Agent Skills

e-commerce

✓★ 1,129

by firecrawl

Navigate e-commerce sites to extract products, pricing, categories, and inventory. Handles pagination, variants, and JS-heavy storefronts.

structured-extraction

✓★ 1,129

by firecrawl

Extract structured data matching a JSON schema from websites. Handles complex nested schemas, arrays, pagination, and validation. Always outputs via formatOutput.

databricks-migration

✓★ 729

by microsoft

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 Defin

dataflows-save-as-authoring-cli

✓★ 729

by microsoft

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 Gen

e2e-medallion-architecture

✓★ 729

by microsoft

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: "medal

eventhouse-authoring-cli

✓★ 729

by microsoft

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 scrip

eventstream-authoring-cli

✓★ 729

by microsoft

Create, wire, and publish Fabric Eventstream real-time streaming topologies via the Items REST API. Build definitions with 25 source types (Event Hubs, IoT Hub, CDC, Kafka, SampleData), 8 operators (Filter, Aggregate, GroupBy, Join, ManageFields, Union, Expand, SQL), 4 destinations (Lakehouse, Eventhouse, Activator, Custom Endpoint), DefaultStream/DerivedStream routing. Use to: (1) author Eventstream topology, (2) add Event Hub source, (3) add filter operator, (4) add CDC source with Debezium fl

eventstream-consumption-cli

✓★ 729

by microsoft

List, inspect, and monitor Fabric Eventstream real-time ingestion pipelines via the Items REST API. Discover Eventstreams across workspaces, decode base64 graph topologies tracing event flow from source through operators to destination nodes. Validate connection IDs, wiring, retention policies (1-90 days), and throughput levels. Retrieve Custom Endpoint Kafka credentials via Topology API. Use to: (1) list Eventstreams, (2) inspect Eventstream topology showing sources and destinations, (3) valida

hdinsight-migration

✓★ 729

by microsoft

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. In

synapse-migration

✓★ 729

by microsoft

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 fro

activator-consumption-cli

✓★ 729

by microsoft

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 questio

fabriciq-ontology-authoring-cli

✓★ 729

by microsoft

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

dataflows-authoring-cli

✓★ 729

by microsoft

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 `d

dataflows-consumption-cli

✓★ 729

by microsoft

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, o

eventhouse-consumption-cli

✓★ 729

by microsoft

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

sqldw-operations-cli

✓★ 729

by microsoft

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 perfor

fabriciq

✓★ 729

by microsoft

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). For raw DAX execution (EVALUATE statements) or model metadata inspection (INFO functions), use `s

pipeline-migration

✓★ 729

by microsoft

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 connec

semantic-model-consumption

✓★ 729

by microsoft

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-auth

spark-consumption-cli

✓★ 729

by microsoft

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

sqldw-authoring-cli

✓★ 729

by microsoft

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/functio

sqldw-consumption-cli

✓★ 729

by microsoft

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.

fabriciq-ontology-consumption-cli

✓★ 729

by microsoft

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; neve

mlv-operations-cli

✓★ 729

by microsoft

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) r

powerbi-report-authoring

✓★ 729

by microsoft

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

search-consumption-cli

✓★ 729

by microsoft

Search the Microsoft Fabric catalog to find an item by name across all workspaces when you don't know which workspace it is in, using the Fabric Catalog Search API. Use when the user wants to: (1) search the catalog for an item by name across workspaces, (2) discover or list items of a specific type across the tenant, (3) identify which workspace contains an item, (4) return item/workspace IDs for downstream API calls. Triggers: "search for an item", "search the catalog", "catalog search", "sear

spark-authoring-cli

✓★ 729

by microsoft

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 engineer

powerbi-report-design

✓★ 729

by microsoft

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 Po

powerbi-report-management

✓★ 729

by microsoft

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 Fabri

powerbi-report-planning

✓★ 729

by microsoft

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