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.
git clone --depth 1 https://github.com/lucasgaravelli/pm-skills-claude-codeWrote 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/commands/lucasgaravelli/pm-skills-claude-code/persona)<a href="https://agentmods.dev/commands/lucasgaravelli/pm-skills-claude-code/persona"><img src="https://agentmods.dev/badge/commands/lucasgaravelli/pm-skills-claude-code/persona/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/commands/lucasgaravelli/pm-skills-claude-code/persona"><img src="https://agentmods.dev/badge/commands/lucasgaravelli/pm-skills-claude-code/persona.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 | $0.00000 | $0.03562 |
| Opus 5 | $0.00000 | $0.01781 |
| Sonnet 5 | $0.00000 | $0.00712 |
| Haiku 4.5 | $0.00000 | $0.00356 |
Grade A, and why
persona 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 9d 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.
This is a copy
100% identical to persona — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/persona
O que essa skill faz
Gera uma persona de produto completa com integração JTBD (Jobs to Be Done). Combina dados demográficos, comportamentais e motivacionais em um documento que serve como referência viva para decisões de produto.
Saída: Persona contract com 9 seções — do perfil demográfico até nível de confiança por seção.
Quando usar
- Início de discovery — precisa definir para quem está construindo
- Está entrando em mercado novo e precisa alinhar o time sobre o usuário-alvo
- Tem dados de pesquisa (entrevistas, surveys) e quer consolidar em persona acionável
- Quer substituir personas vagas ("João, 35 anos, gosta de tecnologia") por personas com JTBD real
Input esperado
Mínimo:
- Produto/contexto: O que está construindo e para quem
- Segmento-alvo: Tipo de usuário, cargo, indústria
- Problema principal: O que esse usuário tenta resolver
Opcional:
- Trechos de entrevistas ou pesquisa qualitativa
- Dados de analytics (comportamento real)
- Personas existentes para refinar
- Competitors que esse usuário usa hoje
- Tamanho do mercado / empresa típica
Processo
- Coletar contexto — Reunir todas as informações disponíveis sobre o segmento. Se não houver dados primários, deixar explícito que a persona é hipotética (confiança Low).
- Definir perfil demográfico — Nome fictício representativo, cargo, empresa típica, indústria, senioridade. Evitar estereótipos — basear em dados reais quando possível.
- Mapear Jobs to Be Done — Identificar job principal (funcional), jobs secundários, jobs emocionais ("sentir-se no controle") e jobs sociais ("ser visto como competente"). Usar formato: "Quando [situação], eu quero [motivação], para que [resultado]."
- Identificar pain points — Listar top 5 dores, rankeadas por severidade (1-5). Conectar cada dor a um JTBD específico.
- Documentar comportamentos — Como avalia soluções, quem influencia decisão, critérios de compra, ciclo de decisão típico.
- Adicionar quotes — 3-4 frases representativas que humanizam a persona. Se baseadas em entrevistas reais, marcar como [Real]. Se inferidas, marcar como [Inferido].
- Avaliar confiança — Para cada seção, classificar: High (dados primários), Medium (dados secundários/inferência), Low (hipótese). Isso guia onde investir mais pesquisa.
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.
- 9d ago First seen · 375 lines · 0 tokens per session scan A 8a07b361e8bc
persona is a command published in the GitHub repository lucasgaravelli/pm-skills-claude-code (20 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,562 tokens. A static security scan graded it A with 0 findings. It is 100% identical to persona, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.