plugins/ssdt-master/skills/sql-server-2025/SKILL.md
SQL Server 2025 and SqlPackage 170.2.70 (October 2025) — vector databases, AI integration, and modern features. PROACTIVELY activate for: (1) SQL Server 2025 features, (2) vector type and vector indexes for AI, (3) Vector functions (VECTOR_DISTANCE, etc.), (4) AI integration (Azure OpenAI, embeddings, RAG), (5) JSON type and JSON_ARRAYAGG, (6) parameter sensitivity plan optimization, (7) SqlPackage 170.2.70 new features and switches, (8) Always Encrypted with secure enclaves on 2025, (9) ledger tables, (10) Azure Arc-enabled SQL Server. Provides: SQL 2025 changelog, vector type usage, AI integration patterns, SqlPackage 170.2.70 reference, and migration guidance.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace sql-server-2025Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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MANDATORY: Always Use Backslashes on Windows for File Paths
When using Edit or Write tools on Windows, you MUST use backslashes (\) in file paths, NOT forward slashes (/).
Examples:
D:/repos/project/file.tsxD:\repos\project\file.tsxThis applies to:
NEVER create new documentation files unless explicitly requested by the user.
SQL Server 2025 is the enterprise AI-ready database with native vector database capabilities, built-in AI model integration, and semantic search from ground to cloud.
SqlPackage 170.2.70 (October 14, 2025) - Latest production release with full SQL Server 2025 support, data virtualization, and parquet file enhancements.
Three major 2025 releases:
Data Virtualization (170.1.61+):
New Data Types:
New Permissions (170.0+):
ALTER ANY INFORMATION PROTECTION - SQL Server 2025 & Azure SQLALTER ANY EXTERNAL MIRROR - Azure SQL & SQL database in FabricCREATE/ALTER ANY EXTERNAL MODEL - AI/ML model managementDeployment Options:
/p:IgnorePreDeployScript=True/False - Skip pre-deployment scripts/p:IgnorePostDeployScript=True/False - Skip post-deployment scripts# Publish to SQL Server 2025
sqlpackage /Action:Publish \
/SourceFile:Database.dacpac \
/TargetServerName:server2025.database.windows.net \
/TargetDatabaseName:MyDatabase \
/TargetDatabaseEdition:Premium \
/p:TargetPlatform=SqlServer2025 # New target platform
# Extract from SQL Server 2025
sqlpackage /Action:Extract \
/SourceServerName:server2025.database.windows.net \
/SourceDatabaseName:MyDatabase \
/TargetFile:Database.dacpac \
/p:ExtractAllTableData=False \
/p:VerifyExtraction=True
# Export with SQL Server 2025 features
sqlpackage /Action:Export \
/SourceServerName:server2025.database.windows.net \
/SourceDatabaseName:MyDatabase \
/TargetFile:Database.bacpac
New ScriptDom version for SQL Server 2025 syntax parsing:
// Package: Microsoft.SqlServer.TransactSql.ScriptDom 170.0.64
using Microsoft.SqlServer.TransactSql.ScriptDom;
// Parse SQL Server 2025 syntax
var parser = new TSql170Parser(true);
IList<ParseError> errors;
var fragment = parser.Parse(new StringReader(sql), out errors);
// Supports SQL Server 2025 new T-SQL features
MAJOR MILESTONE: Microsoft.Build.Sql SDK entered General Availability in 2025!
Breaking Change from Preview:
Current Status: SQL Server 2025 target platform support coming in future Microsoft.Build.Sql release (post-2.0.0).
Workaround for SDK-Style Projects:
<!-- Database.sqlproj (SDK-style with SQL Server 2025 compatibility) -->
<Project Sdk="Microsoft.Build.Sql/2.0.0">
<PropertyGroup>
<Name>MyDatabase</Name>
<!-- Use SQL Server 2022 (160) provider until 2025 provider available -->
<DSP>Microsoft.Data.Tools.Schema.Sql.Sql160DatabaseSchemaProvider</DSP>
<TargetFramework>net8.0</TargetFramework>
<SqlServerVersion>Sql160</SqlServerVersion>
<!-- SQL Server 2025 features will still work in runtime database -->
<!-- Only build-time validation uses Sql160 provider -->
</PropertyGroup>
<ItemGroup>
<Folder Include="Tables\" />
<Folder Include="Views\" />
<Folder Include="StoredProcedures\" />
</ItemGroup>
</Project>
Requirement: Visual Studio 2022 version 17.12 or later for SDK-style SQL projects.
Note: Side-by-side installation with original SQL projects (legacy SSDT) is NOT supported.
Current Status: SQL Server 2025 (17.x) is in Release Candidate (RC1) stage as of October 2025. Public preview began May 2025.
Predicted GA Date: November 12, 2025 (based on historical release patterns - SQL Server 2019: Nov 4, SQL Server 2022: Nov 16). Expected announcement at Microsoft Ignite conference (November 18-21, 2025).
Not Yet Production: SQL Server 2025 is not yet generally available. All features described are available in RC builds for testing purposes only.
Full catalogue of engine features: native vector database (DiskANN indexing for AI / RAG workloads), JSON enhancements, regular expressions, fuzzy search, change event streaming, security and sensitivity classification, performance / Intelligent Query Processing additions, T-SQL improvements, and Always Encrypted updates lives in references/sql-server-2025-features.md. Load that reference when planning a 2025 upgrade or adopting any of those engine features in an SSDT project.
