home / skills / jeremylongshore / claude-code-plugins-plus-skills / gradient-clipping-helper
This skill provides automated guidance for gradient clipping helper tasks in ML training, generating configurations, code, and best-practice recommendations.
npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill gradient-clipping-helperReview the files below or copy the command above to add this skill to your agents.
---
name: "gradient-clipping-helper"
description: |
Configure with gradient clipping helper operations. Auto-activating skill for ML Training.
Triggers on: gradient clipping helper, gradient clipping helper
Part of the ML Training skill category. Use when working with gradient clipping helper functionality. Trigger with phrases like "gradient clipping helper", "gradient helper", "gradient".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---
# Gradient Clipping Helper
## Overview
This skill provides automated assistance for gradient clipping helper tasks within the ML Training domain.
## When to Use
This skill activates automatically when you:
- Mention "gradient clipping helper" in your request
- Ask about gradient clipping helper 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 gradient clipping helper
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 gradient clipping helper"
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
This skill automates configuration and guidance for gradient clipping during machine learning training. It helps set sensible clipping strategies, generates code snippets for common frameworks, and validates configurations against common pitfalls. Use it to ensure stable training and prevent exploding gradients in production and experiments.
The skill inspects your training setup and suggests gradient clipping options such as global norm clipping, per-parameter clipping, and adaptive schemes. It generates framework-specific code (PyTorch, TensorFlow) and config fragments, checks for missing prerequisites, and flags incompatible hyperparameter combinations. It also offers step-by-step instructions for integrating clipping into training loops and validating effectiveness.
Will this skill change my optimizer or learning rate?
It only modifies how gradients are clipped or wrapped; it does not directly change optimizer internals or learning-rate schedules unless you request suggested adjustments.
How do I choose an initial clipping threshold?
Run a short warmup training to collect gradient norm statistics, then pick a threshold slightly above typical peak norms (e.g., 1.5–3× the median) and tune from there.