From jeremylongshore-claude-code-plugins-plus-skills
Guides streaming inference setup for ML deployment, covering model serving, MLOps pipeline, and production optimization.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:streaming-inference-setupThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for streaming inference setup tasks within the ML Deployment domain.
This skill provides automated assistance for streaming inference setup tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with streaming inference setup" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Deployment skill category. Tags: mlops, serving, inference, monitoring, production
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skillsProvides step-by-step guidance and automated configuration for TensorFlow Serving setup in ML deployment workflows.
Deploys ML models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints. Implements autoscaling, monitoring, and A/B testing for real-time inference.
Provides end-to-end MLOps guidance on AWS: platform selection, training, inference, pipelines, monitoring, and cost optimization. Activates on queries about SageMaker, MLflow, Kubeflow, model deployment, or MLOps setup.