engineering-features-for-machine-learning
Execute create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features". Trigger with relevant phrases based on skill purpose.
36
33%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
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npx tessl skill review --optimize ./plugins/ai-ml/feature-engineering-toolkit/skills/engineering-features-for-machine-learning/SKILL.mdQuality
Discovery
67%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description covers the basics of what the skill does and when to use it, earning reasonable completeness marks. However, the final sentence ('Trigger with relevant phrases based on skill purpose') is meaningless filler that wastes space and adds no discriminative value. The trigger terms are adequate but could be expanded with more natural user phrases and common ML terminology variations.
Suggestions
Remove the vague filler sentence 'Trigger with relevant phrases based on skill purpose' and replace it with additional concrete trigger terms like 'feature extraction', 'one-hot encoding', 'normalization', 'PCA', 'dimensionality reduction'.
Add more specific concrete actions such as 'generate polynomial features', 'apply one-hot or label encoding', 'compute SHAP or permutation importance scores' to increase specificity.
Expand the 'Use when' clause with more natural user phrases like 'when asked to prepare features for a model', 'improve model inputs', or 'reduce feature dimensionality'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (feature engineering/ML) and lists some actions (create, select, transform, scaling, encoding, importance analysis), but the actions are somewhat generic within the ML domain and not deeply specific about what concrete operations are performed. | 2 / 3 |
Completeness | Clearly answers both 'what' (create, select, transform features; handles scaling, encoding, importance analysis) and 'when' (when asked to 'engineer features' or 'select features'), with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes some natural keywords like 'engineer features', 'select features', 'feature scaling', 'encoding', and 'importance analysis', but the final sentence 'Trigger with relevant phrases based on skill purpose' is vague filler that adds no value. Missing common variations like 'feature extraction', 'one-hot encoding', 'normalization', 'feature importance', 'dimensionality reduction'. | 2 / 3 |
Distinctiveness Conflict Risk | Reasonably specific to feature engineering within ML, but terms like 'scaling', 'encoding', and 'transform' could overlap with general data preprocessing or data pipeline skills. The niche is identifiable but not sharply delineated. | 2 / 3 |
Total | 9 / 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 almost entirely generic boilerplate with no actionable content. It describes what feature engineering is and what the skill would theoretically do, but provides zero executable code, no concrete API usage for the referenced 'feature-engineering-toolkit plugin', and no specific guidance that Claude couldn't already infer. Multiple sections ('Prerequisites', 'Instructions', 'Output', 'Error Handling', 'Resources') contain placeholder text that adds no value.
Suggestions
Replace the abstract examples with actual executable Python code showing specific feature engineering operations (e.g., using sklearn's PolynomialFeatures, SelectKBest, StandardScaler) with concrete input/output examples.
Remove all generic boilerplate sections (Prerequisites, Instructions, Output, Error Handling, Resources) that contain only placeholder text and provide no skill-specific information.
Provide concrete API calls or commands for the 'feature-engineering-toolkit plugin' — currently it's referenced but never shown how to actually use it.
Add validation checkpoints to the workflow, such as checking feature distributions after scaling, verifying encoded feature shapes, or validating that selected features improve cross-validation scores.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose with extensive explanation of concepts Claude already knows. Sections like 'How It Works', 'When to Use This Skill', 'Integration', 'Prerequisites', 'Instructions', 'Output', 'Error Handling', and 'Resources' are all filler that provide no actionable information. The content explains what feature engineering is and how Claude should think, which is unnecessary. | 1 / 3 |
Actionability | No executable code, no concrete commands, no specific library calls. The examples describe what the skill 'will do' in abstract terms rather than providing actual Python code. References to a 'feature-engineering-toolkit plugin' with no concrete usage instructions, API calls, or code snippets make this entirely non-actionable. | 1 / 3 |
Workflow Clarity | The 'How It Works' section lists abstract meta-steps (analyzing, generating, executing, providing insights) that describe Claude's thought process rather than concrete workflow steps. No validation checkpoints, no error recovery loops, and no specific sequencing of actual operations. The 'Instructions' section is completely generic boilerplate. | 1 / 3 |
Progressive Disclosure | Monolithic wall of text with no references to external files and no bundle files provided. Content is poorly organized with many sections that contain only generic placeholder text ('The skill produces structured output relevant to the task'). No meaningful structure that aids navigation or discovery. | 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 | |
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