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 ljucask/pureinn-product-development --skill pm-personasgit clone --depth 1 https://github.com/ljucask/pureinn-product-developmentWrote 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/ljucask/pureinn-product-development/pm-personas)<a href="https://agentmods.dev/skills/ljucask/pureinn-product-development/pm-personas"><img src="https://agentmods.dev/badge/skills/ljucask/pureinn-product-development/pm-personas/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/ljucask/pureinn-product-development/pm-personas"><img src="https://agentmods.dev/badge/skills/ljucask/pureinn-product-development/pm-personas.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 | $0.00091 | $0.05245 |
| Opus 5 | $0.00046 | $0.02622 |
| Sonnet 5 | $0.00018 | $0.01049 |
| Haiku 4.5 | $0.00009 | $0.00524 |
Grade A, and why
pm-personas 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 — 569 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PM - Customer Segments & Personas
Agent mode (--agent)
Supports --agent: runs autonomously in a subagent, drafts the artifact from existing inputs, and returns a short summary + coverage note.
- No flag → interactive (default); if inputs are heavy, offer agent mode.
--agent→ obey. First check inputs are complete. Anything missing: do NOT invent it - mark[ASSUMED - what/why]in the output and summary. Never hallucinate to fill a gap.- Review required: the artifact contains commitments - after drafting, require the user's review before finalizing; do not close decisions autonomously.
What this skill does
Takes raw VOC (Voice of Customer) data - interview transcripts, synthetic interview outputs, survey results, behavioral observations - and produces:
- Customer Segments (distinct groups with shared needs)
- Personas (1-2 per key segment)
- Early Adopters Profile
This is a "bring your data" skill. Claude synthesizes, structures, and formalizes patterns from the input. No invented personas without data.
Run this before jtbd-building - JTBD analysis uses personas as input.
Provenance discipline: every persona claim carries its source class - real VOC (interviews, surveys, observation), [CLIENT-ASSERTED] (a client/sponsor described their own users - input, not evidence), or [ASSUMED] (our inference). In commissioned builds the client's description of their users is the most common input - treat it as a hypothesis to validate with real users, never as validated research.
Three populations (commissioned builds): the client's customers are not the only users. The client's staff (admins, operators, support - they use the product daily) are legitimate personas too, and skipping them is the classic launch failure. If discovery captured staff roles (see Client Discovery meeting notes), produce staff personas alongside customer personas.
Dependencies
Recommended before running:
pm-project-charter- target customer direction and geographypm-market-analysis- segment data provides foundation for persona development
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 · 569 lines · 91 tokens per session scan A 0654b9d61bb4
pm-personas is a skill published in the GitHub repository ljucask/pureinn-product-development (2 stars, last pushed 2d ago), licensed MIT. It adds 91 tokens to every session and 5,245 once invoked, about $0.0005 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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