skills.rest
mazelb
mazelbCOMMUNITY
Explore all skills by this author →
GitHub

data-engineering

Automate SQL optimization and data pipeline reliability checks across PostgreSQL and MySQL.

1|Updated Oct 15, 2025
Search Tags:#data quality#ETL#pipelines#Airflow#SQL optimization#data-engineering
One-click install
npx skills add https://github.com/mazelb/dev-environment-template --skill data-engineering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-engineering
Source: https://github.com/mazelb/dev-environment-template/tree/main/.claude/skills/data-engineering
Command: npx skills add https://github.com/mazelb/dev-environment-template --skill data-engineering

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automatically reviews data pipelines, optimizes SQL queries, and ensures data quality, reducing latency, errors, and manual tuning across analytics workloads.

Core Features & Use Cases

  • SQL Optimization: identifies slow queries, recommends indexes, and refactors queries for speed.
  • Data Pipeline Design: promotes idempotent, retry-friendly ETL processes with monitoring.
  • Data Quality Checks: validates schema, detects duplicates, and enforces referential integrity.
  • Use Case: When you have a nightly ETL job that's running slowly, Claude can propose indexing, batching, and validation steps to improve throughput.

Quick Start

Use the data-engineering skill to review a slow-running query or ETL snippet and receive a set of optimization suggestions.

Frequently Asked Questions about data-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize slow SQL queries in PostgreSQL or MySQL?▼

SQL optimization identifies slow queries, recommends missing indexes, and refactors joins to improve execution speed. Analyze your query execution plan, apply index suggestions, and validate performance gains against baseline metrics.

What's the best way to design an idempotent ETL pipeline?▼

ETL pipeline design promotes retry-friendly, idempotent processes that handle failures gracefully. Structure jobs with state tracking, deduplication logic, and monitoring so reruns produce consistent results without data duplication.

Can I detect N+1 queries and missing indexes automatically?▼

Yes. Automated detection identifies N+1 query patterns, missing indexes, and slow joins in SQLAlchemy, Prisma, and Spring Data JPA environments. The analysis outputs specific index recommendations and refactored query structures.

How do I validate data quality in my ETL workflows?▼

Data quality checks enforce schema validation, detect duplicates, and verify referential integrity within your pipeline. Run checks at ingestion and transformation stages to catch issues before downstream analysis.

Does this work with Airflow-based data pipelines?▼

Yes. The skill applies to Airflow pipelines and other SQL-based environments, optimizing query performance and improving pipeline reliability through execution plan analysis and validation strategies.

Related Skills

Explore More Data & Analytics Skills→
Dokkabei97Dokkabei97
2

db-schema-designer

Design database schemas from domain descriptions for PostgreSQL, MySQL, and MongoDB.

Community
Intermediate
weirdgmeweirdgme
4

platform-engineering

Design and implement scalable platform engineering patterns for internal developer platforms.

Community
Advanced
pluginagentmarketplacepluginagentmarketplace
5

monitoring-observability

Implement Prometheus metrics, Grafana dashboards, structured logging, and alerting for data pipelines.

Community
Advanced
lluppesmslluppesms

sql-optimization

Analyze SQL execution plans and recommend index optimizations.

Community
Advanced