---
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>"
---

# 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

1. Provides step-by-step guidance for onnx converter
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. 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