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Found 108 Skills
Automatically discover data pipeline and ETL skills when working with ETL, data pipelines, streaming, batch processing, data validation, or pipeline orchestration. Activates for data development tasks.
Use this skill when building real-time or near-real-time data pipelines. Covers Kafka, Flink, Spark Streaming, Snowpipe, BigQuery streaming, materialized views, and batch-vs-streaming decisions. Common phrases: "real-time pipeline", "Kafka consumer", "streaming vs batch", "low latency ingestion". Do NOT use for batch integration patterns (use integration-patterns-skill) or pipeline orchestration (use data-orchestration-skill).
Choose how and where to store football data. Use when the user asks about database choices, file formats, cloud storage, data pipelines, or how to organise their football data project. Also covers publishing and sharing outputs (Streamlit, Observable, GitHub Pages).
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.
Binary streaming between workers via channels. Use when building data pipelines, file transfers, streaming responses, or any pattern requiring binary data transfer between functions.
Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering.
Use Ibis for database-agnostic data access in Python. Use when writing data queries, connecting to databases (DuckDB, PostgreSQL, SQLite), or building portable data pipelines that should work across backends.
Data Catalog Updater - Auto-activating skill for Data Pipelines. Triggers on: data catalog updater, data catalog updater Part of the Data Pipelines skill category.
Use this skill when the user wants to explore lineage, trace data dependencies, perform impact analysis, find root causes, map data pipelines, or understand how data flows between systems. Triggers on: "what feeds into X", "what depends on X", "show lineage for X", "impact analysis", "trace the pipeline", "root cause", "upstream of X", "downstream of X", or any request involving data lineage and dependency tracking.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
Prefect Flow Builder - Auto-activating skill for Data Pipelines. Triggers on: prefect flow builder, prefect flow builder Part of the Data Pipelines skill category.
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.