plugins/ado-master/skills/ado-pipeline-best-practices/SKILL.md
Azure DevOps pipeline best practices, patterns, and industry standards. PROACTIVELY activate for: (1) authoring or reviewing Azure DevOps YAML pipelines, (2) multi-stage pipeline patterns (build, test, deploy), (3) reusable templates (steps, jobs, stages), (4) pipeline caching (Cache@2 task), (5) parallel jobs and matrix strategies, (6) deployment strategies (rolling, blue-green, canary), (7) approvals and environments, (8) variable groups, secret variables, Key Vault linkage, (9) service connections (Azure RM, GitHub, container registries), (10) pipeline versioning and pinning task major versions. Provides: YAML pattern catalog, template library structure, caching recipes, deployment strategy templates, and a pipeline-quality checklist.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace ado-pipeline-best-practicesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Comprehensive best practices for creating and maintaining Azure DevOps YAML pipelines.
Multi-Stage Pipelines:
# Recommended structure
stages:
- stage: Build
- stage: Test
- stage: DeployDev
- stage: DeployStaging
- stage: DeployProduction
Benefits:
Best practices:
trigger:
batch: true
branches:
include: [main, develop]
paths:
exclude: ['docs/*', '**.md']
pr:
autoCancel: true
branches:
include: [main]
schedules:
- cron: '0 0 * * *'
displayName: 'Nightly build'
branches:
include: [main]
always: false # Only if code changed
Hierarchy:
Security:
Implement caching for:
Impact:
Use templates for:
Benefits:
Essential:
Optimize:
Track:
Always verify best practices against latest Azure DevOps documentation.
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.