plugins/azure-to-docker-master/skills/docker-watch-mode-2025/SKILL.md
Docker Compose Watch mode for hot reload during local development (2025 GA). PROACTIVELY activate for: (1) docker compose watch setup, (2) watch action types (sync, rebuild, sync+restart, sync+exec), (3) configuring path filters and ignore patterns, (4) hot-reload for Node/Python/Go/Rust apps in Compose, (5) sync-only vs rebuild trade-offs, (6) using watch with profiles, (7) debugging watch mode (verbose logs), (8) integrating watch with bind mounts, (9) Compose watch vs nodemon/air/cargo-watch. Provides: watch action reference, per-language hot-reload examples, path filter patterns, and troubleshooting steps.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace docker-watch-mode-2025Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Docker Compose Watch enables automatic hot reload during local development by synchronizing file changes instantly without manual container restarts.
For frameworks with hot reload (React, Next.js, Node.js, Flask). Copies changed files directly into running container.
For compiled languages (Go, Rust, Java) or dependency changes. Rebuilds image and recreates container when files change.
For configuration files requiring restart. Syncs files and restarts container.
docker compose up before adding watch rules.sync for source directories when the runtime already reloads files.rebuild for dependency manifests, Dockerfiles, compiled artifacts, or base-image changes.sync+restart for configuration files that are read only at process startup.ignore rules for dependency folders, build outputs, VCS folders, caches, secrets, and generated files.docker compose up --watch and verify both fast source edits and dependency changes.services:
frontend:
build: ./frontend
develop:
watch:
- action: sync
path: ./frontend/src
target: /app/src
ignore: [node_modules/, .git/, dist/]
- action: rebuild
path: ./frontend/package.json
Start with: docker compose up --watch.
ignore.sync from manifest rebuild rules.Use docker compose watch --help and Docker's Compose Watch documentation for the current action set and CLI flags. Pair this skill with compose-patterns-2025 when watch rules interact with service dependencies, profiles, networks, or volumes.
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.