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Found 130 Skills
Build a chronological narrative for a topic by interleaving meeting transcript excerpts and email thread summaries across a time range.
Apache Spark distributed computing. Use for big data processing.
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, implementing real-time data processing, handling stateful operations, or optimizing streaming performance.
Sendspark integration. Manage data, records, and automate workflows. Use when the user wants to interact with Sendspark data.
Build and modify EdgeSpark apps. Use when a project has edgespark.toml, the user mentions EdgeSpark, or work involves the edgespark CLI, server SDK types, storage/auth/database workflows, deployment, or @edgespark/web.
Audit team assignment distribution: per-member loads, delegated items, unassigned work, and workload imbalances.
End-to-end ELT pipeline using SSIS, SQL Server, and PySpark for enterprise data warehousing and analytics
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.
Query a running Apache Spark History Server from Copilot CLI. Use this whenever the user wants to inspect SHS applications, jobs, stages, executors, SQL executions, environment details, or event logs, especially when they mention Spark History Server, SHS, event log history, benchmark runs, or application IDs.
Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.
End-of-day review: check for loose ends, review pinned items, and preview tomorrow's calendar.
Analyze meeting load over a time range: total meetings, meeting hours, busiest days, back-to-back chains, and free blocks.