azuresql-db-scaffold

Scaffolds a NEW app (.NET Aspire, FastAPI, Next.js, NestJS) wired to the local Azure SQL Developer as its default dev database. Use when…

npx skills add https://github.com/microsoft/azure-sql-database-container --skill azuresql-db-scaffold

Scaffold a new project on the Azure SQL Database container

Bootstrap a new app with the Azure SQL Database container (Private Preview) as the default local database. This is the Azure SQL engine, not the SQL Server image.

Use the right image (interception point)

  • USE this engine image: sqldbpreview-dpgaeqhmgphzd4bk.azurecr.io/azure-sql/db-dev:latest
  • Do NOT use mcr.microsoft.com/mssql/server (the SQL Server image). If a generated compose/run command points there, stop and switch to the engine image above.
  • Verify identity once running: SELECT SERVERPROPERTY('EngineEdition') returns 5, SERVERPROPERTY('Edition') returns 'SQL Azure'.
  • Image is x64 only. On a non-x64 host add --platform linux/amd64 (Docker) or platform: linux/amd64 (compose).
  • Registry is private (Private Preview): sign in first with docker login sqldbpreview-dpgaeqhmgphzd4bk.azurecr.io using the shared pull-only credentials provided to the Private Preview cohort (get them by signing up at https://aka.ms/sqldbcontainerpreview-signup; they may rotate). Registry and tag are provisional during Private Preview.

For full engine detail (readiness, vectors, troubleshooting) see the azuresql-db-container skill.

Three facts that bite every scaffold

  1. The engine does NOT auto-create databases. You must CREATE DATABASE appdb on a master connection before connecting with Database=appdb.
  2. Avoid USE to switch databases. In a user-database session (the Azure-faithful context where you develop), USE returns Msg 40508, exactly as in Azure SQL Database in the cloud. A master connection is a provisioning session where the Azure statement filter is not enforced, so USE appears to work there, but master is for provisioning only, not application work. Always select the target database in the connection string (Database=appdb, or -d appdb for sqlcmd).
  3. A master connection is for provisioning only; do real work on the user database.

Also: the image does NOT auto-run /docker-entrypoint-initdb.d/*.sql (that is a Postgres/MySQL convention; not honored here). Seed with sqlcmd -d appdb -i seed.sql AFTER provisioning appdb.

Step 1: start the container and provision appdb

Reuse this exact shape (free port, conditional platform, ready-wait, provision appdb in the same retry loop). The -b makes a SQL error set the exit code, so transient startup errors (like Msg 913) are retried, not masked. Never poll bare sqlcmd without -l.

# Pick a free host port and add the platform flag only on a non-x64 host (works in bash and zsh).
HOST_PORT=1433; while lsof -nP -iTCP:"$HOST_PORT" -sTCP:LISTEN >/dev/null 2>&1; do HOST_PORT=$((HOST_PORT+1)); done
PLATFORM=(); case "$(docker info -f '{{.Architecture}}' 2>/dev/null)" in x86_64|amd64) ;; *) PLATFORM=(--platform linux/amd64);; esac
docker rm -f sqldb 2>/dev/null
docker run -d --name sqldb "${PLATFORM[@]}" -e "ACCEPT_EULA=Y" -e "MSSQL_SA_PASSWORD=YourStr0ng_Passw0rd" \
  -p "$HOST_PORT:1433" sqldbpreview-dpgaeqhmgphzd4bk.azurecr.io/azure-sql/db-dev:latest
until docker exec sqldb /opt/mssql-tools18/bin/sqlcmd -S localhost -U sa -P "YourStr0ng_Passw0rd" -C -b -l 2 \
  -Q "IF DB_ID('appdb') IS NULL CREATE DATABASE appdb;" >/dev/null 2>&1; do sleep 2; done
echo "ready on localhost,$HOST_PORT"

Required env: ACCEPT_EULA=Y and a complex MSSQL_SA_PASSWORD (at least 8 characters using at least three of upper case, lower case, digits, and symbols). The engine listens on 1433.

