when-debugging-ml-training-use-ml-training-debugger
Debug ML training issues and optimize performance including loss divergence, overfitting, and slow convergence
npx skills add majiayu000/claude-skill-registry --skill when-debugging-ml-training-use-ml-training-debugger-dnyoussef-ai --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML Training Debugger - Diagnose and Fix Training Issues ## Overview Systematic debugging workflow for ML training issues including loss divergence, overfitting, slow convergence, gradient problems, and performance optimization. ## When to Use - Training loss becomes NaN or infinite - Severe overfitting (train >> val performance) - Training not converging - Gradient vanishing/exploding - Poor validation accuracy - Training too slow ## Phase 1: Diagnose Issue (8 min) ### Objective Identify the specific training problem ### Agent: ML-Developer **Step 1.1: Analyze Training Curves** ```python import json import numpy as np # Load training history with open('training_history.json', 'r') as f: history = json.load(f) # Diagnose issues diagnosis = { 'loss_divergence': check_loss_divergence(history['loss']), 'overfitting': check_overfitting(history['loss'], history['val_loss']), 'slow_convergence': check_convergence_rate(history['loss']), 'gradient_issues': check_gradient_health(history), 'nan_values': any(np.isnan(history['loss'])) } def check_loss_divergence(losses): # Loss increasing over time if len(losses) > 10: recent_trend = np.mean(losses[-5:]) > np.mean(losses[-10:-5]) return rece
- Overview
- When to Use
- Phase 1: Diagnose Issue (8 min)
- Objective
- Agent: ML-Developer
- Validation Criteria
- Phase 2: Analyze Root Cause (10 min)
- Agent: Performance-Analyzer
- Phase 3: Apply Fix (15 min)
- Agent: Coder
- Phase 4: Validate Fix (12 min)
- Phase 5: Optimize Performance (5 min)
- Success Metrics
- Skill Completion
What does the when-debugging-ml-training-use-ml-training-debugger skill do?
Debug ML training issues and optimize performance including loss divergence, overfitting, and slow convergence
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill when-debugging-ml-training-use-ml-training-debugger-dnyoussef-ai --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.