| name | roc-curve-plotter |
|---|---|
| description | Manage roc curve plotter operations. Auto-activating skill for ML Training. Triggers on: roc curve plotter, roc curve plotter Part of the ML Training skill category. Use when working with roc curve plotter functionality. Trigger with phrases like "roc curve plotter", "roc plotter", "roc". |
| allowed-tools | Read, Write, Edit, Bash(python:*), Bash(pip:*) |
| version | 1.0.0 |
| license | MIT |
| author | Jeremy Longshore <[email protected]> |
Roc Curve Plotter
Overview
This skill provides automated assistance for roc curve plotter tasks within the ML Training domain.
When to Use
This skill activates automatically when you: - Mention "roc curve plotter" in your request - Ask about roc curve plotter patterns or best practices - Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.
Instructions
- Provides step-by-step guidance for roc curve plotter
- 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 roc curve plotter" 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 training 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 Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn