Agent Skills

by pexoai

Turn product photos or a store URL into a polished product video with Pexo. Upload your product images (or download from a link, then upload) and Pexo composes the shots, adds motion, music, and transitions, and returns a publish-ready clip for your listing, ads, or socials. Use for e-commerce/product clips: "product video", "video for my product", "ecommerce video", "video from product photos". NOT for explainer or non-product content.

startup-video

by pexoai

Make a video for your startup with Pexo. Describe your company, product, or vision (or paste your site) and Pexo writes the script, generates the shots, picks the models, and returns a finished, publish-ready startup video with music — for your homepage, fundraising deck, or social channels. Use for startup brand and intro videos: "startup video", "video for my startup", "company intro video", "about us video". NOT for paid ad campaigns (use the video-ad skill).

text-to-video

by pexoai

Turn a text prompt or script into a finished, multi-shot video with Pexo. Auto-selects the best model across 10+ engines (Seedance, Kling, Veo, Sora), writes the prompts, generates every shot, and returns a publish-ready video with music and subtitles. Use when the user wants a video FROM TEXT: "make a video about X", "turn this script into a video", "text to video", "generate a video from this description". NOT for editing an existing video or turning an image into video (use the image-to-video

tiktok-video-ad

by pexoai

Create native TikTok video ads with Pexo — vertical 9:16, sound-on, hook-first. Give it your product and Pexo writes the script in TikTok's voice, sequences fast-paced shots, picks the models, and delivers a ready-to-post ad with trending-style music. Use for TikTok ads: "tiktok ad", "tiktok video ad", "ad for tiktok", "vertical video ad". NOT for landscape or long-form ads (use the video-ad skill).

veo-3.2-prompter

by pexoai

Expert prompt engineering for Google Veo 3.2 (Artemis engine). Use when the user wants to generate a video with Veo 3.2, needs help crafting cinematic prompts, or mentions Veo, Google video generation, or Artemis engine.

video-ad

by pexoai

Create scroll-stopping video ads with Pexo. Give it your product, brand, or a photo and Pexo writes the hook, sequences the shots, picks the best models, and delivers a finished ad with music — ready to run on Meta, TikTok, or YouTube. Use for advertising/promo video: "video ad", "make an ad", "product ad", "promo video", "commercial". NOT for explainer or tutorial content (use the explainer-video skill).

videoagent-audio-studio

by pexoai

Tired of juggling multiple audio APIs? This skill gives you one-command access to TTS, music generation, sound effects, and voice cloning. Use when you want to generate any audio without managing multiple API keys.

seedance-2.0-prompter

by pexoai

Expert prompt engineering for Seedance 2.0. Use when the user wants to generate a video with multimodal assets (images, videos, audio) and needs the best possible prompt.

videoagent-director

by pexoai

AI creative director that turns a user's natural-language idea into a complete storyboard and generates all assets — images, video clips, and audio — automatically. The user only describes what they want; all prompt engineering is handled internally.

videoagent-image-studio

by pexoai

Tired of juggling 8 API keys? This skill gives you one-command access to Midjourney, Flux, Ideogram, and more, with zero setup. Use when you want to generate any image without worrying about API keys.

databricks-migration

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

🔥🔥FreeQuick setup

dataflows-save-as-authoring-cli

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

🔥🔥FreeQuick setup

e2e-medallion-architecture

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.