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
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 essentially a restated title with no substantive content. It fails to describe any concrete capabilities, lacks natural trigger terms users would use, and provides no explicit guidance on when Claude should select this skill. It would be nearly useless for skill selection among multiple ML-related skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Builds and trains PyTorch neural network models, configures optimizers and loss functions, manages training loops, evaluates model performance, and saves/loads model checkpoints.'
Add an explicit 'Use when...' clause with natural trigger terms, e.g., 'Use when the user asks about training a PyTorch model, writing training loops, tuning hyperparameters, working with .pt/.pth files, or building neural networks with PyTorch.'
Remove the duplicated trigger term ('pytorch model trainer' listed twice) and replace with diverse natural language variations users might actually say, such as 'deep learning training', 'neural network', 'train a model', 'PyTorch', 'GPU training', 'fine-tuning'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain ('ML Training', 'Pytorch') but lists no concrete actions. There are no specific capabilities like 'train models', 'tune hyperparameters', 'evaluate loss curves', etc. It is essentially a title repeated with no actionable detail. | 1 / 3 |
Completeness | The 'what' is extremely vague (just 'ML Training') and there is no explicit 'when' clause. The 'Triggers on' line merely repeats the skill name rather than describing meaningful trigger conditions. Both what and when are very weak. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'pytorch model trainer' repeated twice. It misses natural user phrases like 'train a model', 'PyTorch training loop', 'deep learning', 'neural network', 'GPU training', 'fine-tune', 'epochs', '.pt files', etc. | 1 / 3 |
Distinctiveness Conflict Risk | Mentioning 'Pytorch' and 'Model Trainer' does narrow the domain somewhat compared to a fully generic description, but the lack of specific actions or file types means it could still overlap with other ML-related skills (e.g., TensorFlow training, model evaluation, data preprocessing). | 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 is essentially a placeholder template with no actual instructional content. It repeatedly describes itself in vague terms ('provides automated assistance for pytorch model trainer tasks') without ever delivering any concrete PyTorch training guidance, code examples, or workflows. It fails on every dimension of the rubric.
Suggestions
Add concrete, executable PyTorch code examples: a minimal training loop, data loading with DataLoader, loss computation, and optimizer steps.
Define a clear multi-step workflow for model training (e.g., 1. Prepare dataset → 2. Define model → 3. Configure training → 4. Train with validation checkpoints → 5. Evaluate and save).
Remove all meta-description sections (When to Use, Example Triggers, Capabilities) and replace with actionable technical content covering data preparation, hyperparameter tuning, and experiment tracking.
Add references to advanced topics in separate files (e.g., HYPERPARAMETER_TUNING.md, DISTRIBUTED_TRAINING.md) rather than listing them as vague bullet points.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is entirely filler and meta-description. It explains what the skill does in abstract terms without providing any actual technical content. Every section restates the same vague information about 'pytorch model trainer' without adding substance. | 1 / 3 |
Actionability | There is zero concrete guidance—no code examples, no commands, no specific PyTorch patterns, no training loop examples, no hyperparameter tuning strategies. The skill describes rather than instructs, offering nothing executable or copy-paste ready. | 1 / 3 |
Workflow Clarity | No workflow is defined at all. There are no steps for data preparation, model training, evaluation, or any multi-step process. The 'step-by-step guidance' mentioned in Capabilities is never actually provided. | 1 / 3 |
Progressive Disclosure | The content is a flat, repetitive document with no meaningful structure. There are no references to detailed guides, no links to examples or advanced topics, and the sections are all meta-descriptions rather than organized technical content. | 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
3076d78
- Reviewed
Table of Contents
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