plugins/ado-master/skills/sprint-254-features/SKILL.md
Azure DevOps Sprint 254-262 new features and enhancements (2025). PROACTIVELY activate for: (1) checking what is new in recent Azure DevOps sprints, (2) using new pipeline tasks introduced in Sprint 254+, (3) Azure Boards updates (delivery plans, query enhancements), (4) Azure Repos updates (branch policies, advanced security), (5) Azure Pipelines updates (1ES hosted pools, agent retirement), (6) Azure Artifacts updates (upstream sources, retention), (7) deprecation notices (TFVC, classic pipelines), (8) GitHub Advanced Security for ADO. Provides: sprint-by-sprint changelog, new feature reference, deprecation timeline, and migration notes.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace sprint-254-featuresInstall 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.
# Syntax: iif(condition, valueIfTrue, valueIfFalse)
variables:
environment: 'production'
# Use iif for conditional values
instanceCount: ${{ iif(eq(variables.environment, 'production'), 10, 2) }}
deploymentSlot: ${{ iif(eq(variables.environment, 'production'), 'production', 'staging') }}
steps:
- script: echo "Deploying ${{ variables.instanceCount }} instances to ${{ variables.deploymentSlot }}"
parameters:
- name: branchName
type: string
default: ' feature/my-branch '
variables:
# Remove leading/trailing whitespace
cleanBranch: ${{ trim(parameters.branchName) }}
# Result: 'feature/my-branch' (no spaces)
Who requested the stage execution:
stages:
- stage: Deploy
jobs:
- job: DeployJob
steps:
- script: |
echo "Stage requested by: $(Build.StageRequestedBy)"
echo "Stage requester ID: $(Build.StageRequestedById)"
displayName: 'Log stage requester'
# Use for approval notifications
- task: SendEmail@1
inputs:
to: '[email protected]'
subject: 'Deployment requested by $(Build.StageRequestedBy)'
View stage dependencies when stage is expanded in pipeline UI:
stages:
- stage: Build
jobs:
- job: BuildJob
steps:
- script: echo "Building..."
- stage: Test
dependsOn: Build # Shown visually when expanded
jobs:
- job: TestJob
steps:
- script: echo "Testing..."
- stage: Deploy_USEast
dependsOn: Test
jobs:
- job: DeployJob
steps:
- script: echo "Deploying to US East..."
- stage: Deploy_EUWest
dependsOn: Test # Parallel with Deploy_USEast - visualized clearly
jobs:
- job: DeployJob
steps:
- script: echo "Deploying to EU West..."
Benefits:
pool:
vmImage: 'ubuntu-24.04' # Latest LTS - Recommended
# OR use ubuntu-latest (will map to 24.04 soon)
# vmImage: 'ubuntu-latest'
steps:
- script: |
lsb_release -a
# Ubuntu 24.04 LTS (Noble Numbat)
Key Information:
ubuntu-latest will soon map to ubuntu-24.04 (currently ubuntu-22.04)pool:
vmImage: 'windows-2025' # GA: June 16, 2025
steps:
- pwsh: |
Get-ComputerInfo | Select-Object WindowsProductName, WindowsVersion
Key Information:
windows-latest will map to windows-2025 starting September 2, 2025pool:
vmImage: 'macOS-15' # Sequoia
steps:
- script: |
sw_vers
# macOS 15.x (Sequoia)
Key Information:
Fully Removed (2025):
Extended Support:
Upcoming Deprecations:
Migration Recommendations:
# Ubuntu Migration
# OLD (Removed)
pool:
vmImage: 'ubuntu-20.04'
# NEW (Recommended)
pool:
vmImage: 'ubuntu-24.04' # Best: explicit version
# OR
vmImage: 'ubuntu-latest' # Will map to 24.04 soon
# Windows Migration
# OLD (Being deprecated)
pool:
vmImage: 'windows-2019'
# NEW (Recommended)
pool:
vmImage: 'windows-2022' # Current stable
# OR wait for
vmImage: 'windows-2025' # GA June 2025
GitHub branches linked to work items automatically link PRs:
# When PR is created for branch linked to work item,
# PR automatically appears in work item's Development section
trigger:
branches:
include:
- feature/*
- users/*
# Work item auto-linking based on branch name pattern
# AB#12345 in commits auto-links to work item 12345
GitHub repos show which build integrated the PR:
pr:
branches:
include:
- main
- develop
