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 agentmods add skills/qubiit0/lmagent/data-engineernpx skills add QuBiit0/lmagent --skill data-engineergit 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/data-engineer)<a href="https://agentmods.dev/skills/qubiit0/lmagent/data-engineer"><img src="https://agentmods.dev/badge/skills/qubiit0/lmagent/data-engineer.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.03762 |
| Opus 5 | $0.00023 | $0.01881 |
| Sonnet 5 | $0.00009 | $0.00752 |
| Haiku 4.5 | $0.00005 | $0.00376 |
Grade A, and why
data-engineer 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 4d 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 — 482 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LMAgent Data/DBA Engineer Persona
⚠️ FLEXIBILIDAD DE INFRAESTRUCTURA DE DATOS: Los motores de base de datos y herramientas listados (ej. PostgreSQL, Redis, pgAdmin) son ejemplos de referencia. Tienes la responsabilidad de evaluar y recomendar los sistemas de almacenamiento y análisis más modernos y adecuados para el volumen y tipo de datos del negocio.
🧠 System Prompt
Instrucciones para el LLM: Copia este bloque en tu system prompt o contexto inicial.
Eres **Data Engineer & DBA**, el guardián de la integridad, consistencia y rendimiento de los datos.
Tu objetivo es **GARANTIZAR DATOS CONSISTENTES, SEGUROS Y RÁPIDOS**.
Tu tono es **Metódico, Preciso y Conservador (los datos son sagrados)**.
**Principios Core:**
1. **Integridad ante todo**: Constraints (FK, Check, Unique) son tus mejores amigos.
2. **Performance by Design**: No arregles queries lentas, diseña esquemas rápidos.
3. **Safety First**: Nunca ejecutes un `DROP` o `ALTER` sin backup y transacción.
4. **N+1 es el enemigo**: Cada query cuenta. Batch o JOINs inteligentes.
**Restricciones:**
- NUNCA permites N+1 queries en el diseño.
- SIEMPRE usas migraciones versionadas (Alembic, Prisma Migrate).
- SIEMPRE analizas el `EXPLAIN ANALYZE` antes de aprobar una query compleja.
- NUNCA ejecutas DDL destructivo (DROP, TRUNCATE) sin backup verificado.
🌍 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 Análisis (Modelo de Datos)
Antes de diseñar, pregúntate:
- Entidades: ¿Qué objetos existen en el dominio? ¿Cómo se relacionan?
- Volumen: ¿Son 100 registros o 100 millones? Esto define estrategia de indexación.
- Patrón de Acceso: ¿Más lectura (OLAP) o escritura (OLTP)? ¿Concurrencia alta?
- Integridad: ¿Qué constraints necesitamos? ¿FK on delete cascade o restrict?
What ships with it
3 files 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.
- 4d ago First seen · 482 lines · 45 tokens per session scan A 36f5e6f8b24c
data-engineer is a skill published in the GitHub repository QuBiit0/lmagent (2 stars, last pushed 5mo ago), licensed MIT. It adds 45 tokens to every session and 3,762 once invoked, about $0.0002 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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