plugins/dotnet-microservices-master/skills/microsoft-guide/SKILL.md
Microsoft .NET Microservices Architecture Guide reference. PROACTIVELY activate for: (1) reading or applying patterns from Microsoft .NET Microservices Architecture for Containerized .NET Applications, (2) eShopOnContainers reference architecture, (3) DDD and bounded contexts, (4) integration events (RabbitMQ, Azure Service Bus), (5) API gateway patterns (Ocelot, YARP), (6) resilience patterns (Polly), (7) data patterns (CQRS, event sourcing), (8) testing strategies in microservices, (9) deployment to Kubernetes/AKS/Container Apps. Provides: chapter-by-chapter summary, code patterns from eShopOnContainers, decision matrices for sync vs async integration, and links into specific guide sections.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace microsoft-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides access to the complete official Microsoft guide: ".NET Microservices Architecture for Containerized .NET Applications" (Edition v7.0 - Updated to ASP.NET Core 7.0).
Invoke this skill when you need:
The complete 350-page Microsoft guide (text content only) including:
When a user asks about:
Invoke this skill and follow the procedure below.
| User intent | Guide area to inspect | |---|---| | Define service boundaries | DDD, bounded contexts, data ownership | | Choose HTTP vs messaging | Communication in microservices, integration events | | Add API gateway | API Gateway patterns, Ocelot/YARP considerations | | Improve resilience | Retry, circuit breaker, timeout, health checks, Polly | | Split databases | Data sovereignty, eventual consistency, CQRS | | Deploy containers | Docker, Kubernetes, orchestrators, environment configuration |
The complete guide is available in this skill directory at:
NET-Microservices-Architecture.md
When you invoke this skill, you have access to read this file which contains the full 350-page Microsoft guide with all technical details, code examples, architecture patterns, and implementation guidance.
When this skill is invoked:
Note: The guide contains comprehensive text explanations of all concepts, patterns, and implementations. Image references have been removed to optimize plugin size (reduced from 18MB to ~800KB).
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.