What is data-engineer?

A Spanish-language data engineering and database administration guide for designing data pipelines, moving and transforming data, modeling databases, and analyzing data.

Use it when building pipelines, designing schemas, improving database queries, or planning migrations while avoiding repeated queries and unsafe structural changes.

It sets expectations for consistent, safe, and efficient data work, including constraints, performance, backups, and versioned database changes.

How QuBiit0/lmagent configures Claude Code

Reuse

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.

Copy the file

curl -O https://raw.githubusercontent.com/QuBiit0/lmagent/main/.agents/skills/data-engineer/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/QuBiit0/lmagent

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/qubiit0/lmagent/data-engineer/github.svg)](https://agentmods.dev/skills/qubiit0/lmagent/data-engineer)

Your own site

<a href="https://agentmods.dev/skills/qubiit0/lmagent/data-engineer"><img src="https://agentmods.dev/badge/skills/qubiit0/lmagent/data-engineer/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/qubiit0/lmagent/data-engineer"><img src="https://agentmods.dev/badge/skills/qubiit0/lmagent/data-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,762 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00045	$0.03762
Opus 5.5	$0.00018	$0.01505
Sonnet 5.5	$0.00009	$0.00752
Haiku 4.5	$0.00005	$0.00376

Measured 2mo ago against content hash 36f5e6f8b24c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/backup_postgres.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/data-engineer/SKILL.md · 482 lines

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?

Read the full file on GitHub · 482 lines

Files

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.

Changes

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 · 482 lines · 45 tokens per session scan A 36f5e6f8b24c

Subscribe to this mod's changes

data-engineer is a skill published in the GitHub repository QuBiit0/lmagent (2 stars, last pushed 8mo ago), licensed MIT. It adds 45 tokens to every session and 3,762 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.

Data and AI Databases

data-engineer next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
data-engineerQuBiit0/lmagent	Skill	2	8mo ago	MIT	A	45	Claude Code
embeddingsruvnet/ruflo	Skill	81k	yesterday	MIT	A	62	Claude Code
rag-pineconedavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	65	Claude Code, Codex
data-engineerdavila7/claude-code-templates	Skill	33k	yesterday	MIT	A	35	Claude Code, Codex
similarity-search-patternsforyourhealth111-pixel/Vibe-Skills	Skill	3.8k	2d ago	Apache-2.0	A	30	Claude Code, Codex
ingesting-into-data-lakeaws/agent-toolkit-for-aws	Skill	3.4k	yesterday	Apache-2.0	A	228	Claude Code
graphjin-envdosco/graphjin	Skill	3.2k	5d ago	Apache-2.0	A	61	Claude Code, Codex

lmagent: Skill for Claude Code