skills-for-fabric

Manage Microsoft Fabric Materialized Lake View (MLV) refresh schedules and job execution via REST APIs. Create, update, and delete refresh schedules (interval-based: hourly, daily, weekly). Trigger on-demand refreshes, monitor job status, and cancel running jobs. Uses human-in-the-loop confirmations for safety. Materialized Lake Views are also known as Spark Materialized Views, MLVs, or lakehouse materialized views in Fabric documentation. Note: MLV discovery (list MLVs, lineage, data quality) requires UI as REST APIs are not yet available. Triggers: "schedule MLV refresh", "manage MLV", "MLV refresh schedule", "schedule materialized lake view", "schedule materialized view", "automate MLV refresh", "trigger MLV refresh", "monitor MLV refresh", "MLV job status", "cancel MLV refresh", "refresh schedule", "MLV automation", "manage materialized lake view", "manage materialized view", "materialized view refresh", "spark materialized view schedule", "lakehouse materialized view", "refresh my materialized views"

Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".

Create and evolve Fabric IQ Ontology (preview) items from CLI — define entity types, properties (including timeseries), relationship types, and bind them to OneLake lakehouse tables (static + timeseries) or Eventhouse / KQL database tables (timeseries only). Uses the Fabric item-definition REST API (Create Item / Update Item Definition) with `InlineBase64` parts. Use to create a Fabric Ontology item; add or alter entity types, properties, or keys; add timeseries properties and bindings; bind an entity type to a lakehouse or Eventhouse table; add relationship types and contextualizations; or script ontology deployment from source. Triggers: "create fabric ontology", "add ontology entity type", "bind entity type to lakehouse", "bind entity type to eventhouse", "ontology timeseries binding", "add ontology relationship type", "ontology contextualization", "fabric iq ontology authoring", "update ontology definition"

Develop Microsoft Fabric Spark/data engineering workflows and write code in Fabric Notebook cells with intelligent routing to specialized resources. Provides workspace/lakehouse management, notebook code authoring (PySpark, Scala, SparkR, SQL), and Materialized Lake View (MLV) authoring (Spark SQL MLVs support incremental refresh; PySpark is full-refresh only). Routes to data engineering patterns, development workflow, or infrastructure orchestration. Triggers: "develop notebook", "data engineering", "workspace setup", "pipeline design", "Delta Lake patterns", "Spark development", "lakehouse configuration", "write notebook code", "notebookutils", "notebook cell", "PySpark notebook", "%%sql cell", "%%configure", "fabric notebook", "run notebook", "notebook deployment", "materialized lake view", "MLV", "CREATE MATERIALIZED LAKE VIEW", "MLV incremental refresh", "review MLV for incremental refresh", "MLV refresh policy", "infrastructure provisioning"

Explore Fabric IQ Ontology (preview) items (read-only) from the CLI to ground an agent before it queries data. Explore, describe, and summarize what an ontology exposes — its entity types, keys, relationships, and the bindings that map each concept onto a lakehouse or Eventhouse source — then route the underlying data query to the matching per-datasource consumption skill (eventhouse-consumption-cli, spark-consumption-cli, sqldw-consumption-cli). Read-only discovery via Get Item Definition; never writes to or alters an ontology. Use to explore or summarize an ontology, describe its schema and data lineage, build agent grounding context, or run an ontology-backed query over the source records. Triggers: "query fabric ontology", "explore fabric ontology", "list ontology entities", "enumerate ontology entity types", "describe ontology", "ontology grounding context", "ground query with ontology", "query ontology entity data", "fabric iq ontology consumption", "ontology-backed query", "ontology entity bindings"

Diagnose failed Spark jobs, unhealthy Livy sessions, and performance bottlenecks in Microsoft Fabric via read-only CLI triage. Use when the user wants to: (1) diagnose why a Spark job, notebook run, or Lakehouse job failed, (2) triage stuck or dead Livy sessions, (3) identify OOM, shuffle spill, or data skew, (4) retrieve driver and executor logs or Spark Advisor findings, (5) copy event logs and start a local Spark History Server, (6) diagnose all Spark activities within a failed pipeline run. Triggers: "diagnose my failed notebook", "why did my spark job fail", "triage spark failure", "diagnose pipeline run failure", "why did my pipeline fail", "livy session stuck in starting", "spark executor OOM", "check spark advisor findings", "shuffle spill diagnosis", "why did my lakehouse job fail", "diagnose lakehouse table load", "data skew diagnosis", "open spark history server locally", "analyze spark failure logs", "spark job triage".

