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Found 194 Skills
Diagnose, compare, and optimize Apache Spark applications and SQL queries using Spark History Server data. Use this skill whenever the user wants to understand why a Spark app is slow, compare two benchmark runs or TPC-DS results, find performance bottlenecks (skew, GC pressure, shuffle spill, straggler tasks), get tuning recommendations, or optimize Spark/Gluten configurations. Also trigger when the user mentions 'diagnose', 'compare runs', 'why is this query slow', 'tune my Spark job', 'benchmark comparison', 'performance regression', or asks about executor skew, shuffle overhead, AQE effectiveness, or Gluten offloading issues.
End-of-day review: check for loose ends, review pinned items, and preview tomorrow's calendar.
Build a chronological narrative for a topic by interleaving meeting transcript excerpts and email thread summaries across a time range.
Sendspark integration. Manage data, records, and automate workflows. Use when the user wants to interact with Sendspark data.
Sales rep / account manager persona for Spark. Client relationship tracking, pipeline review, follow-up cadence, and deal context.
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
Process GateKeeper's new sender queue: review, accept legitimate contacts, and block unwanted senders.
Analyze lakehouse data interactively using Fabric Livy sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
Implement Syncfusion React Sparkline components for compact, inline data visualization. Use this when working with sparklines, mini charts, or trend indicators in constrained spaces. This skill covers all 5 sparkline types (line, column, area, win-loss, pie), tooltips, markers, data labels, range bands, axis customization, and themes. Ideal for displaying data trends within grids, dashboards, or tables without full-sized charts.
Audit team assignment distribution: per-member loads, delegated items, unassigned work, and workload imbalances.