smithery/jeremylongshore

learning-rate-scheduler

Manage learning rate scheduler operations. Auto-activating skill for ML Training. Triggers on: learning rate scheduler, learning rate scheduler Part of the ML Training skill category. Use when working with learning rate scheduler functionality. Trigger with phrases like "learning rate scheduler", "learning scheduler", "learning". '

Installation

$ npx skills add smithery/jeremylongshore --skill learning-rate-scheduler

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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 2,306 B
  • docs SUMMARY.md 364 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Learning Rate Scheduler

Overview

This skill provides automated assistance for learning rate scheduler tasks within the ML Training domain.

When to Use

This skill activates automatically when you:

  • Mention "learning rate scheduler" in your request
  • Ask about learning rate scheduler 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 learning rate scheduler
  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 learning rate scheduler" 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