feature-engineering-helper
Feature Engineering Helper - Auto-activating skill for ML Training. Triggers on: feature engineering helper, feature engineering helper Part of the ML Training skill category.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill feature-engineering-helperOverall
score
19%
Does it follow best practices?
Validation for skill structure
Activation
7%This description is severely underdeveloped, consisting primarily of the skill name and category without any substantive content. It fails to describe what the skill actually does, provides no natural trigger terms users would say, and lacks explicit guidance on when to use it. This would be nearly impossible for Claude to correctly select from a pool of skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Creates derived features from raw data, handles categorical encoding, performs feature scaling and normalization, generates interaction terms'
Add a 'Use when...' clause with natural trigger terms like 'Use when the user mentions creating features, transforming variables, encoding categories, feature selection, or preparing data for ML models'
Replace the redundant trigger list with varied natural language terms users would actually say: 'feature creation', 'variable transformation', 'one-hot encoding', 'feature scaling', 'data preprocessing for ML'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description contains no concrete actions - only the name 'Feature Engineering Helper' and category 'ML Training'. There are no specific capabilities listed like 'creates features', 'transforms variables', or 'handles encoding'. | 1 / 3 |
Completeness | The description fails to answer 'what does this do' beyond the name, and has no 'Use when...' clause or equivalent guidance for when Claude should select this skill. Both what and when are essentially missing. | 1 / 3 |
Trigger Term Quality | The only trigger terms listed are 'feature engineering helper' repeated twice, which is the skill name itself rather than natural user language. Missing common terms like 'create features', 'transform data', 'encode variables', 'feature selection', etc. | 1 / 3 |
Distinctiveness Conflict Risk | While 'feature engineering' is a specific ML domain, the lack of detail means it could overlap with other ML-related skills. The category mention 'ML Training' provides some context but insufficient differentiation. | 2 / 3 |
Total | 5 / 12 Passed |
Implementation
0%This skill is an empty template that provides no actual value. It consists entirely of meta-descriptions about what the skill claims to do without any concrete feature engineering content, code examples, or actionable guidance. The skill would need to be completely rewritten with actual feature engineering techniques, code examples, and specific workflows.
Suggestions
Add concrete feature engineering techniques with executable code examples (e.g., handling missing values, encoding categorical variables, feature scaling, creating interaction features)
Include specific workflows for common feature engineering tasks like 'For tabular data: 1. Analyze distributions, 2. Handle nulls, 3. Encode categoricals, 4. Scale numerics'
Provide copy-paste ready code snippets using pandas, sklearn, or other ML libraries for common transformations
Replace generic capability claims with actual examples showing input data and expected transformed output
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is padded with generic boilerplate that provides no actual information. Phrases like 'provides automated assistance' and 'follows industry best practices' are meaningless filler that waste tokens without teaching Claude anything specific. | 1 / 3 |
Actionability | There is zero concrete guidance - no code, no commands, no specific steps, no examples of actual feature engineering techniques. The content only describes what the skill claims to do rather than instructing how to do anything. | 1 / 3 |
Workflow Clarity | No workflow is defined. Despite claiming to provide 'step-by-step guidance,' there are no actual steps, sequences, or processes described. The skill is entirely meta-description without substance. | 1 / 3 |
Progressive Disclosure | The content is a monolithic block of vague descriptions with no structure for discovery. There are no references to detailed materials, no links to examples, and no organization beyond generic section headers. | 1 / 3 |
Total | 4 / 12 Passed |
Validation
69%Validation — 11 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 11 / 16 Passed | |
Reviewed
Table of Contents
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