| name | model-pruning-helper |
| description | Assist with model pruning helper operations. Auto-activating skill for ML Deployment.
Triggers on: model pruning helper, model pruning helper
Part of the ML Deployment skill category. Use when working with model pruning helper functionality. Trigger with phrases like "model pruning helper", "model helper", "model".
|
| allowed-tools | Read, Write, Edit, Bash(cmd:*), Grep |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
Model Pruning Helper
Overview
This skill provides automated assistance for model pruning helper tasks within the ML Deployment domain.
When to Use
This skill activates automatically when you:
- Mention "model pruning helper" in your request
- Ask about model pruning helper patterns or best practices
- Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.
Instructions
- Provides step-by-step guidance for model pruning helper
- Follows industry best practices and patterns
- Generates production-ready code and configurations
- Validates outputs against common standards
Examples
Example: Basic Usage
Request: "Help me with model pruning helper"
Result: Provides step-by-step guidance and generates appropriate configurations
Prerequisites
- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml deployment concepts
Output
- Generated configurations and code
- Best practice recommendations
- Validation results
Error Handling
| 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 |
Resources
- Official documentation for related tools
- Best practices guides
- Community examples and tutorials
Related Skills
Part of the ML Deployment skill category.
Tags: mlops, serving, inference, monitoring, production
