microsoft

Deploy, evaluate, fine-tune, and manage Foundry agents end-to-end with azd: hosted agent scaffold/run/deploy, prompt agent create, batch eval, continuous eval, prompt optimizer, Agent Optimizer scaffold, agent.yaml, dataset curation from traces, model fine-tuning (SFT/DPO/RFT). USE FOR: azd ai agent, azd provision/deploy, deploy agent, hosted agent, create agent, add tool to agent, invoke agent, evaluate agent, continuous eval, continuous monitoring, optimize prompt, improve prompt, optimize agent instructions, agent optimizer, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, AI Services, create Foundry resource, provision, knowledge index, customize deployment, onboard, availability, fine-tune, SFT, DPO, RFT, training-data, grader, distillation, fine-tuned model, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Builds, tests, and deploys Microsoft 365 apps and agents for Teams and Copilot. Includes sub-skills for project creation, local testing, cloud deployment, troubleshooting, and Slack-to-Teams migration. USE FOR: Teams agent, bot, tab, message extension, Declarative Agents, Custom Engine Agents, local testing, Agents Playground, Azure resource provision, remote deployment, Slack to Teams migration, cross-platform bot development, Block Kit to Adaptive Cards conversion. DO NOT USE FOR: general web development, non-bot/non-Teams projects.

Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.

Azure Cosmos DB library for Rust (NoSQL API). Document CRUD, containers, and globally distributed data. Triggers: "cosmos db rust", "CosmosClient rust", "document crud rust", "NoSQL rust", "partition key rust".

Azure Blob Storage library for Rust. Upload, download, and manage blobs and containers. Triggers: "blob storage rust", "BlobClient rust", "upload blob rust", "download blob rust", "storage container rust", "BlobServiceClient rust".

Azure Event Hubs library for Rust. Send and receive events for streaming data ingestion and batch processing. Triggers: "event hubs rust", "ProducerClient rust", "ConsumerClient rust", "send event rust", "streaming rust", "eventhub rust".

Azure Identity library for Rust. Microsoft Entra ID authentication for all Azure SDK clients. Triggers: "azure identity rust", "DeveloperToolsCredential", "authentication rust", "managed identity rust", "credential rust", "Entra ID rust".

Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API, switch from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions to responses, gpt-5 migration, azure openai python migration, chat completions to responses, AzureOpenAI to OpenAI client, python azure openai upgrade. DO NOT USE FOR: building new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (this skill is Python-only), Azure infrastructure setup (use azure-prepare), deploying models (use microsoft-foundry).

ユーザーが実験、探索、またはチュートリアル用の Jupyter Notebook（`.ipynb`）を作成、スキャフォールド、または編集するよう依頼したときに使用します。バンドルされたテンプレートを優先し、ヘルパースクリプト `new_notebook.py` を実行してクリーンな開始ノートブックを生成してください。

사용자가 실험, 탐색 또는 튜토리얼을 위한 Jupyter 노트북(`.ipynb`)을 생성, 스캐폴딩하거나 편집해 달라고 요청할 때 사용합니다; 번들로 제공되는 템플릿을 우선 사용하고 깨끗한 시작 노트북을 생성하기 위해 헬퍼 스크립트 `new_notebook.py`를 실행하는 것을 권장합니다.

ប្រើនៅពេលដែលអ្នកប្រើស្នើឲ្យបង្កើត រៀបចំ (scaffold) ឬកែសម្រួល Jupyter notebooks (`.ipynb`) សម្រាប់សាកល្បង ស្វែងរក ឬមេរៀន; អនុសាសន៍ឲ្យប្រើទំរង់គំរូដែលភ្ជាប់មកជាមួយ និងរត់ស្គ្រីបជំនួយ `new_notebook.py` ដើម្បីបង្កើតសៀវភៅចាប់ផ្តើមដែលស្អាត។

ស្វែងរកឯកសារផ្លូវការរបស់ Microsoft ដើម្បីរកមើល គំនិត មេរៀន និងឧទាហរណ៍កូដ នៅលើ Azure, .NET, Agent Framework, Aspire, VS Code, GitHub និងផ្សេងទៀត។ ប្រើ Microsoft Learn MCP ជាលំនាំដើម ហើយប្រើ Context7 និង Aspire MCP សម្រាប់មាតិកាដែលស្ថិតនៅខាងក្រៅ learn.microsoft.com។

