| name | onnx-converter |
| description | Convert onnx converter operations. Auto-activating skill for ML Deployment.
Triggers on: onnx converter, onnx converter
Part of the ML Deployment skill category. Use when working with onnx converter functionality. Trigger with phrases like "onnx converter", "onnx converter", "onnx".
|
| allowed-tools | Read, Write, Edit, Bash(cmd:*), Grep |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["ai","mlops"] |
| compatibility | Designed for Claude Code |
Onnx Converter
Overview
This skill provides automated assistance for onnx converter tasks within the ML Deployment domain.
When to Use
This skill activates automatically when you:
- Mention "onnx converter" in your request
- Ask about onnx converter 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 onnx converter
- 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 onnx converter"
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
