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.

1.07x

Quality

Does it follow best practices?

Impact

86%

1.07x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./planned-skills/generated/07-ml-training/feature-engineering-helper/SKILL.md

Evaluation results

90%

13%

Customer Churn Prediction: Data Preparation Pipeline

Data preparation pipeline with sklearn

Criteria

Without context

With context

Uses sklearn

100%

pip install present

Handles missing values

100%

Handles categorical encoding

70%

100%

Numeric scaling

100%

Pipeline object

100%

Validation report produced

100%

No nulls in output

100%

Step-by-step structure

100%

Output file produced

100%

90%

12%

Model Training Experiment: Hyperparameter Search

Hyperparameter tuning and experiment tracking

Criteria

Without context

With context

Uses sklearn or pytorch

100%

pip install present

Multiple configurations tried

100%

Experiment log produced

100%

Metric recorded per run

100%

Best config identified

100%

Reproducibility seed

100%

Validation step present

100%

Step-by-step structure

100%

Production-ready config format

100%

80%

-5%

End-to-End ML Feature Engineering Workflow

Production-ready feature engineering workflow

Criteria

Without context

With context

Uses sklearn or pandas

100%

pip install present

Date feature decomposition

100%

Derived ratio feature

100%

Transformation log produced

100%

Rationale in log

100%

Validation summary produced

100%

No nulls in output

100%

Step-by-step structure

100%

Engineered features file produced

100%

Production-ready code quality

37%

100%

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 3076d78

Evaluated: about 2 months ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Customer Churn Prediction: Data Preparation Pipeline Model Training Experiment: Hyperparameter Search End-to-End ML Feature Engineering Workflow

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