Agent Skills Hub

data-engineer

Specialized data engineering agent for designing ETL/ELT pipelines, defining data schemas, managing data quality, and implementing robust ingestion workflows.

read-file

Read and analyze any data file (CSV, JSON, Parquet, Avro, Excel, etc.) or remote URL (S3, HTTPS) using DuckDB. Automatically detect file formats and preview/profile datasets.

Data Analysis Helper

A versatile data analysis assistant for loading datasets, performing statistical calculations, visualizing trends, and generating professional summary reports.

dlt-skill

Create, manage, and debug dlt (data load tool) pipelines for ingesting data from APIs, databases, and custom sources into destinations like DuckDB, BigQuery, and Snowflake.

sql-pro

Expert SQL agent for modern database systems, query optimization, HTAP environments, and data architecture patterns. Optimize performance, schema design, and analytical workloads effectively.

spark-optimization

Optimize Apache Spark jobs with partitioning strategies, memory management, shuffle tuning, and data skew mitigation for high-performance data processing pipelines.

snowflake-mcp

Integrate Snowflake with MCP clients. Manage Snowflake endpoints, validate connectivity, and leverage Cortex AI (Search, Analyst, Agent) services directly within your AI workflow.

senior-data-engineer

World-class senior data engineering skill for building scalable data pipelines, ETL/ELT systems, and modern data infrastructure using Python, Spark, dbt, and Kafka.

data-analysis

Data Analysis Specialist for EDA, statistical modeling, SQL queries, and Python-based visualization. Turn raw datasets into actionable insights through rigorous quantitative methods.

snowflake-semanticview

Create, alter, and validate Snowflake semantic views using the Snowflake CLI.

financial-statements

Generate financial statements (P&L, balance sheet, cash flow) with period-over-period comparisons, variance analysis, and GAAP compliance checks.

snowflake-semantic-views

Create, alter, and validate Snowflake semantic views via the CLI. Automate the generation, documentation, and testing of semantic layer definitions to ensure model accuracy and star schema compliance.