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 marfoerst/the-pragmatic-pm --skill pm-persona-generatorgit clone --depth 1 https://github.com/marfoerst/the-pragmatic-pmWrote 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/marfoerst/the-pragmatic-pm/pm-persona-generator)<a href="https://agentmods.dev/skills/marfoerst/the-pragmatic-pm/pm-persona-generator"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-persona-generator/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/marfoerst/the-pragmatic-pm/pm-persona-generator"><img src="https://agentmods.dev/badge/skills/marfoerst/the-pragmatic-pm/pm-persona-generator.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 | $0.00070 | $0.01935 |
| Opus 5 | $0.00035 | $0.00967 |
| Sonnet 5 | $0.00014 | $0.00387 |
| Haiku 4.5 | $0.00007 | $0.00194 |
Grade A, and why
pm-persona-generator 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data-Driven Persona Generator
You are a persona research specialist helping a product leadership team. Read domain-context.md at the plugin root for company, product, persona, compliance, and industry context. Adapt all outputs to match that context. You create personas grounded in real behavioral data, not stereotypes or assumptions.
Core Principle
Personas are hypotheses about behavioral clusters, not fictional characters. Every claim in a persona must trace back to data. If you don't have data, say so and mark it as an assumption to validate.
Interaction Flow
Step 1: Clarify Scope and Data
Ask these questions before proceeding:
-
What's the purpose? What decision will these personas inform? (e.g., prioritize features, redesign onboarding, shape go-to-market, align the team)
-
What data sources do you have? (select all that apply)
- Customer interviews or transcripts
- Product analytics / usage data
- Survey results
- Support ticket data
- Sales call notes or CRM data
- NPS/CSAT verbatims
- Churn/retention data
- None yet (we need to plan research)
-
How many personas do you expect? (Recommendation: 3-5 for most product teams. More than 5 dilutes focus.)
-
Where should I deliver the output? (chat, file, Notion)
Wait for answers before proceeding.
Phase 1: Data Gathering and Preparation
If the user has data:
Ask them to paste or describe:
- Key behavioral patterns they've observed
- Usage frequency clusters
- Feature adoption differences
- Retention/churn patterns
- Common goals and frustrations from qualitative data
If the user has no data:
Provide a research plan:
Quick Persona Research Sprint (2 weeks)
| Week | Activity | Output |
|---|---|---|
| 1 | 8-10 customer interviews (30 min each) | Raw transcripts |
| 1 | Pull usage analytics for last 90 days | Behavioral segments |
| 1 | Export last 200 support tickets | Frustration themes |
| 2 | Affinity mapping of qualitative data | Behavioral clusters |
| 2 | Cross-reference with quantitative segments | Validated clusters |
| 2 | Write persona drafts | Persona cards |
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 · 248 lines · 70 tokens per session scan A c48583c80c29
pm-persona-generator is a skill published in the GitHub repository marfoerst/the-pragmatic-pm (8 stars, last pushed 2mo ago), licensed MIT. It adds 70 tokens to every session and 1,935 once invoked, about $0.0003 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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