From cc-polymath
Auto-discovers and loads skills for ETL, data pipelines, batch/stream processing, data validation, and orchestration. Activates for data development tasks like real-time analytics and incremental computation.
How this skill is triggered — by the user, by Claude, or both
Slash command
/cc-polymath:discover-dataThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Provides automatic access to comprehensive data skills.
Provides automatic access to comprehensive data skills.
This skill auto-activates when you're working with:
The Data category contains 9 skills:
For complete descriptions and workflows:
Read ../data/INDEX.md
This loads the full Data category index with:
Load individual skills as needed:
Read ../data/batch-processing.md Read ../data/data-validation.md Read ../data/etl-patterns.md Read ../data/pipeline-orchestration.md
Read ../data/stream-processing.md Read ../data/streaming-aggregations.md
Read ../data/timely-dataflow.md Read ../data/differential-dataflow.md Read ../data/dataflow-coordination.md
Read ../data/stream-processing.md # Kafka setup Read ../data/streaming-aggregations.md # Windowing patterns Read ../data/dataflow-coordination.md # Coordination
Read ../data/timely-dataflow.md # Foundation Read ../data/differential-dataflow.md # Incremental updates Read ../data/dataflow-coordination.md # Distributed coordination
Read ../data/batch-processing.md # Batch jobs Read ../data/stream-processing.md # Stream processing Read ../data/pipeline-orchestration.md # Overall coordination
This gateway skill enables progressive loading:
Read ../data/INDEX.md for full category overviewNext Steps: Run Read ../data/INDEX.md to see full category details.
npx claudepluginhub rand/cc-polymath --plugin cc-polymathDesign batch and streaming data pipelines. Plan ingestion, transformation, quality checks, and failure recovery. Use when building ETL/ELT systems or data infrastructure.
Builds scalable data pipelines, modern data warehouses, and real-time streaming architectures using Apache Spark, dbt, Airflow, and cloud-native platforms.
Designs data pipelines and ETL processes covering extraction, transformation, loading, data quality checks, orchestration, and patterns for batch, streaming, CDC, ELT. Useful for building pipelines, data flows, syncing, or moving data between systems.