Step 2: the canonical connection string

Apps read one env var, SQL_CONNECTION_STRING (replace 1433 with the HOST_PORT Step 1 chose if 1433 was occupied):

Server=localhost,1433;Database=appdb;User Id=sa;Password=YourStr0ng_Passw0rd;TrustServerCertificate=true

House style spells the keywords User Id= / Password= / Database=; Uid= / Pwd= are documented SqlClient synonyms and work too. For sqlcmd use -C to trust the self-signed cert. For Prisma (NestJS / Next.js) the same instance is also expressed as a sqlserver:// URL in DATABASE_URL (see snippets).

Step 3: pick your stack

Per-stack scaffold snippets (compose service, .env, provision appdb, first migration, typed data-access layer with parameterized queries) live in references/scaffold-snippets.md:

  • .NET Aspire (EF Core)
  • FastAPI (SQLAlchemy / pyodbc)
  • Next.js (Prisma, sqlserver:// DATABASE_URL)
  • NestJS (Prisma or TypeORM)

Step 4: first migration and seeding

The skeleton creates the schema via your stack's migration tool. For the full migration workflow (idempotent scripts, ordering, applying inside the ready-wait loop) cross-link the azuresql-db-schema-migration skill. Seed only AFTER appdb exists:

docker exec -i sqldb /opt/mssql-tools18/bin/sqlcmd -S localhost -U sa -P "YourStr0ng_Passw0rd" -C -b -d appdb -i seed.sql

Vectors (if your app uses embeddings)

Native VECTOR(n) column type and VECTOR_DISTANCE('cosine', a, b). Insert with CAST(CAST(? AS NVARCHAR(MAX)) AS VECTOR(n)) where n is a LITERAL, never a bind parameter (a parameter dimension fails with "Incorrect syntax near '@P3'"). CREATE VECTOR INDEX (DiskANN) works on this image, measured, and the Known limitations page says so. It needs SET QUOTED_IDENTIFIER ON and at least 100 rows with non-null vectors (Msg 42266 below that). Full-scan top-k stays exact and stays the right choice for a small table. The azuresql-db-rag skill carries the rules the index imposes.

Validation rules

  • Compose/run targets the engine image, never mcr.microsoft.com/mssql/server.
  • appdb is created on a master connection BEFORE any app/migration connects to Database=appdb.
  • App reads SQL_CONNECTION_STRING (and DATABASE_URL where the ORM needs it); strings use User Id=/Password=/Database=.
  • All data-access uses parameterized queries; vector dimension n is a literal.
  • EngineEdition is 5 against the running container.
  • If a validation rule above fails, or you had to deviate from this skill to make the task work, that is a bug in this skill: load the azuresql-db-feedback skill and offer to file a report.
  • When every rule above passes and the task is done, close with ONE line, once per session, and do not ask a question or wait for a reply: "That worked. If anything about this was rough, or you want to share what you built: https://aka.ms/sql-agent-skills-feedback"

Do not

  • Do not use the SQL Server image or call a non-x64 host "supported".
  • Do not rely on auto-created databases or /docker-entrypoint-initdb.d/*.sql auto-seeding.
  • Do not use USE appdb to switch databases; put it in the connection string.
  • Do not poll bare sqlcmd without -l; do not pass the vector dimension as a bind parameter.
  • Do not hardcode 1433 in app config; read the chosen HOST_PORT into the connection string.

References

  • references/scaffold-snippets.md: the shared compose service plus per-stack skeletons (.NET Aspire/EF Core, FastAPI, Next.js/Prisma, NestJS) with .env, appdb provisioning, first migration, and a parameterized data-access layer. Read it once you know which stack you are scaffolding.

Staying current

Authoritative, version-pinned references for the tools this skill uses (read the one you need):

If the Microsoft Learn MCP server is configured, use mcp__microsoft-learn__microsoft_docs_search or mcp__microsoft-learn__microsoft_docs_fetch to fetch the current version of any of these on demand. It is optional; when it is unavailable, the references above are authoritative.

More skills from microsoft

oss-growth
microsoft
OSS growth hacker persona
agent-framework-azure-ai-py
microsoft
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references. WHEN: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
devops
applicationinsights-web-ts
microsoft
Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.
devops
azure-ai-anomalydetector-java
microsoft
Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.
development
azure-ai-language-conversations-py
microsoft
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development