ಬಳಕೆದಾರರು ಪ್ರಯೋಗಗಳು, ಅನ್ವೇಷಣೆಗಳು ಅಥವಾ ಟ್ಯುಟೋರಿಯಲ್‌ಗಳಿಗಾಗಿ Jupyter ನೋಟ್ಬುಕ್‌ಗಳು (`.ipynb`) ರಚಿಸಲು, ಮೂಲ ರಚನೆ ಸಿದ್ಧಪಡಿಸಲು ಅಥವಾ ಸಂಪಾದಿಸಲು ಕೇಳಿದಾಗ ಬಳಸಿರಿ; ಸಂಯೋಜಿತ ಟೆಂಪ್ಲೇಟ್‌ಗಳಿಗೆ ಪ್ರಾಧಾನ್ಯ ನೀಡಿ ಮತ್ತು ಪ್ರಾರಂಭಿಕ ಸ್ವಚ್ಛ ನೋಟ್ಬುಕ್ ರಚಿಸಲು ಸಹಾಯಕ ಸ್ಕ್ರಿಪ್ಟ್ `new_notebook.py` ಅನ್ನು ರನ್ ಮಾಡಿ.

ಅಧಿಕೃತ Microsoft ಡಾಕ್ಯುಮೆಂಟೇಶನ್ ಅನ್ನು ಪ್ರಶ್ನೆ ಮಾಡಿ Azure, .NET, Agent Framework, Aspire, VS Code, GitHub ಮತ್ತು ಇತರೆ ಸಂಬಂಧಿತ ವಿಷಯಗಳ ಮೇಲೆ ತತ್ವಗಳು, ಟ್ಯುಟೋರಿಯಲ್ಗಳು ಮತ್ತು ಕೋಡ್ ಉದಾಹರಣೆಗಳನ್ನು ಕಂಡುಹಿಡಿಯಿರಿ. ಡೀಫಾಲ್ಟ್‌ವಾಗಿ Microsoft Learn MCP ಅನ್ನು ಬಳಸುತ್ತದೆ; learn.microsoft.com ಹೊರಗಿನ ವಿಷಯಗಳಿಗೆ Context7 ಮತ್ತು Aspire MCP ಅನ್ನು ಬಳಸಲಾಗುತ್ತದೆ.

ഉപയോക്താവ് പരീക്ഷണങ്ങൾ, അന്വേഷണങ്ങൾ, അല്ലെങ്കിൽ ട്യൂട്ടോറിയലുകൾക്കുള്ള Jupyter നോട്ട്ബുക്കുകൾ (`.ipynb`) സൃഷ്ടിക്കാൻ, സ്‌കാഫോൾഡ് ചെയ്യാൻ, അല്ലെങ്കിൽ തിരുത്താൻ ആവശ്യപ്പെടുമ്പോൾ ഉപയോഗിക്കുക; ബണ്ടിൽ ചെയ്ത ടെംപ്ലേറ്റുകൾ പ്രാഥമ്യം നൽകുക, കൂടാതെ ശുദ്ധമായ ആരംഭ നോട്ട്ബുക്ക് സൃഷ്ടിക്കാൻ സഹായി സ്ക്രിപ്റ്റ് `new_notebook.py` ഓടിക്കുക.

Create, validate, and iteratively improve agent skills that follow Anthropic's best practices. Use this skill whenever someone wants to create a new skill, improve an existing skill, write a SKILL.md, set up a skill directory structure, or needs guidance on skill authoring patterns. Also triggers when users mention "custom instructions", "agent skill", "skill template", or want to codify a workflow into a reusable skill. This skill enforces a create-validate-improve loop that produces production-ready skills passing code review on the first submission.

How to reconcile tasks.md with plan.md when plan changes are detected

Migrates Azure Functions projects from in-process Startup hooks (FunctionsStartup, IFunctionsHostBuilder) to the isolated worker model with Program.cs service registration. Use when upgrading Azure Functions from in-process to isolated, migrating Startup.cs to Program.cs, removing Microsoft.Azure.Functions.Extensions, or converting FunctionsHostBuilderContext to HostBuilderContext. Triggers for Startup.cs, FunctionsStartup, ConfigureFunctionsWorkerDefaults, ConfigureFunctionsWebApplication, and project files (.csproj, .vbproj, .fsproj) referencing Microsoft.NET.Sdk.Functions.