Create, update, delete, and refresh Fabric Dataflows Gen2 via write-side CLI against Fabric Items and Connections APIs. Builds mashup.pq + queryMetadata definitions, triggers parameterized refreshes, manages connections, and configures output destinations (Lakehouse, Warehouse, ADX, Azure SQL). Includes preview-driven authoring loop (executeQuery + customMashupDocument). Lists `supportedConnectionTypes`/`credentialType` per connector. For executing saved queries or reading refresh status, use `dataflows-consumption-cli`. Triggers: "create dataflow", "update dataflow", "delete dataflow", "trigger dataflow refresh", "refresh dataflow", "preview Power Query M", "preview mashup", "preview before save", "iterate dataflow M", "create Fabric data source connection", "create dataflow connection", "bind connection", "list supportedConnectionTypes", "dataflow output destination", "dataflow write to lakehouse", "dataflow write to warehouse", "dataflow write to ADX", "DataDestinations annotation".

Develops and manages Power BI semantic models across Desktop, PBIP projects, and Fabric Service. Handles: (1) creating new models (Import, DirectQuery, Direct Lake), (2) editing existing models (e.g. measures, tables, columns, relationships), (3) deploying models to Fabric workspaces, (4) working with PBIP project files, (5) refreshing semantic models, (6) configuring data sources and permissions, (7) DAX performance optimization. Supports both Power BI Desktop and Fabric Service development workflows. For read-only DAX queries, use `semantic-model-consumption`. Does NOT handle report layout/visual authoring, workspace administration, or RLS/OLS role membership management. Triggers: "create semantic model", "edit semantic model", "add a DAX measure to semantic model", "refresh semantic model", "set semantic model permissions", "Prepare semantic model for AI/Copilot".

Monitor, inspect, and query saved Fabric Dataflows Gen2 via read-only CLI. List dataflows, decode base64 definitions (mashup.pq, queryMetadata.json, .platform), discover parameters, retrieve refresh status and job history, classify queries by staging, and execute queries against saved dataflows via the read-side `executeQuery` mashup engine (Arrow IPC response). Runs persisted or ad-hoc read-only executeQuery requests; parses/renders Arrow results. For previewing candidate M before persisting, or for `supportedConnectionTypes`/`credentialType` discovery and connection configuration, use `dataflows-authoring-cli` (not this skill). Triggers: "list dataflows", "inspect dataflow", "decode dataflow definition", "dataflow parameters", "dataflow refresh status", "refresh history", "last refresh status", "dataflow job history", "execute dataflow query", "executeQuery saved query", "executeQuery fetch rows", "ad-hoc dataflow query", "parse Arrow response", "Arrow IPC", "dataflow staging analysis".

Answer business questions by querying Power BI reports and dashboards through the FabricIQ MCP endpoint. Orchestrates: discover Power BI artifacts, inspect report/model schemas, resolve entity values, generate DAX, execute queries. Returns plain-language answers from Power BI semantic models. Use when the user asks a natural-language question about Power BI report or dashboard content (not raw DAX). Triggers: "ask power bi", "PBI question", "discover report", "report data", "dashboard data", "what are the top", "show me the power bi data", "which products sold", "compare sales in report".

Migrate Synapse Data Factory pipeline artifacts to Microsoft Fabric Data Factory. Handles: linked services → Fabric connections, dataset definitions inlined into pipeline activities, global parameters → Variable Libraries, SynapseNotebook activities → TridentNotebook. SSIS, SHIR-only, and Databricks activities are parked. Use when: (1) migrating Synapse pipelines to Fabric Data Factory, (2) converting SynapseNotebook activities to TridentNotebook, (3) translating linked services to Fabric connections, (4) converting global parameters to Fabric Variable Libraries, (5) inlining dataset definitions into Fabric pipeline activities. Triggers: "synapse pipeline to fabric", "data factory pipeline migration", "tridentnotebook pipeline activity", "global parameters to variable library", "linked service to fabric connection", "inline dataset fabric pipeline", "pipeline migration from synapse".

