feature-engineer
Comprehensive feature engineering for ML pipelines: data quality assessment, feature creation, selection, transformation, and encoding. Activates for "feature engineering", "create features", "feature selection", "data preprocessing", "handle missing values", "encode categorical", "scale features", "feature importance". Ensures features are production-ready with automated validation, documentation, and integration with SpecWeave increments.
npx skills add majiayu000/claude-skill-registry --skill feature-engineer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Feature Engineer ## Overview Feature engineering often makes the difference between mediocre and excellent ML models. This skill transforms raw data into model-ready features through systematic data quality assessment, feature creation, selection, and transformation—all integrated with SpecWeave's increment workflow. ## The Feature Engineering Pipeline ### Phase 1: Data Quality Assessment **Before creating features, understand your data**: ```python from specweave import DataQualityReport # Automated data quality check report = DataQualityReport(df, increment="0042") # Generates: # - Missing value analysis # - Outlier detection # - Data type validation # - Distribution analysis # - Correlation matrix # - Duplicate detection ``` **Quality Report Output**: ```markdown # Data Quality Report ## Dataset Overview - Rows: 100,000 - Columns: 45 - Memory: 34.2 MB ## Missing Values | Column | Missing | Percentage | |-----------------|---------|------------| | email | 15,234 | 15.2% | | phone | 8,901 | 8.9% | | purchase_date | 0 | 0.0% | ## Outliers Detected - transaction_amount: 234 outliers (>3 std dev) - user_age: 12 outliers (<18 or >100) ## Data Type Issues - user_id: Stored as float,
- Overview
- The Feature Engineering Pipeline
- Phase 1: Data Quality Assessment
- Phase 2: Feature Creation
- Phase 3: Feature Selection
- Phase 4: Feature Transformation
- Phase 5: Feature Validation
- Integration with SpecWeave
- Automatic Feature Documentation
- Living Docs Integration
- Best Practices
- 1. Document Feature Rationale
- 2. Handle Missing Values Systematically
- 3. Avoid Data Leakage
Generate feature engineering pipeline for increment Validate features before training Generate feature importance report
What does the feature-engineer skill do?
Comprehensive feature engineering for ML pipelines: data quality assessment, feature creation, selection, transformation, and encoding. Activates for "feature engineering", "create features", "feature selection", "data preprocessing", "handle missing values", "encode categorical", "scale features", "feature importance". Ensures features are production-ready with automated validation, documentation, and integration with SpecWeave increments.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill feature-engineer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.