Migrates Azure Functions projects from legacy HostBuilder or in-process model to the modern Version 2.x pattern using IHostApplicationBuilder and Application Insights. Use when upgrading Azure Functions to isolated worker V2, replacing HostBuilder with FunctionsApplication.CreateBuilder, or adding Application Insights telemetry. Triggers for project files (.csproj, .vbproj, .fsproj) with Microsoft.Azure.Functions.Worker or Microsoft.NET.Sdk.Functions packages, and Program.cs files using HostBuilder patterns.

Build tool selection and orchestration for .NET projects during modernization upgrades. Use this skill whenever the agent modifies code files, project files, packages, or configuration and needs to validate the build — which is virtually every upgrade task. Covers: choosing between `dotnet build` and `msbuild.exe`, restore strategy, targeted vs solution builds, resolving MSBuild errors (MSB3086, MSB4019, NETSDK1005, etc.), handling multi-targeting scenarios, resource generation failures with .resx files, WPF/WinForms/XAML compilation issues, SDK-style vs legacy csproj differences, and recovering when a build fails. Also use when diagnosing why a project compiles under one tool but not another, or when deciding build scope (single project vs full solution) during iterative migration work.

Converts .NET projects and solutions to NuGet Central Package Management (CPM) with Directory.Packages.props. Use when the user wants to centralize, convert, align, or sync NuGet package versions across multiple projects, resolve version conflicts or mismatches, or get versions consistent across a solution or repository. Also triggers when packages are out of sync or drifting across projects.

Converts legacy non-SDK-style .NET project files (.csproj, .vbproj, .fsproj) to modern SDK-style format while preserving target frameworks, dependencies, and build behavior. Use when converting old-format .NET projects, migrating from packages.config to PackageReference, or modernizing project files. Also triggers for "convert to SDK style", "modernize csproj", "update project format", "legacy project migration", and "SDK-style conversion".

Manages NuGet packages in old-style .NET Framework projects (.NET Framework 4.x). Handles both packages.config (classic NuGet) and PackageReference in non-SDK-style csproj files. Use when updating, adding, or removing NuGet packages in projects that use packages.config, have old-style csproj format (ToolsVersion attribute), or target .NET Framework without SDK-style project format. Also triggers for "nuget update", "packages.config", "update package in old project", or "HintPath" tasks.

Use when the user wants to start a new Power Apps mobile app (Expo / React Native / TypeScript, targeting iOS and Android) from scratch.

Use when the user has finished building a mobile app, started it with `npm run dev`, and wants the running app monitored for runtime errors AND silent failures (empty lists, blank screens, swallowed network errors) and fixed autonomously. Accepts a free-text symptom (e.g., `/debug-app "todos not appearing on home screen"`) to drive terminal-log diagnostics — injects temporary console.log statements at data-path boundaries, reads Metro terminal output, and cleans up logs after the root cause is fixed. Otherwise polls the Metro terminal every 5s, classifies errors using an 8-category table, fixes inline or routes to the right skill, verifies each fix from terminal output, and exits after 3 consecutive clean polls. Foreground loop — blocks the conversation while running. Run only after the app is loaded.

Use when the user wants to deploy / publish / push a Power Apps mobile app to a Power Platform tenant so others can run it.

Use when the user wants to iterate on an existing generated Power Apps mobile app after /create-mobile-app: update the plan, data model, native capabilities, design, screens, generated app code, and preview without restarting the full project flow.

Use when the user wants to report a bug, file an issue, submit a bug report, or report any problem with the mobile-app plugin.

Use when the user wants to add a Power Platform connector to a mobile app and there is no dedicated /add-* skill for that connector (Dataverse and SharePoint have their own skills).

Use when the user wants to add a data source to a mobile app but hasn't said which kind (Dataverse table, SharePoint list, or other connector).

Use when the user wants to add Dataverse tables (existing or new) to a Power Apps mobile app, extend an existing Dataverse table with new columns, or apply an approved data model plan.

Agents window architecture — covers the agents-first app, layering, folder structure, chat widget, menus, contributions, entry points, and development guidelines. Use when implementing features or fixing issues in the Agents window.

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

Use when adding, modifying, or reviewing VS Code configuration policies. Covers the full policy lifecycle from registration to export to platform-specific artifacts. Run on ANY change that adds a `policy:` field to a configuration property.

Use this when working on the VS Code integrated browser ("browserView") to understand its architecture and mental model. Covers the embedded Chromium browser, its editor tab, navigation, overlay/layout, sessions, and agent browser tools under `src/vs/platform/browserView` and `src/vs/workbench/contrib/browserView`.

Investigate unexpected behavior in the current Copilot CLI agent session by analyzing its event log. Use when the user asks why something happened, why a request was slow, why a tool was or was not used, or why instructions/skills/agents did not load.

Use when validating Azure DevOps pipeline changes for the VS Code build. Covers queueing builds, checking build status, viewing logs, and iterating on pipeline YAML changes without waiting for full CI runs.

Act on user feedback attached to the current session. Use when the user submits feedback on the session's changes via the Submit Feedback button.

Use when running VS Code smoke tests or working on smoke-test CI steps. Covers npm run smoketest / smoketest-no-compile, grep filtering tests, and a temporary repeat-loop technique for tracking down flaky smoke tests in CI.

Consolidated accessibility skill entrypoint for WCAG 2.2, ARIA Authoring Practices, cognitive accessibility, Section 508, EN 301 549, and the Accessibility Planner workflow.

OpenVEX v0.2.0 specification reference plus VEX management playbooks - Brought to you by microsoft/hve-core.

Generate customer-card PowerPoint content YAML from Design Thinking canonical artifacts and build using the shared PowerPoint skill pipeline

Video-to-GIF conversion with FFmpeg two-pass optimization

Retrieves and groups GitHub code scanning alerts by rule and severity using the gh CLI

PowerPoint slide deck generation and management using python-pptx with YAML-driven content and styling

Mural workspace, room, mural, and widget workflows via the Mural REST API exposed through a Python CLI. Use when you need to read or write Mural content or automate widget creation.

Text-to-speech voice-over generation from YAML speaker notes using Azure Speech SDK with SSML pronunciation control

**WORKFLOW SKILL** — Iteratively improve skill frontmatter compliance using the Ralph loop pattern. WHEN: "run sensei", "sensei help", "improve skill", "fix frontmatter", "skill compliance", "frontmatter audit", "score skill", "check skill tokens". INVOKES: token counting tools, test runners, git commands. FOR SINGLE OPERATIONS: use token CLI directly for counts/checks.

Guidelines for writing Agent Skills that comply with the agentskills.io specification. WHEN: "create a skill", "new skill", "write a skill", "skill template", "skill structure", "review skill", "skill PR", "skill compliance", "SKILL.md format", "skill frontmatter", "skill best practices".

Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.

Deploy, evaluate, fine-tune, and manage Foundry agents end-to-end with azd: hosted agent scaffold/run/deploy, prompt agent create, batch eval, continuous eval, prompt optimizer, Agent Optimizer scaffold, agent.yaml, dataset curation from traces, model fine-tuning (SFT/DPO/RFT). USE FOR: azd ai agent, azd provision/deploy, deploy agent, hosted agent, create agent, add tool to agent, invoke agent, evaluate agent, continuous eval, continuous monitoring, optimize prompt, improve prompt, optimize agent instructions, agent optimizer, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, AI Services, create Foundry resource, provision, knowledge index, customize deployment, onboard, availability, fine-tune, SFT, DPO, RFT, training-data, grader, distillation, fine-tuned model, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

Author, validate, and run Vally eval.yaml evaluation suites for agent skills. TRIGGERS: create eval, write eval, add eval, run eval, validate eval, vally eval, eval.yaml, add stimulus, map test to eval, migrate test to eval, eval graders, eval scoring, add eval to CI.

Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.

Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, can't connect / RDP / SSH, refused, black screen, reset password, reach VM, port 3389, NSG, security, Linux, troubleshoot, troubleshooting, connectivity, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.

Azure cost management: query costs, forecast spending, optimize to reduce waste. WHEN: "Azure costs", "Azure bill", "cost breakdown", "how much am I spending", "forecast spending", "optimize costs", "reduce spending", "orphaned resources", "rightsize VMs", "cost spike", "reduce storage costs", "AKS cost". DO NOT USE FOR: deploying resources, provisioning, diagnostics, or security audits.

Activate when the user asks about APM (Agent Package Manager): installing, configuring, authoring, or troubleshooting AI-agent packages, dependencies, compilation, MCP servers, policy, or any `apm` CLI command.

Use this skill to run a multi-persona expert advisory review on a labelled pull request in microsoft/apm. The panel fans out to five mandatory specialists plus a test-coverage specialist (active on every PR that touches src/) plus three conditional specialists (auth, doc-writer, performance-expert), all running in their own agent threads, and a CEO synthesizer. The orchestrator is the sole writer to the PR: ONE recommendation comment, no verdict labels, no merge gating. The panel is advisory -- it surfaces findings, prioritizes follow-ups, and renders a ship-recommendation that the maintainer and author weigh. Activate when a non-trivial PR needs a cross-cutting recommendation (architecture, CLI logging, DevX UX, supply-chain security, growth/positioning, optionally auth, docs, perf, and test coverage, with CEO arbitration).

Use this skill to run a four-panel adversarial advisory review on any pull request that touches the OpenAPM specification artifact (docs/src/content/docs/specs/openapm-*.md), its inline / sidecar JSON Schemas (docs/src/content/docs/specs/schemas/*.schema.json), or the conformance fixture seed (tests/fixtures/spec-conformance/**). The panel fans out to four spec-ecosystem reviewers (swagger-openapi-editor, oci-distribution-editor, pkgmgr-registry-contract-editor, w3c-tag-architect), each running in its own agent thread, and a spec-editor synthesizer that produces a fold-now / defer-v0.1.1 / defer-v0.2 / reject list plus a ship decision keyed off a 1..10 shocked_meter scale. The orchestrator is the sole writer to the PR: ONE consolidated comment, no verdict labels, no merge gating. The panel is advisory -- it surfaces findings, prioritizes folds, and renders a ship recommendation that the maintainer weighs.

Activate for changes to project positioning, release communication, community-facing artifacts, or breaking-change decisions in microsoft/apm. Triggers on README, MANIFESTO, PRD, CHANGELOG, release workflows, and issue templates.

Activate when code touches token management, credential resolution, git auth flows, GITHUB_APM_PAT, ADO_APM_PAT, AuthResolver, HostInfo, AuthContext, or any remote host authentication -- even if 'auth' isn't mentioned explicitly.

Use this skill when editing or creating CLI output, logging, warnings, error messages, progress indicators, or diagnostic summaries in the APM codebase. Activate whenever code touches console helpers (_rich_success, _rich_warning, _rich_error, _rich_info, _rich_echo), DiagnosticCollector, STATUS_SYMBOLS, CommandLogger, or any user-facing terminal output — even if the user doesn't mention "logging" or "UX" explicitly.

Use this skill to cut an APM release from the current worktree: assess whether the cycle since the last tag warrants a patch or minor bump (semver discipline against the merged-since-last-tag diff), sanitize the [Unreleased] CHANGELOG block into a dated version block with one concise "so what" entry per merged PR (drop internal-only churn, consolidate duplicates), bump pyproject.toml + uv.lock, run the CI-mirror lint chain, and open the release PR. Activate on "ship a release", "cut v0.x", "release prep", "bump and PR", "open release PR", "what kind of release do we need", or any phrasing that ends in opening a release PR -- even when the user does not say "skill". Stops BEFORE tagging; tagging stays a human gate that triggers the release workflow. Refuses to bump to a major (>= 1.0.0) version without explicit operator confirmation.

Activate when designing or modifying CLI command surfaces, command help text, install/init/run flows, error wording, or first-run experience in the APM CLI -- even when the user does not say "UX" explicitly.

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".

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 flattening, (5) wire destinations, (6) modify/delete Eventstream definitions. Triggers: "create eventstream", "deploy eventstream", "eventstream topology", "add source to eventstream", "add event hub source", "add filter operator", "eventstream filter", "eventstream destination", "CDC source", "eventstream operator", "eventstream definition", "update eventstream", "wire eventstream", "real-time ingestion pipeline", "eventstream topology deployment".

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".

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) validate Eventstream configurations, (4) check Eventstream retention policy and throughput level, (5) get connection strings. Triggers: "list eventstreams", "inspect eventstream", "inspect eventstream topology", "eventstream sources and destinations", "eventstream health", "eventstream configuration", "eventstream retention", "eventstream retention policy", "eventstream throughput level", "eventstream connection string", "custom endpoint credentials", "kafka connection", "check eventstream".

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 `semantic-model-consumption` instead. 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".

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", "search Fabric for an item", "search for item", "find an item by name across workspaces", "which workspace has", "discover items across the tenant".

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".

Use when building an Amplifier-powered workflow or automation tool and deciding how to expose it — as standalone .dot attractor pipelines (incl. inside the Resolve dot-graph resolver), an importable Python lib, agent-callable tool modules, or a CLI. Covers the four leverage levels, the DRY rule that keeps logic in ONE home, and the judgment for which levels a real consumer actually needs (and when adding a level is just ceremony).

