ml-model-training
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
95
93%
Does it follow best practices?
Impact
100%
1.07xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
100%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 is a strong skill description that effectively communicates both capabilities and usage triggers. It uses third person voice, lists specific ML frameworks and tasks, and includes natural terminology that practitioners would use. The explicit 'Use for...' clause with concrete scenarios makes skill selection straightforward.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Train ML models', 'classification/regression', 'neural networks', 'hyperparameter tuning' and specific problem types like 'overfitting, underfitting, convergence issues'. | 3 / 3 |
Completeness | Clearly answers both what ('Train ML models with scikit-learn, PyTorch, TensorFlow') and when ('Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'ML models', 'scikit-learn', 'PyTorch', 'TensorFlow', 'classification', 'regression', 'neural networks', 'hyperparameter tuning', 'overfitting', 'underfitting' - these are terms practitioners naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Clear niche focused on ML model training with specific frameworks and problem types; distinct triggers like 'scikit-learn', 'PyTorch', 'TensorFlow', 'overfitting', 'convergence' make it unlikely to conflict with general coding or data analysis skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong skill with excellent actionability and progressive disclosure. The code examples are executable and the Known Issues Prevention section adds significant value with concrete problem/solution pairs. The main weakness is the lack of explicit validation checkpoints in the workflow - there's no guidance on verifying data quality after preparation or model quality thresholds before deployment.
Suggestions
Add validation checkpoints to the workflow, e.g., 'After Data Preparation: verify no NaN values remain, check class distribution, confirm train/val/test sizes'
Include a model quality gate before considering training complete, such as 'If val_accuracy < 0.7 or val_loss increasing for 5 epochs: review data quality and hyperparameters before proceeding'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient, providing executable code without explaining basic concepts Claude already knows. Every section delivers actionable information without padding. | 3 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples for data preparation, scikit-learn, and PyTorch training. Includes concrete solutions for common problems with correct/incorrect code comparisons. | 3 / 3 |
Workflow Clarity | The workflow is listed (Data Preparation → Feature Engineering → Model Selection → Training → Evaluation) but lacks explicit validation checkpoints between steps. No feedback loops for catching training failures or model quality issues before proceeding. | 2 / 3 |
Progressive Disclosure | Excellent structure with a concise overview, clear sections, and well-signaled one-level-deep references to PyTorch and TensorFlow detailed guides. The 'When to Load References' section provides clear navigation guidance. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- secondsky/claude-skills
- Commit
90d6bd7
- Reviewed
Table of Contents
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