smithery/jeremylongshore

model-evaluation-metrics

Build model evaluation metrics operations. Auto-activating skill for ML Training. Triggers on: model evaluation metrics, model evaluation metrics Part of the ML Training skill category. Use when working with model evaluation metrics functionality. Trigger with phrases like "model evaluation metrics", "model metrics", "model". '

Installation

$ npx skills add smithery/jeremylongshore --skill model-evaluation-metrics

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from smithery/jeremylongshore · top by installs.

npx skills add smithery/jeremylongshore

Browse all from smithery/jeremylongshore

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
LicenseMIT
CompatibilityDesigned for Claude Code
Allowed toolsRead, Write, Edit, Bash(python:*), Bash(pip:*)
Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,309 B
  • docs SUMMARY.md 361 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Model Evaluation Metrics

Overview

This skill provides automated assistance for model evaluation metrics tasks within the ML Training domain.

When to Use

This skill activates automatically when you:

  • Mention "model evaluation metrics" in your request
  • Ask about model evaluation metrics patterns or best practices
  • Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

Instructions

  1. Provides step-by-step guidance for model evaluation metrics
  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 model evaluation metrics" 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