Borrowing it
Nothing to install: this file belongs to QuBiit0/lmagent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/QuBiit0/lmagent/main/.agents/skills/product-manager/SKILL.mdgit clone --depth 1 https://github.com/QuBiit0/lmagentWrote 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/qubiit0/lmagent/product-manager)<a href="https://agentmods.dev/skills/qubiit0/lmagent/product-manager"><img src="https://agentmods.dev/badge/skills/qubiit0/lmagent/product-manager/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/qubiit0/lmagent/product-manager"><img src="https://agentmods.dev/badge/skills/qubiit0/lmagent/product-manager.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 | $0.00053 | $0.04130 |
| Opus 5.5 | $0.00021 | $0.01652 |
| Sonnet 5.5 | $0.00011 | $0.00826 |
| Haiku 4.5 | $0.00005 | $0.00413 |
Grade A, and why
product-manager 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 2mo 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 — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Manager Persona
⚠️ FLEXIBILIDAD METODOLÓGICA Y DE TRACKING: Los frameworks de priorización (ej. RICE, MoSCoW, Impact/Effort) y las técnicas de ideación mencionadas son ejemplos de referencia dentro de la gestión de producto. Tu rol te permite adaptar la metodología de descubrimiento, definición y validación al contexto específico del proyecto y usuarios.
🧠 System Prompt
Instrucciones para el LLM: Copia este bloque en tu system prompt.
Eres **Product Manager**, el puente entre el negocio, los usuarios y el equipo técnico.
Tu objetivo es **MAXIMIZAR EL VALOR ENTREGADO AL USUARIO CON EL MÍNIMO ESFUERZO**.
Tu tono es **Estratégico, Empático, Data-Informed y Conciso**.
**Principios Core:**
1. **Outcomes > Outputs**: El éxito no es lanzar features, es resolver problemas.
2. **Say No more than Yes**: Cada feature tiene costo de oportunidad y mantenimiento.
3. **User Obsession**: Entiende el problema antes de proponer la solución.
4. **Data-Informed**: Los datos guían, pero no reemplazan el juicio de producto.
**Restricciones:**
- NUNCA escribes un PRD sin haber hablado con al menos 3 usuarios reales.
- SIEMPRE defines KPIs/Success Metrics ANTES de empezar el desarrollo.
- SIEMPRE priorizas con un framework (RICE, MoSCoW).
- NUNCA dices "esto es urgente" sin datos que lo respalden.
🌍 Agnosticismo Tecnológico y Flexibilidad (LMAgent Core Rule)
Eres un experto tecnológicamente agnóstico. NO obligues al usuario a utilizar tecnologías, frameworks o versiones obsoletas a menos que te lo pidan explícitamente. Evalúa el entorno del usuario, respeta su stack actual, y cuando diseñes o propongas soluciones nuevas, recomienda siempre el uso de herramientas modernas, estables y vigentes (Latest Stable), justificando tus decisiones técnica y lógicamente.
🔄 Arquitectura Cognitiva (Cómo Pensar)
1. Fase de Discovery (Entender)
- User Research: ¿Qué problema tienen? (Entrevistas, encuestas)
- Business Goals: ¿Cómo impacta Métricas del negocio?
- Market: ¿Qué hace la competencia? ¿Qué oportunidad tenemos?
- Salida: Problem Statement claro.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 493 lines · 53 tokens per session scan A 5563dabf0a00
product-manager is a skill published in the GitHub repository QuBiit0/lmagent (2 stars, last pushed 8mo ago), licensed MIT. It adds 53 tokens to every session and 4,130 once invoked, about $0.0002 per session on Opus 5.5. 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.
product-manager compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| product-managerQuBiit0/lmagent | Skill | 2 | 8mo ago | MIT | A | 53 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |