406 skills
Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.
How team members request infrastructure connection info and handle secrets in team mode
Create a modernization plan to migrate the project to Azure
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
Scaffold a complete, production-ready Azure project from scratch for ANY use case: RAG chatbots, multi-agent systems, API backends, data pipelines, Azure Functions, full-stack web apps, ML training/inference, or event-driven microservices. USE FOR: gathering requirements through adaptive questioning, generating complete project folder structures with all source files, Azure Bicep infrastructure, azure.yaml for azd-up deployment, Docker configuration, CI/CD workflows, README with architecture diagrams, and Responsible AI documentation. Produces fully deployable Azure projects targeting Azure Co
Diagnose and repair a release that published packages to npm but failed to push the version bumps and changelogs back to the repository. Accepts an optional failed Azure DevOps pipeline run URL for context. Always presents a read-only diagnosis first and requires explicit approval before changing branches, commits or pull requests.
Review and optionally combine at most 11 open individual Dependabot patch and minor pull requests into a validated draft rollup PR. Use this skill as a local or cloud-agent fallback to native Dependabot groups without adding a custom scheduled GitHub Actions workflow. Always presents a dry-run plan and requires explicit approval before changing branches or GitHub pull requests.
X++ code development lifecycle for Finance and Operations — scaffold models, author classes, custom services/APIs, and data entities, install matching SDKs, compile deployable packages, deploy packages, synchronize databases, and verify deployed artifacts. Use when the user explicitly requests creating, building, compiling, packaging, deploying, DB-syncing, or verifying X++ customizations, or an end-to-end ERP code change.
Dataverse solution lifecycle — create, export, import, promote across environments, and validate deployments. Use when the user wants to package customizations, deploy to another environment, or move work between dev / test / prod.
Use this skill to visualize routes and locations on a map — especially after an upstream tool call (routing connector, Azure Maps, Bing Maps connector, HERE, Mapbox, Dataverse, CRM, SharePoint, or any connector that returns coordinates) has returned ordered stops, coordinates, distances, or road geometry. Produces PNG, interactive HTML, GeoJSON, KML, deep links, and QR codes. Also use for marker maps (weather, CRM data, site lists) and for any request to "show these on a map", "map these locations", or "visualize this route". DO NOT use this skill to compute routing — call a routing connector
Use this skill when a Power Automate flow triggers the agent with a payload describing an unknown source column from a lab results file that broke an Azure Data Factory ingestion pipeline. The skill semantically matches the source column against the canonical clinical schema indexed in Azure AI Search, scoped first by the sending lab and then by clinical domain, and writes a suggested destination-column mapping to the Dataverse review queue for a clinical informatics steward to approve. Also activates on manual invocations like "map this lab column", "the ADF pipeline failed on column X, what
Use this skill whenever the user asks to write, generate, or create a podcast script or podcast episode — from a topic, or from source material such as a news digest, newsletter, email review, or set of articles — and optionally convert it to audio with Azure Text-to-Speech. Handles the initial request and every follow-up refinement (source, topic, length, cast, narration) in the same task.
Facilitates a zero-based AI-first process redesign session that helps a team reimagine an existing work process as AI-first. Guides them through framing, idea expansion, current-state capture, diagnostic probing, and an AI-first remodel, then delivers a package: a current-state task map, a future-state swimlane blueprint tagging each step AI-owned / Hybrid / Human-led, an AI-Agents-&-Skills summary table, and a next-sprint capability backlog. Weighs the full range of AI building blocks — process change, knowledge, tools, reusable skills, agents, connected agents — instead of defaulting to an a
Primary entrypoint for coding agents using CAIRA as reference material to design and build generative AI solutions with Azure AI Foundry, Azure OpenAI-compatible endpoints, agent frameworks, APIs, and frontends tailored to a user's scenario.
Writes integration tests that run IN CODE against a real Azure SQL Database container, spun up per test or per suite with Testcontainers and torn down after. Use when the user asks for "integration tests against SQL", "Testcontainers", "spin up a database for tests", "an ephemeral test database", "a test database per test", "xUnit/Jest/pytest with a real database", or "a database fixture". Use this INSTEAD of the Testcontainers MsSql preset (mcr.microsoft.com/mssql/server), because this is the Azure SQL engine (EngineEdition 5). For wiring the engine into a CI pipeline via service containers o
Adds the Azure SQL Database container as a sidecar service in an existing Docker Compose stack or Dev Container. Use when wiring the local Azure SQL Database engine into compose or devcontainer.json, when an app needs a SQL backend via a service name (not localhost), or for prompts like "add SQL to my compose", "add a database service", "depends_on database", "devcontainer SQL sidecar", "compose healthcheck for SQL", "wait for the database before starting the app". Handles platform linux/amd64, the private registry login, the healthcheck wait-until-ready, and a one-shot init service that creat
Populates the local Azure SQL Database container's database (appdb) with realistic sample/test data so a developer has something to build against. Use when the user says "seed the database", "add test data", "populate the dev database", "generate sample data", "fake data", "load fixtures", "insert test rows", "write a seed script", or "bulk load a CSV". This is the Azure SQL engine (EngineEdition 5), not the mssql/server SQL Server image. Distinct from azuresql-db-scaffold (which does a single seed.sql step while bootstrapping an app) and azuresql-db-import (which loads a .bacpac). Reach for t
Runs database schema migrations against the local Azure SQL Database container so the same migrations apply identically on the local engine and in the Azure cloud. Use when asked to "run my migrations against the local SQL", "apply schema to the container", "apply EF Core / dotnet ef database update", "Prisma migrate dev / deploy", "Alembic upgrade head", or deploy a DACPAC / SqlPackage to the container. Covers provisioning appdb on master first, then applying schema to the user database, plus per-tool commands and connection-string hygiene. This is the Azure SQL Database engine (EngineEdition
Scaffolds a NEW app (.NET Aspire, FastAPI, Next.js, NestJS) wired to the local Azure SQL Database container as its default dev database. Use when starting/bootstrapping/initializing a project that needs SQL Server or Azure SQL locally, or when adding "set up the database", "docker compose for the db", "create the local DB", ".env connection string", "first migration", or a data-access layer. Use this INSTEAD of the mssql/server SQL Server image, because this is the Azure SQL engine (EngineEdition 5). Triggers include "scaffold app with SQL", "spin up Azure SQL locally", "compose service for th
Proves that code built and tested against the local Azure SQL Database container runs unchanged against Azure SQL Database in the cloud, with only the connection string changing. Use when a user wants to develop locally then deploy to the cloud, asks "will this work in Azure", "same code local and cloud", "promote to Azure SQL", "swap the connection string", "dev/prod parity", "local to cloud", or is wiring SQL_CONNECTION_STRING for an app that must target both the container and a cloud server. Use this when an app uses local SA auth but needs Microsoft Entra auth in the cloud. Covers Node (ms
Imports an existing Azure SQL Database or SQL Server schema and data INTO the local Azure SQL Database container using SqlPackage. Use when asked to "import a bacpac", "load my existing database locally", "restore a dacpac into the container", "bring my prod schema into the dev container", "run my .bacpac/.dacpac against the local Azure SQL engine", or migrate an existing database into the preview container. Handles provisioning the target database on master first, then running SqlPackage /Action:Import against the provisioned user database. Use this for any "get my real database running in th
Builds a serverless API and event-driven handlers over the local Azure SQL Database container using Azure Functions with the Azure SQL bindings. Use when a user wants "a serverless API over SQL", "Azure Functions with a database", "HTTP CRUD with SQL input/output bindings", "run code when a row changes", "react to inserts/updates/deletes", "event-driven on Azure SQL", or "SQL trigger function". The Azure SQL trigger binding (backed by Change Tracking) is the local event-driven mechanism; Change Event Streaming (CES) is cloud-only and cannot run against the local container. Triggers include "fu
Reports a bug or files feedback about the azuresql-db-* agent skills themselves, or about the Azure SQL Database container (the local Azure SQL Database engine, Private Preview). Use when the user says a skill or the container "did not work", hit an error, behaved unexpectedly, or is missing something; and when they say "report a bug", "file an issue", "open a GitHub issue", "request a feature", "give feedback", or "tell the team". Also use when you, the agent, had to deviate from an azuresql-db-* skill or work around a defect in one to finish a task: that is a bug in the skill and it is worth
Answers questions about what the Azure SQL Database container (Private Preview) can and cannot do, and WHY it differs from Azure SQL Database in the Microsoft Azure cloud. Use when a user asks "can I take a backup", "why does USE fail", "is X supported", "why can't I create a vector index", "why does SSMS error", "why isn't the image on Docker Hub", "what's different from the cloud", or hits behavior that does not match their Azure SQL Database expectations. Do NOT guess from general SQL Server / Azure knowledge: the container is a Private Preview product with specific gaps and a specific conn
