From jeremylongshore-claude-code-plugins-plus-skills
Optimizes GPU resources for ML deployment tasks like model serving, MLOps pipelines, monitoring, and production inference. Generates code, configs, and best practices guidance. Auto-activates on 'gpu resource optimizer' or 'gpu optimizer' phrases.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin framecraftThis skill is limited to using the following tools:
This skill provides automated assistance for gpu resource optimizer tasks within the ML Deployment domain.
Optimizes ML inference latency via model compression, distillation, pruning, quantization, caching strategies, and edge deployment patterns.
Generates TorchServe configuration files and operations for ML model serving in production. Provides step-by-step guidance, best practices, code, and validation for MLOps pipelines, inference, and monitoring.
Provides Vast.ai reference architecture for GPU compute workflows in ML training: three-tier orchestrator-workers-storage, Python job queues, Docker workers, and YAML configs.
Share bugs, ideas, or general feedback.
This skill provides automated assistance for gpu resource optimizer tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with gpu resource optimizer" 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