data-engineering
De la autor
Design and operate data infrastructure — database operations (vector, relational, graph, time-series), ETL/ELT pipeline design (dbt patterns, incremental loading), SQL analytical patterns, data quality monitoring, schema migration, and storage infrastructure management, including model-assisted transformation contracts, deterministic acceptance, bounded retries, and sink reconciliation. Do not use for statistical analysis or ML model development.
Instalare
npx skills add magnus919/agent-skills --skill data-engineering -a claude-code
Se instalează în .claude/skills/data-engineering/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 84
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- tool.data_engineering.134100a5a52f2e93AI45Lab/OpenARTData engineering practice patterns for data pipelines, ETL/ELT, orchestration, data quality, and infrastructure. Covers Airflow, Dagster, Prefect, Spark, dbt, data lakes, lakehouses, Iceberg, Delta Lake, warehouses (Snowflake, BigQuery, Databricks), streaming (Kafka, Kinesis, Flink), CDC, data contracts, data governance, data mesh, and batch processing. Use when reviewing or building data pipelines, ingestion systems, stream processing, data infrastructure, or data platform architecture. Do not use for SQL modeling or dbt projects (use analytics-engineering), business dashboards (use analytics), or ML model training (use data-science).Date★ 253
- tool.data_engineering.55c8f35d7b3cac72AI45Lab/OpenARTData engineering practice patterns for data pipelines, ETL/ELT, orchestration, data quality, and infrastructure. Covers Airflow, Dagster, Prefect, Spark, dbt, data lakes, lakehouses, Iceberg, Delta Lake, warehouses (Snowflake, BigQuery, Databricks), streaming (Kafka, Kinesis, Flink), CDC, data contracts, data governance, data mesh, and batch processing. Use when reviewing or building data pipelines, ingestion systems, stream processing, data infrastructure, or data platform architecture. Do not use for SQL modeling or dbt projects (use analytics-engineering), business dashboards (use analytics), or ML model training (use data-science).Date★ 253
- analytics-engineerborghei/Claude-SkillsAnalytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.Date★ 971
- data-architectmagnus919/agent-skillsUse this skill to assess, design, and evolve data architectures, including data platforms, data products, data mesh adoption, event-driven data flows, governance, modeling, and migration decisions. Load it when teams need workload-grounded tradeoffs, ownership and quality agreements, or a current-to-target data architecture. Do not use it for pipeline or platform operations, implementation details, interface contract semantics, SQL tuning, or statistical modeling; route those to data-engineering, platform-engineering, api-design-and-evolution, postgres, or data-scientist.Planificare★ 147
- senior-data-engineerborghei/Claude-SkillsData engineering for batch and streaming pipelines with Airflow, dbt, Spark, and Kafka. Use when designing data architectures, building pipelines, adding data-quality checks, optimizing ETL/ELT, or troubleshooting pipeline failures.Date★ 971
- fluxtonone-ai/tononeData engineer — databases, migrations, pipelines, schema design, and query optimization. Use when asked to "design a schema", "write a migration", "build a data pipeline", or "optimize this query".Date★ 76
Note și recenzii
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