pytorch-model-trainer

Pytorch Model Trainer - Auto-activating skill for ML Training. Triggers on: pytorch model trainer, pytorch model trainer Part of the ML Training skill category.

1.03x

Quality

Does it follow best practices?

Impact

97%

1.03x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/pytorch-model-trainer/SKILL.md

Evaluation results

100%

Customer Churn Prediction: Data Pipeline Setup

PyTorch data preparation pipeline

Criteria

Without context

With context

Custom Dataset class

100%

Dataset __len__ and __getitem__

100%

DataLoader usage

100%

Shuffle on training split

100%

Train/validation split

100%

Batch size configured

100%

Feature normalization or standardization

100%

Reproducibility seed

100%

Smoke test output

100%

Production-ready structure

100%

Without context: $0.4205 · 3m 30s · 24 turns · 25 in / 6,260 out tokens

With context: $0.5570 · 3m 59s · 32 turns · 30 in / 7,515 out tokens

92%

Sentiment Classifier: Neural Network Training Script

PyTorch training loop with metrics tracking

Criteria

Without context

With context

nn.Module subclass

100%

forward() method

100%

Appropriate loss function

100%

Optimizer usage

100%

optimizer.zero_grad() called

100%

Device handling

Train/eval mode switching

100%

Per-epoch metric logging

100%

Model checkpoint saved

100%

torch.no_grad() in validation

100%

Without context: $0.5092 · 4m 48s · 19 turns · 20 in / 8,397 out tokens

With context: $0.7501 · 4m 51s · 29 turns · 63 in / 10,302 out tokens

100%

Energy Consumption Forecaster: Finding the Best Training Configuration

Hyperparameter tuning and experiment tracking

Criteria

Without context

With context

Multiple hyperparameter configs

100%

Systematic config iteration

100%

Per-epoch loss tracking

100%

Results persisted to JSON

100%

Best config identified

100%

Learning rate varied

100%

Batch size varied

100%

Reproducibility seed per config

87%

100%

Validation split

100%

Experiment summary produced

100%

Without context: $0.5222 · 2m 26s · 25 turns · 25 in / 8,078 out tokens

With context: $0.8333 · 2s · 1 turns · 3 in / 31 out tokens

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: f17dd51

Evaluated: 18 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Customer Churn Prediction: Data Pipeline Setup Sentiment Classifier: Neural Network Training Script Energy Consumption Forecaster: Finding the Best Training Configuration

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