# After PR merged, work item shows:
# "Integrated in build: Pipeline Name #123"
# Direct link to build that deployed the change
stages:
- stage: Build
variables:
buildConfiguration: 'Release'
platform: 'x64'
jobs:
- job: BuildJob
steps:
- script: echo "Building $(buildConfiguration) $(platform)"
- stage: Deploy
variables:
environment: 'production'
region: 'eastus'
jobs:
- job: DeployJob
steps:
- script: |
echo "Stage: $(System.StageName)"
echo "Requested by: $(Build.StageRequestedBy)"
echo "Deploying to $(environment) in $(region)"
parameters:
- name: deployToProd
type: boolean
default: false
variables:
# Use iif for conditional values
targetEnvironment: ${{ iif(parameters.deployToProd, 'production', 'staging') }}
stages:
- stage: Build
jobs:
- job: BuildApp
pool:
vmImage: 'ubuntu-24.04' # New image
steps:
- script: npm run build
- stage: Test
dependsOn: Build
jobs:
- job: RunTests
pool:
vmImage: 'ubuntu-24.04'
steps:
- script: npm test
- stage: Deploy_USEast
dependsOn: Test
condition: succeeded()
variables:
region: 'eastus'
jobs:
- deployment: DeployToUSEast
environment: ${{ variables.targetEnvironment }}
pool:
vmImage: 'ubuntu-24.04'
strategy:
runOnce:
deploy:
steps:
- script: |
echo "Deploying to $(region)"
echo "Requested by: $(Build.StageRequestedBy)"
- stage: Deploy_EUWest
dependsOn: Test # Parallel with Deploy_USEast
condition: succeeded()
variables:
region: 'westeurope'
jobs:
- deployment: DeployToEUWest
environment: ${{ variables.targetEnvironment }}
pool:
vmImage: 'ubuntu-24.04'
strategy:
runOnce:
deploy:
steps:
- script: |
echo "Deploying to $(region)"
echo "Requested by: $(Build.StageRequestedBy)"
# Stage dependencies visualized clearly in UI (Sprint 254)
Azure DevOps now supports Continuous Access Evaluation (CAE), enabling near real-time enforcement of Conditional Access policies through Microsoft Entra ID.
Key Benefits:
Triggers for Access Revocation:
Example Scenario:
# Your pipeline with CAE enabled automatically
stages:
- stage: Production
jobs:
- deployment: Deploy
environment: 'production'
pool:
vmImage: 'ubuntu-24.04'
strategy:
runOnce:
deploy:
steps:
- script: echo "Deploying..."
# If user credentials are revoked mid-deployment,
# CAE will instantly terminate access
Implementation:
Security Improvements:
Important Change:
Migration Recommendations:
# Use service connections with Microsoft Entra ID instead
- task: AzureCLI@2
inputs:
azureSubscription: 'service-connection' # Uses Managed Identity or Service Principal
scriptType: 'bash'
scriptLocation: 'inlineScript'
addSpnToEnvironment: true
inlineScript: |
az account show
Network Requirement:
What to Check:
Critical Security Change:
Azure DevOps is enforcing one-time visibility for OAuth client secrets:
Migration Path:
# Replace OAuth apps with Microsoft Entra ID authentication
# Use service connections with Managed Identity or Service Principal
- task: AzureCLI@2
inputs:
azureSubscription: 'entra-id-service-connection'
scriptType: 'bash'
addSpnToEnvironment: true
inlineScript: |
az account show
# Authenticated via Entra ID
Action Required:
Major Upgrade:
The Azure Pipelines agent has been upgraded from v3.x to v4.x, powered by .NET 8:
Key Improvements:
Platform Support:
ARM64 Support:
# Self-hosted ARM64 agent
pool:
name: 'arm64-pool'
demands:
- agent.os -equals Linux
- Agent.OSArchitecture -equals ARM64
steps:
- script: uname -m
displayName: 'Verify ARM64 architecture'
Note: ARM64 support is available for self-hosted agents. Microsoft-hosted ARM64 macOS agents are in preview.
AI-Powered Work Item Assistance (Private Preview):
Connect Azure Boards work items directly with GitHub Copilot:
Capabilities:
Usage Pattern:
Integration with Pipelines:
# Work items auto-link with PRs
trigger:
branches:
include:
- feature/*
# Mention work item in commit
# Example: "Fix login bug AB#12345"
# Automatically links PR to work item and tracks in build
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.