Convene the persona panel on the CURRENT conversation / work-in-progress — the plan, design, or decision you've been building in this session. The INLINE counterpart to /council (which forks and runs isolated, so it cannot see the chat). Use when you want the council to critique what we're working on right now.

Convene the persona panel (six orthogonal review lenses) on a target — cold independent fan-out, debate-to-consensus, synthesized verdict with recorded dissent and a roster manifest.

Analyze images using LLM vision APIs (Anthropic Claude, OpenAI GPT-4, Google Gemini, Azure OpenAI). Use when tasks require: (1) Understanding image content, (2) Describing visual elements, (3) Answering questions about images, (4) Comparing images, (5) Extracting text from images (OCR). Provides ready-to-use scripts - no custom code needed for simple cases.

Simplicity-obsessed design reviewer that interrogates complexity, questions every abstraction, and insists on the minimal viable design. Sounds like a senior engineer who has watched too many systems collapse under their own weight and now treats every unnecessary layer as a personal affront. Not a generalist skeptic — a simplicity zealot. A lens for any checkpoint — brainstorm, design, plan, implement, debug, or review — not just design. Use when: anything looks more complex than the problem needs — a speculative idea, an abstraction, a layer, an over-built fix — any time the worry is "do we actually need this, or can it be deleted?"

Curmudgeonly engineering advisor that provides grounded skepticism, evidence-linked judgment, and constructive progress on architectural decisions, legacy refactors, tooling choices, and broad "how should I start?" questions. Sounds like a senior systems engineer who has reviewed too many designs to be impressed, but still cares about correctness. A lens for any checkpoint — brainstorm, design, plan, implement, debug, or review — not just up-front decisions. Use when: weighing consequences, hidden costs, or failure modes of any choice — an idea, an architecture, a tooling/legacy call, an implementation path, or a fix — any time the worry is "what will this cost us later?"

Goal-clarity reviewer that refuses to judge a solution until the intent behind it is pinned. Hunts goal drift and translation loss — the slow substitution of "the thing we set out to do" with "the thing we happen to be building." Sounds like a patient, relentless interrogator of "why are we doing this?" who will not be hurried past the question. Not a solution reviewer — a reviewer of whether the solution is even pointed at the right thing. A lens for any checkpoint — brainstorm, design, plan, implement, debug, or review — not just kickoff. Use when: the deliverable has quietly become the goal, the build has wandered from the brief, or nobody can say in one sentence what success looks like — any time the worry is "is this still the real goal?"

Research and plan a large-scale change, then execute it in parallel across isolated agents that each open a PR.

Use when the user explicitly asks for a CI check or to push their branch — e.g. "ci readiness", "check ci", "pre-push check", "ready for CI", "ci check", "ready to push", "push my changes", "push the branch", "let's push". Catches common CI failures before pushing — formatting, stale API reports, missing changesets, policy violations.

Fluid Framework client release group — minor releases, patch releases, and post-release type test updates. Covers release prep, branching, version bumps, changelogs, release notes, and type test baselines. In autonomous mode, auto-detects state from the schedule and repo, attempts to execute, and falls back to a GitHub issue on failure. Triggers on "release", "do the release", "release status", version bump, release notes, changelog, release branch, or release engineering.

IMMEDIATELY USE THIS SKILL when creating or develop anything and before writing code or implementation plans - refines rough ideas into fully-formed designs through structured Socratic questioning, alternative exploration, and incremental validation

Use when implementing user interfaces or user experiences - guides through exploration of design variations, frontend setup, iteration, and proper integration

Use this skill when faced with a difficult debugging task where you need to replicate some bug or behavior in order to see what is going wrong.

Use when you need to create a new custom skill for a profile - guides through gathering requirements, creating directory structure, writing SKILL.md, and optionally adding bundled scripts

Use this when you have completed some feature implementation and have written passing tests, and you are ready to create a PR.

Use this skill to split large plans into smaller chunks. This skill manages your context window for large tasks. Use it when a task will take a long time and cause context issues.

Generate API and service communication contracts with sequence diagram

Generate architecture diagram with component relationship details from project analysis

Run application assessment for a single repository

Generate core business workflow documentation with sequence diagram

Generate comprehensive configuration and externalized settings inventory

Create a modernization plan to migrate the project to Azure

Generate task DAGs for modernization projects — select fragments from task catalog, produce initial DAG (Stage 1), and execute/validate DAG from plan artifacts (Stage 2).

Generate data architecture and persistence layer documentation with data model diagram