Create and modify Power BI report files in PBIR/PBIP format using the `powerbi-report-author` and `powerbi-desktop` CLIs. Use when the user wants to: (1) implement an approved report spec or design brief, (2) add or edit pages, visuals, filters, slicers, bookmarks, themes, or formatting, (3) validate PBIR and verify rendering in Power BI Desktop. For open-ended visual design, use `powerbi-report-design` first. For end-to-end requirements and approval workflow, use `powerbi-report-planning` first. Triggers: "edit PBIR", "create Power BI report page", "add visual to PBIP", "format report visual", "validate Power BI report", "reload Desktop screenshot", "implement an approved PBIP report spec", "edit PBIR pages/visuals".

Generate Power BI report visual design guidance before PBIR files are written. Use when the user wants to: (1) choose tone, signature, page archetypes, chart types, layout, color, typography, theme direction, or accessibility approach, (2) redesign/restyle an existing report, apply a brand, or critique chart/layout choices, (3) produce a design contract for `powerbi-report-authoring`. For end-to-end requirements, approval, and build sequencing, use `powerbi-report-planning`. Triggers: "design Power BI report", "make dashboard look professional", "choose chart type", "apply brand to report", "redesign report", "create design brief".

Manage Power BI report workspace items in Microsoft Fabric via `az rest` CLI against the Fabric REST API. Use when the user wants to: (1) create reports from PBIR definitions, (2) get or download report definitions, (3) update report definitions or properties, (4) list workspace reports, (5) delete reports. For report layout authoring (pages, visuals, filters, formatting), use `powerbi-report-authoring`. Triggers: upload Power BI report, download PBIR definition, publish Power BI report to Fabric, manage Power BI reports.

Build a guided requirements-to-implementation workflow for new Power BI reports and dashboards from semantic models, datasets, or PBIP projects. Use when the user wants to: (1) plan then implement a report, (2) define audience, scope, page plan, design direction, dependencies, and delivery target, (3) create a locked report spec with approval before PBIR authoring. For direct edits to existing report files, use `powerbi-report-authoring`. For design-only critique or redesign, use `powerbi-report-design`. Triggers: "build me a dashboard", "create a new report", "plan then implement", "define and build Power BI report", "walk me through creating a report".

Create alerts, notifications, and automated actions on Fabric data and events via Fabric REST API and `az rest` CLI. **Invoke this skill** whenever the user wants to: (1) create, update, or delete an alert or notification flow, (2) send a Teams message, email, or run a Fabric item when something happens, (3) connect alert logic to Eventhouse, Eventstream, Real-time Hub, or DTB / Ontology data, (4) adjust thresholds, filters, event triggers, or actions, (5) troubleshoot or change an existing Activator/Reflex definition. Invoke this skill **before** asking clarifying questions — clarification is part of this skill, not a preamble to it. Triggers: "create an alert", "create an activator", "create a reflex", "create an activator item", "create an alert item", "notify me when", "let me know when", "take action when", "send me an email when", "send a teams message when", "run a pipeline when", "update an alert", "delete an alert", "activator rule"

Assess, plan, and execute dataflow Gen1 → Gen2.1 CI/CD save-as operations via CLI (az rest / curl) against Power BI REST and Fabric REST APIs. Scan workspaces or entire tenants for Gen1 dataflows, evaluate save-as readiness with seven risk signals (incremental refresh, BYOSA storage, Power Automate triggers, pipeline dependencies, linked entities, DirectQuery, caller-not-owner), produce a Save-As Readiness Snapshot (markdown + JSON), and invoke the SaveAsNativeArtifact API to create upgraded Gen2.1 copies of Gen1 dataflows. **Invoke this skill** whenever the user wants to: (1) discover Gen1 dataflows in a workspace or tenant, (2) assess save-as readiness and risk signals, (3) upgrade or migrate Gen1 into a Gen2.1 copy, (4) validate post-save-as data integrity, (5) detect residual Gen1 references. Triggers: "save Gen1 dataflow", "convert dataflow Gen1", "upgrade dataflow", "migrate dataflow", "dataflow readiness", "Gen1 to Gen2", "dataflow save-as assessment", "saveAsNativeArtifact", "dataflow save-as scan".

