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.
36
Quality
3%
Does it follow best practices?
Impact
97%
1.03xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/pytorch-model-trainer/SKILL.mdQuality
Discovery
7%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description is severely underdeveloped, functioning more as a label than a useful skill description. It provides no concrete actions, uses redundant trigger terms, and fails to explain what the skill actually does or when it should be used. Claude would struggle to appropriately select this skill from a library of ML-related skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Creates training loops, configures optimizers, implements data loaders, saves/loads model checkpoints, monitors training metrics'
Add a 'Use when...' clause with natural trigger terms like 'train a neural network', 'PyTorch training', 'deep learning model', 'training loop', 'model checkpoints', '.pt files'
Remove the redundant trigger term and replace with varied natural language users would actually say when needing ML training help
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the domain ('ML Training', 'Pytorch Model Trainer') but provides no concrete actions. It doesn't describe what the skill actually does - no mention of training loops, data loading, model architecture, hyperparameter tuning, or any specific capabilities. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the vague 'ML Training' category, and the 'when' clause is just a redundant trigger phrase rather than explicit guidance on when Claude should select this skill. | 1 / 3 |
Trigger Term Quality | The triggers listed are just 'pytorch model trainer' repeated twice. Missing natural user terms like 'train a model', 'neural network', 'deep learning', 'training loop', 'epochs', 'loss function', '.pt files', etc. | 1 / 3 |
Distinctiveness Conflict Risk | While 'pytorch' is somewhat specific and distinguishes from TensorFlow or other frameworks, 'ML Training' is broad and could overlap with other machine learning skills. The lack of specific capabilities makes it harder to distinguish from general ML skills. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill content is essentially a placeholder with no actual instructional value. It describes what a PyTorch model trainer skill should do without providing any concrete guidance, code examples, or workflows. The content fails on all dimensions by being verbose yet empty of actionable information.
Suggestions
Add executable PyTorch code examples showing a complete training loop (DataLoader setup, model definition, optimizer, training/validation steps)
Include a clear workflow with validation checkpoints: data loading -> model setup -> training -> checkpoint saving -> evaluation
Add specific guidance on hyperparameter tuning, experiment tracking (e.g., with wandb/tensorboard), and common pitfalls
Reference separate files for advanced topics like distributed training, mixed precision, and custom loss functions
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that explains nothing Claude doesn't already know. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler with no actual information. | 1 / 3 |
Actionability | There is zero concrete guidance - no code examples, no commands, no specific steps for PyTorch model training. The content only describes what the skill claims to do without actually providing any executable instructions. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. For a model training skill, there should be clear steps for data preparation, model definition, training loops, validation, and checkpointing - none of which are present. | 1 / 3 |
Progressive Disclosure | The content is a flat, uninformative document with no structure pointing to detailed materials. There are no references to examples, API documentation, or advanced topics that would be essential for a PyTorch training skill. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
- Commit
f17dd51
- Reviewed
Table of Contents
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