From jeremylongshore-claude-code-plugins-plus-skills
Manages learning rate scheduler operations for ML training. Provides step-by-step guidance, code, and configurations for PyTorch, TensorFlow, and scikit-learn.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:learning-rate-schedulerThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for learning rate scheduler tasks within the ML Training domain.
This skill provides automated assistance for learning rate scheduler tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with learning rate scheduler" Result: Provides step-by-step guidance and generates appropriate configurations
| 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 |
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin ejentum-reasoningOptimizes deep learning models by tuning optimizers (Adam, SGD), learning rate schedules, and regularization to improve accuracy and reduce training time.
Guides hyperparameter tuning operations for ML training, including configuration generation and best practices.
Organizes PyTorch code into LightningModules, configures Trainers for multi-GPU/TPU, builds data pipelines and callbacks, and runs distributed training (DDP, FSDP, DeepSpeed). Use when structuring training loops or scaling neural-network training.