PatrickGallucci/fabric-skills Skills

Create, configure, and manage Microsoft Fabric Data Agents that enable natural language Q&A over lakehouses, warehouses, Power BI semantic models, KQL databases, and ontologies. Use when asked to build data agents, configure NL2SQL/NL2DAX/NL2KQL experiences, write agent instructions, create example queries, automate data agent provisioning via REST API or PowerShell, integrate Fabric data agents with Azure AI Foundry, or troubleshoot data agent configuration issues.

Diagnose and resolve Microsoft Fabric Data Agent issues including tenant settings, data source configuration, query generation failures, cross-region capacity errors, XMLA endpoint setup, Power BI semantic model integration, lakehouse/warehouse/KQL connectivity, example query validation, publishing/sharing problems, and Azure AI Foundry integration. Use when asked to troubleshoot data agent, fix Fabric AI agent, debug NL2SQL/NL2DAX/NL2KQL, resolve Copilot tenant settings, or diagnose Fabric data agent errors.

Diagnose and resolve Microsoft Fabric Data Factory pipeline performance issues. Use when pipelines are slow, copy activities timeout, dataflows stall, activities are stuck, throughput is low, capacity is throttled, or jobs queue indefinitely. Covers copy activity tuning (parallelCopies, DIU, ITO, partitioning), pipeline monitoring via Monitoring Hub and workspace monitoring, Spark job queueing, capacity SKU limits, error code resolution, and dataflow optimization. Keywords include Fabric pipeline slow, copy activity performance, Data Factory throttling, pipeline timeout, activity stuck, TooManyRequestsForCapacity, HTTP 430, pipeline troubleshoot, dataflow performance, copy parallelism, intelligent throughput optimization.

Diagnose and resolve Microsoft Fabric Dataflow Gen2 performance issues including slow refresh times, Fast Copy optimization, query folding failures, staging bottlenecks, gateway latency, incremental refresh tuning, capacity throttling, and data destination write performance. Use when troubleshooting dataflow refresh failures, optimizing Dataflow Gen2 execution time, debugging Power Query mashup engine performance, resolving staging Lakehouse or Warehouse compute issues, configuring Fast Copy connectors, fixing insufficient permissions errors, monitoring dataflow refresh history, or automating dataflow health checks via REST API and PowerShell.

Troubleshoot and optimize Delta Lake and Apache Spark performance in Microsoft Fabric. Use when diagnosing slow Spark jobs, small file problems, data skew, shuffle bottlenecks, out-of-memory errors, V-Order tuning, OPTIMIZE/VACUUM operations, partition strategy, resource profile selection (writeHeavy, readHeavyForSpark, readHeavyForPBI), autotune configuration, Native Execution Engine, broadcast joins, AQE (Adaptive Query Execution), or when Spark notebooks or Spark Job Definitions run slower than expected in Fabric Lakehouse workloads.

Guide for working with Microsoft Fabric IQ (preview), the semantic intelligence workload for unified data and business vocabulary. Use when creating or managing ontology items, defining entity types, binding data to ontologies, creating relationship types, configuring data agents with ontology sources, working with Graph in Microsoft Fabric, managing Fabric IQ tenant settings, querying ontology graphs, generating ontologies from Power BI semantic models, or remediate Fabric IQ preview features. Covers ontology, graph, data agent, operations agent, and semantic model items.

Troubleshoot Microsoft Fabric Lakehouse access control issues including OneLake security roles, SQL analytics endpoint permissions, workspace roles, data access roles, row-level security (RLS), column-level security (CLS), object-level security (OLS), dynamic data masking, shortcut permissions, Direct Lake security integration, DefaultReader role, ReadAll permission, and OneLake data access role conflicts. Use when users report permission denied, unauthorized access, missing data, empty query results, or cannot see tables in Fabric Lakehouse.

Diagnose and resolve Microsoft Fabric Lakehouse performance issues including slow Spark queries, small file problems, Delta table fragmentation, V-Order configuration, table maintenance (OPTIMIZE, VACUUM, Z-Order), SQL analytics endpoint tuning, Direct Lake performance, resource profile selection, autotune configuration, capacity throttling, and streaming ingestion optimization. Use when asked to troubleshoot Fabric Lakehouse slowness, optimize Delta tables, fix small file problems, configure Spark settings, run table maintenance, or improve query performance in notebooks or pipelines.

Troubleshoot Microsoft Fabric materialized lake views (MLV) performance issues including slow refresh, incremental refresh failures, full refresh fallback, Spark job failures, lineage execution errors, data quality constraint violations, and optimal refresh configuration. Use when diagnosing MLV refresh duration, monitoring MLV runs in Monitor Hub, analyzing Spark logs for MLV failures, enabling change data feed (CDF), resolving delta table not found errors, or optimizing MLV query definitions for incremental refresh eligibility. Covers Spark SQL syntax, TBLPROPERTIES, PARTITIONED BY, CONSTRAINT CHECK ON MISMATCH DROP/FAIL, and custom environment configuration.

Diagnose and resolve Microsoft Fabric network performance issues including connectivity failures, latency, private endpoints, managed VNets, outbound access protection, gateway diagnostics, OneLake endpoint routing, service tag configuration, firewall allowlisting, DNS resolution, and Spark session startup delays. Use when remediate Fabric networking, slow Spark jobs, connection timeouts, private link errors, managed private endpoint approval, or capacity throttling.

Diagnose and resolve performance issues in Microsoft Fabric notebooks running Apache Spark. Use when notebooks are slow, Spark jobs are timing out, sessions hit HTTP 430 throttling errors, data skew causes straggling tasks, shuffle operations are inefficient, Delta Lake tables need optimization, V-Order configuration is needed, capacity utilization is high, OOM errors occur, or Spark Advisor warnings appear. Covers Spark session tuning, Native Execution Engine, autotune, custom Spark pools, partition optimization, and Monitoring Hub diagnostics.

Diagnose and resolve Microsoft Fabric OneLake performance issues including slow queries, cold cache latency, small file problems, Delta table fragmentation, V-Order optimization, Spark throttling, capacity SKU sizing, and cross-region data access. Use when remediate OneLake read/write performance, lakehouse query slowness, Direct Lake fallback, table maintenance failures, Spark concurrency limits, warehouse cold starts, or optimizing Delta parquet file layouts. Supports PowerShell, T-SQL, and Spark SQL diagnostic workflows.

Troubleshoot and optimize pandas performance in Microsoft Fabric Spark notebooks. Use when diagnosing slow pandas operations, toPandas() out-of-memory errors, pandas API on Spark (pyspark.pandas) bottlenecks, DataFrame conversion failures, collect() memory issues, driver memory exhaustion, notebook cell timeouts, or when optimizing pandas workloads for Fabric capacity. Covers pandas vs Spark DataFrame conversion, memory profiling, broadcast joins, shuffle tuning, resource profiles, and Native Execution Engine integration.

Diagnose and resolve Power BI performance issues in Microsoft Fabric. Use when reports load slowly, DAX queries are inefficient, semantic models need optimization, DirectQuery is underperforming, capacity is throttled, visuals render slowly, or data refresh is slow. Covers Performance Analyzer, DAX Studio, Fabric Capacity Metrics app, VOrder, query folding, incremental refresh, storage modes, Best Practice Analyzer, and Spark resource profiles for Power BI workloads.

Diagnose and resolve Microsoft Fabric Power BI security issues including row-level security (RLS), object-level security (OLS), column-level security (CLS), workspace permissions, sensitivity labels, service principal authentication, XMLA endpoint access, DirectLake security fallback, Entra ID app registration, and data loss prevention (DLP) policy restrictions. Use when remediate access denied errors, missing data in reports, broken visuals from OLS, RLS not filtering, sensitivity label problems, permission model conflicts, or capacity-related security issues.

Monitor and optimize Microsoft Fabric capacity, Spark compute, and workload performance. Use when asked to check capacity utilization, diagnose throttling (HTTP 430), monitor Spark VCore consumption, analyze CU usage, review Monitoring Hub jobs, query Fabric REST APIs for capacity health, generate performance reports, tune Spark resource profiles, investigate concurrency limits, or optimize Fabric SKU sizing. Supports PowerShell, T-SQL, and REST API workflows.

Diagnose and resolve Apache Spark performance issues in Microsoft Fabric notebooks and Spark Job Definitions. Use when PySpark jobs are slow, notebooks take too long, Spark stages are skewed, shuffles are excessive, out-of-memory errors occur, Delta Lake writes are slow, or Fabric capacity is throttled. Covers data skew, shuffle optimization, broadcast joins, partition tuning, VOrder, Optimized Write, resource profiles, autotune, native execution engine, small file compaction, and Spark UI interpretation. Keywords include slow notebook, OOM, spill, shuffle, skew, broadcast, repartition, coalesce, OPTIMIZE, VACUUM, Z-ORDER, checkpoint, cache, persist, executor memory, driver memory, spark.sql.shuffle.partitions, autoBroadcastJoinThreshold, maxPartitionBytes, Fabric capacity throttling, CU utilization.