# Publish with 2025 features
sqlpackage /Action:Publish \
/SourceFile:Database.dacpac \
/TargetConnectionString:"Server=tcp:server2025.database.windows.net;Database=MyDb;Authentication=ActiveDirectoryManagedIdentity;" \
/p:BlockOnPossibleDataLoss=True \
/p:IncludeCompositeObjects=True \
/p:DropObjectsNotInSource=False \
/p:DoNotDropObjectTypes=Users;RoleMembership \
/p:GenerateSmartDefaults=True \
/DiagnosticsFile:deploy.log
<!-- Database.publish.xml -->
<Project>
<PropertyGroup>
<TargetConnectionString>Server=tcp:server2025.database.windows.net;Database=MyDb;Authentication=ActiveDirectoryManagedIdentity;</TargetConnectionString>
<BlockOnPossibleDataLoss>True</BlockOnPossibleDataLoss>
<TargetDatabaseName>MyDatabase</TargetDatabaseName>
<ProfileVersionNumber>1</ProfileVersionNumber>
</PropertyGroup>
<ItemGroup>
<SqlCmdVariable Include="Environment">
<Value>Production</Value>
</SqlCmdVariable>
</ItemGroup>
</Project>
# Deploy using MSBuild
msbuild Database.sqlproj \
/t:Publish \
/p:PublishProfile=Database.publish.xml \
/p:TargetPlatform=SqlServer2025
State-Based Deployment (Recommended):
Testing & Quality:
Security:
Version Control:
name: Deploy to SQL Server 2025
on:
push:
branches: [main]
jobs:
build-and-test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Setup .NET 8
uses: actions/setup-dotnet@v4
with:
dotnet-version: '8.0.x'
- name: Install SqlPackage 170.2.70
run: dotnet tool install -g Microsoft.SqlPackage --version 170.2.70
- name: Build DACPAC
run: dotnet build Database.sqlproj -c Release
- name: Run tSQLt Unit Tests
run: |
# Run unit tests and capture results
# Abort if tests fail
echo "Running tSQLt unit tests..."
# Add your tSQLt test execution here
- name: Generate Deployment Report
run: |
sqlpackage /Action:DeployReport \
/SourceFile:bin/Release/Database.dacpac \
/TargetConnectionString:"${{ secrets.SQL_CONNECTION_STRING }}" \
/OutputPath:deploy-report.xml \
/p:BlockOnPossibleDataLoss=True
- name: Publish to SQL Server 2025
run: |
sqlpackage /Action:Publish \
/SourceFile:bin/Release/Database.dacpac \
/TargetConnectionString:"${{ secrets.SQL_CONNECTION_STRING }}" \
/p:TargetPlatform=SqlServer2025 \
/p:BlockOnPossibleDataLoss=True \
/DiagnosticsFile:publish.log \
/DiagnosticsLevel:Verbose
- name: Upload Artifacts
if: always()
uses: actions/upload-artifact@v4
with:
name: deployment-logs
path: |
publish.log
deploy-report.xml
trigger:
- main
pool:
vmImage: 'windows-2022'
steps:
- task: MSBuild@1
displayName: 'Build Database Project'
inputs:
solution: 'Database.sqlproj'
configuration: 'Release'
- task: SqlAzureDacpacDeployment@1
displayName: 'Deploy to SQL Server 2025'
inputs:
azureSubscription: 'Azure Subscription'
authenticationType: 'servicePrincipal'
serverName: 'server2025.database.windows.net'
databaseName: 'MyDatabase'
deployType: 'DacpacTask'
deploymentAction: 'Publish'
dacpacFile: '$(Build.SourcesDirectory)/bin/Release/Database.dacpac'
additionalArguments: '/p:TargetPlatform=SqlServer2025'
# Enable detailed diagnostics
sqlpackage /Action:Publish \
/SourceFile:Database.dacpac \
/TargetServerName:server2025.database.windows.net \
/TargetDatabaseName:MyDatabase \
/DiagnosticsLevel:Verbose \
/DiagnosticPackageFile:diagnostics.zip
# Creates diagnostics.zip containing:
# - Deployment logs
# - Performance metrics
# - Error details
# - Schema comparison results
New in SqlPackage 162.5+: Full support for SQL database in Microsoft Fabric.
Fabric Deployment:
# Deploy to Fabric Warehouse
sqlpackage /Action:Publish \
/SourceFile:Warehouse.dacpac \
/TargetConnectionString:"Server=tcp:myworkspace.datawarehouse.fabric.microsoft.com;Database=mywarehouse;Authentication=ActiveDirectoryInteractive;" \
/p:DatabaseEdition=Fabric \
/p:DatabaseServiceObjective=SqlDbFabricDatabaseSchemaProvider
# Extract from Fabric
sqlpackage /Action:Extract \
/SourceConnectionString:"Server=tcp:myworkspace.datawarehouse.fabric.microsoft.com;Database=mywarehouse;Authentication=ActiveDirectoryInteractive;" \
/TargetFile:Fabric.dacpac
# New permission: ALTER ANY EXTERNAL MIRROR (Fabric-specific)
GRANT ALTER ANY EXTERNAL MIRROR TO [FabricAdmin];
<PropertyGroup>
<TargetPlatform>SqlServer2025</TargetPlatform>
</PropertyGroup>
-- Verify vector support
SELECT SERVERPROPERTY('IsVectorSupported') AS VectorSupport;
-- Track model execution
SELECT
model_name,
AVG(execution_time_ms) AS AvgExecutionTime,
COUNT(*) AS ExecutionCount
FROM sys.dm_exec_external_model_stats
GROUP BY model_name;
-- Classify all PII columns
ADD SENSITIVITY CLASSIFICATION TO dbo.Customers.Email
WITH (LABEL = 'Confidential - GDPR', INFORMATION_TYPE = 'Email');
development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.