Inspect existing alerts, notifications, and automated actions in Fabric via read-only REST API calls using `az rest` CLI. **Invoke this skill** whenever the user wants to: (1) list existing alerts in a workspace, (2) inspect how an alert or notification is configured, (3) read and decode an Activator/Reflex definition (ReflexEntities.json), (4) list rules, sources, and actions behind an alert, (5) understand why an alert fires or what action it takes. **Invoke this skill before answering questions** about an Activator/Reflex item in a Fabric workspace — the listing, lookup, and decoding workflows are part of this skill, not preamble to it. Triggers: "show my alerts", "what alerts do I have", "inspect this alert", "show me the rule", "show me the action", "show me the source", "get reflex definition", "list activators", "list alerts", "list reflex items", "show activator items", "activator details", "find activator named"

Execute raw DAX queries and inspect metadata of Microsoft Fabric Power BI semantic models via the MCP server ExecuteQuery tool. Use when the user already knows the DAX to write, wants to run EVALUATE statements, or needs to inspect model metadata (tables, columns, measures, relationships, hierarchies) using INFO functions. For natural-language business questions (where you generate the DAX), use `fabriciq`. For creating, deploying, or managing semantic model definitions, use `semantic-model-authoring`. Triggers: "run DAX query", "execute EVALUATE", "semantic model metadata", "list semantic model tables", "INFO.VIEW.TABLES", "get measure expression", "DAX against", "query the model".

Port Databricks notebooks and jobs to Microsoft Fabric. Provides an exhaustive dbutils to notebookutils substitution table: fs operations (mount removal via OneLake Shortcuts), secret scope to Key Vault URL conversion, notebook run and exit, widget replacement with parameter-tagged cells, and library install replacement with Fabric Environments. Covers Unity Catalog three-level namespace reduction to Lakehouse two-level schemas, DBFS path conversion to OneLake, Databricks Jobs to Spark Job Definitions, MLflow tracking URI removal, and Photon to Native Execution Engine substitution. Use when the user wants to: (1) replace dbutils with notebookutils, (2) collapse Unity Catalog namespaces to Lakehouse schemas, (3) convert Databricks Jobs or Delta Live Tables. Triggers: "migrate from databricks", "databricks to fabric", "dbutils to notebookutils", "dbutils fabric", "unity catalog migration", "dbfs to onelake", "databricks notebook migration", "delta live tables fabric", "photon native execution".

Port Azure Synapse Analytics Spark workloads to Microsoft Fabric. Translates mssparkutils calls to notebookutils (including the env→runtime namespace change), replaces Linked Services with Fabric Data Connections and OneLake Shortcuts. Covers Spark Pools, Lake Databases, Notebooks, and Spark Job Definitions. Use when the user wants to: (1) port Synapse Spark notebooks to Fabric Lakehouse or Spark Job Definitions, (2) replace mssparkutils or Linked Services in Synapse code. Triggers: "migrate from synapse", "synapse to fabric", "mssparkutils to notebookutils", "synapse linked service replacement", "port synapse notebooks", "synapse workspace migration".

Check for skills-for-fabric marketplace updates at session start. Compares local version against GitHub releases and shows changelog if updates are available. Use when the user wants to: (1) check for skill updates, (2) see what's new in skills-for-fabric, (3) verify current version. Triggers: "check for updates", "am I up to date", "what version", "update skills", "show changelog".

Find and discover Microsoft Fabric items across workspaces when the workspace is unknown. Use when the user wants to: (1) find an item by name across workspaces, (2) list items of specific type across workspaces, (3) identify which workspace contains an item, (4) return item/workspace IDs for downstream API calls. Triggers: "which workspace has", "where is", "what items do I have", "do I have", "find item", "find all items", "search for item", "discover items", "find across workspaces".

Create, wire, and publish Microsoft Fabric Eventstream real-time event streaming topologies via the Fabric Items REST API. Build graph-based definitions with 25 source types (Event Hubs, IoT Hub, CDC connectors, Kafka, SampleData), 8 transformation operators (Filter, Aggregate, GroupBy, Join, ManageFields, Union, Expand, SQL), 4 destination types (Lakehouse Delta, Eventhouse, Activator, Custom Endpoint), and DefaultStream/DerivedStream routing. Use when the user wants to: (1) author or publish an Eventstream topology, (2) add CDC sources with SQL-based Debezium payload flattening, (3) assemble multi-table fan-out routing, (4) modify or delete Eventstream definitions. Triggers: "create eventstream", "deploy eventstream", "design eventstream topology", "CDC source", "eventstream operator", "real-time ingestion pipeline", "eventstream definition", "update eventstream".

