the GitHub repository Uxcel-Lab/product-skills (12 stars, last pushed 2mo ago), licensed MIT. It adds 144 tokens to every session and 2,621 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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