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Found 55 Skills
End-to-end data engineering pipeline using MinIO, Airbyte, PostgreSQL, DBT, and Airflow with medallion architecture (Bronze/Silver/Gold layers)
Install and initialize task-master for AI-powered task management and specification-driven development. Use this skill when users ask you to parse a new PRD, when starting a new project that needs structured task management, when users mention wanting task breakdown or project planning, or when implementing specification-driven development workflows.
AI-powered task management for structured, specification-driven development. Use this skill when you need to manage complex projects with PRDs, break down tasks into subtasks, track dependencies, and maintain organized development workflows across features and branches.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
Expert guidance for Dagster data orchestration including assets, resources, schedules, sensors, partitions, testing, and ETL patterns. Use when building or extending Dagster projects, writing assets, configuring automation, or integrating with dbt/dlt/Sling.
This skill helps the agent generate or update orchestration pipeline definitions for Google Cloud Composer to initialize orchestration pipeline or update the orchestration definition for orchestration of various data pipelines, like dbt pipelines, notebooks, Spark jobs, Dataform, Python scripts or inline BigQuery SQL queries. This skill also helps deploy and trigger orchestration pipelines.
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
Plan a migration onto MotherDuck. Use when moving from Snowflake, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, validation, rollback, and native-versus-DuckLake posture.
Automated data quality and transformation capabilities for Dataform/dbt/BigQuery pipelines. Processes data sourced from BigQuery or Cloud Storage (GCS), applying best practices for data ingestion, movement, schema mapping, and comprehensive data cleaning.
Free 9-week data engineering course covering Docker, Terraform, Kestra, BigQuery, dbt, Spark, and Kafka with hands-on projects
Design dashboards, write analytical SQL, define KPIs, and manage stakeholder analytics requirements. Cover chart selection, data storytelling, cohort/funnel analysis, metric definitions, and BI tool patterns (Tableau, Looker, Power BI). Triggers on "build dashboard", "design dashboard", "write analytical SQL", "cohort analysis", "funnel analysis", "define KPI", "define metric", "reporting requirements", "data storytelling", "stakeholder analytics", "retention analysis", or "BI report". For business model canvas, TAM/SAM/SOM, and competitor monetization research, use business-model-researcher—not bi-analyst. For building warehouse marts, dbt models, tests, and lineage—not dashboards—use analytics-data-engineer.