List, inspect, and monitor Microsoft Fabric Eventstream real-time event ingestion pipelines via the Fabric Items REST API. Discover Eventstreams across workspaces, decode base64-encoded graph topologies to trace event flow from source through operators to destination nodes. Validate source connection IDs, destination wiring, retention policies (1-90 days), and throughput levels. Use when the user wants to: (1) list or search Eventstreams in a workspace, (2) decode and trace graph topology from source to destination, (3) validate source and destination configurations, (4) check retention and throughput settings. Triggers: "list eventstreams", "show eventstream", "inspect eventstream", "explain eventstream", "eventstream health", "monitor eventstream", "describe eventstream", "check eventstream configuration", "eventstream retention".

Port Azure HDInsight Spark clusters and Hive workloads to Microsoft Fabric. Removes legacy HiveContext and standalone SparkContext constructors, replacing them with the pre-instantiated SparkSession. Converts WASB and ABFS storage paths to OneLake abfss URLs via Shortcuts. Transforms Hive DDL (STORED AS ORC, external tables) to Delta Lake schemas inside Fabric Lakehouse. Maps Oozie workflow actions — spark, hive, shell, sqoop, coordinator — to Fabric Pipeline activities and schedule triggers. Introduces notebookutils for file and credential operations previously handled via subprocess or HDFS client calls. Use when the user wants to: (1) retire an HDInsight cluster and move to Fabric, (2) convert WASB paths or Hive DDL, (3) replace Oozie coordinators with Fabric Pipelines. Triggers: "migrate from hdinsight", "hdi to fabric", "hivecontext sparksession fabric", "wasb to onelake", "hive ddl to delta", "oozie to fabric pipelines", "hive metastore lakehouse", "hdinsight spark migration".

Analyze lakehouse data interactively using Fabric Lakehouse Livy API 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".

Analyze Fabric Data Warehouse performance via CLI using sqlcmd and queryinsights views. Diagnose slow queries, SQL pool pressure, cache coldness, and recommend clustering keys. Triggers: "DW slow query analysis", "slowest queries warehouse", "queryinsights long running", "warehouse CPU resource consumers", "SQL pool pressure window", "pressure events warehouse", "DW cache warmth cold start", "cache warmth analysis", "warehouse cluster key recommendation", "cluster tables performance", "DW performance baseline comparison", "performance degraded warehouse", "warehouse user query patterns", "queryinsights diagnostics", "DW optimization sqlcmd".

Execute KQL management commands (table management, ingestion, policies, functions, materialized views) against Fabric Eventhouse and KQL Databases via CLI. Use when the user wants to: 1. Create or alter KQL tables, columns, or functions 2. Ingest data into an Eventhouse (inline, from storage, streaming) 3. Configure retention, caching, or partitioning policies 4. Create or manage materialized views and update policies 5. Manage data mappings for ingestion pipelines 6. Deploy KQL schema via scripts Triggers: "create kql table", "kql ingestion", "ingest into eventhouse", "kql function", "materialized view", "kql retention policy", "eventhouse schema", "kql authoring", "create eventhouse table", "kql mapping"

Run KQL queries against Fabric Eventhouse for real-time intelligence and time-series analytics using `az rest` against the Kusto REST API. Covers KQL operators (where, summarize, join, render), Eventhouse schema discovery (.show tables), time-series patterns with bin(), and ingestion monitoring. Use when the user wants to: 1. Run read-only KQL queries against an Eventhouse or KQL Database 2. Discover Eventhouse table schema and metadata 3. Analyse real-time or time-series data with KQL operators 4. Monitor ingestion health and active KQL queries 5. Export KQL results to JSON Triggers: "kql query", "kusto query", "eventhouse query", "kql database", "real-time intelligence", "time-series kql", "query eventhouse", "explore eventhouse", "show tables kql"

Execute authoring T-SQL (DDL, DML, data ingestion, transactions, schema changes) against Microsoft Fabric Data Warehouse and SQL endpoints from agentic CLI environments. Use when the user wants to: (1) create/alter/drop tables from terminal, (2) insert/update/delete/merge data via CLI, (3) run COPY INTO or OPENROWSET ingestion, (4) manage transactions or stored procedures, (5) perform schema evolution, (6) use time travel or snapshots, (7) generate ETL/ELT shell scripts, (8) create views/functions/procedures on Lakehouse SQLEP. Triggers: "create table in warehouse", "insert data via T-SQL", "load from ADLS", "COPY INTO", "run ETL with T-SQL", "alter warehouse table", "upsert with T-SQL", "merge into warehouse", "create T-SQL procedure", "warehouse time travel", "recover deleted warehouse data", "create warehouse schema", "deploy warehouse", "transaction conflict", "snapshot isolation error".

Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".