From jeremylongshore-claude-code-plugins-plus-skills
Guides model quantization tasks for ML deployment, including configuration generation and best practices for serving and inference optimization.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:model-quantization-toolThis 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 model quantization tool tasks within the ML Deployment domain.
This skill provides automated assistance for model quantization tool tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with model quantization tool" 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-skills --plugin j-rigReduces model size, improves inference speed, and prepares models for edge deployment using quantization, pruning, knowledge distillation, ONNX export, and TensorRT optimization.
Exports TensorFlow models to SavedModel and TensorFlow Lite with quantization, serving signatures for production and edge deployment.
Assists with model export helper operations for ML deployment, including model serving, mlops pipelines, monitoring, and production optimization.