plugins/modal-web-scheduling/skills/modal-web-scheduling-knowledge/SKILL.md
This skill should be used when the user asks to expose Modal.com functions as APIs or schedule Modal jobs. PROACTIVELY activate for: FastAPI endpoints, @modal.fastapi_endpoint, @modal.asgi_app, @modal.wsgi_app, webhooks, WebSockets, custom domains, endpoint timeouts, modal.Cron, modal.Period, scheduled ETL, cron timezones, deploy-vs-run schedule behavior, and endpoint debugging. Provides: endpoint decorator selection guide, scheduling patterns, timezone/cron rules, and debugging recipes for endpoints and schedules.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace modal-web-scheduling-knowledgeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill for Modal web endpoints and scheduled jobs. For GPU compute, autoscaling, or storage primitives, use the focused Modal compute or storage skills.
@modal.fastapi_endpoint() for simple function-backed HTTP APIs.@modal.asgi_app() for full FastAPI/Starlette apps, WebSockets, middleware, and routing.@modal.wsgi_app() for WSGI frameworks.@modal.concurrent for high-throughput endpoints that can safely handle concurrent inputs per container.modal serve, then deploy with modal deploy.modal.Cron(...) for calendar schedules and explicit timezones.modal.Period(...) for interval-based execution.modal run is only for manual testing.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.