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Found 17 Skills
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.
Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment
The foundational library for creating static, animated, and interactive visualizations in Python. Highly customizable and the industry standard for publication-quality figures. Use for 2D plotting, scientific data visualization, heatmaps, contours, vector fields, multi-panel figures, LaTeX-formatted plots, custom visualization tools, and plotting from NumPy arrays or Pandas DataFrames.
Load data into MotherDuck from local files, object storage, HTTPS, dataframes, or external databases. Use when choosing a MotherDuck-specific ingestion path, especially CTAS and INSERT...SELECT, bulk loading, secrets, and Postgres-endpoint versus DuckDB-client tradeoffs.
Pandas data manipulation with DataFrames. Use for data analysis.
This skill should be used when the user asks to "use pandas", "analyze data with pandas", "work with DataFrames", "clean data with pandas", or needs guidance on pandas best practices, data manipulation, performance optimization, or common pandas patterns.
Serverless GDS sessions on Neo4j Aura — covers GdsSessions, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection, gds.graph.project.remote, gds.graph.construct, algorithm execution (mutate/stream/write), async job polling, result retrieval, and session lifecycle. Use when running graph algorithms on Aura Business Critical or VDC, processing graph data from Pandas/Spark, or using the graphdatascience Python client in AGA (serverless) mode. Covers all three data source three source modes (AuraDB-connected, self-managed Neo4j, standalone from DataFrames). Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-skill.
Use this skill for data pipeline work — ingestion with dlt, transformations with sqlmesh, analytics with DuckDB/MotherDuck, DataFrames with polars, notebooks with marimo, and project management with uv.
Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.
Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".