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Transform and enrich tabular data — numeric normalization (min-max, z-score, robust scaling), financial metric calculation (revenue, margin), time feature extraction, moving averages, growth rates, and derived column computation.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Validate tabular data against configurable business rules — range checks, cross-column constraints, referential integrity against lookup tables, and temporal consistency.
Load and parse CSV files into structured data frames with configurable encoding, delimiter, and date parsing options.
Export pandas DataFrames or processed datasets to well-formatted CSV files with configurable encoding, delimiter, quoting, and column ordering.
Aggregate tabular data by one or more dimensions with configurable metrics — sum, mean, count, min, max, median. Supports multi-level grouping and pivot table generation.
Comprehensive data cleaning toolkit — handles missing values, duplicate removal, type coercion, whitespace trimming, and outlier detection in a single pass over the dataset.
Identify and remove duplicate records from tabular datasets using exact-match, fuzzy-match, or composite-key-based deduplication strategies.
| name | data-transformation |
| description | Transform and enrich tabular data — numeric normalization (min-max, z-score, robust scaling), financial metric calculation (revenue, margin), time feature extraction, moving averages, growth rates, and derived column computation. |
| version | 1.0.0 |
Computes derived features, financial metrics, and applies numeric transformations to tabular datasets. Combines numeric normalization/scaling with domain-specific feature engineering: revenue calculation, profit margin computation (via cost lookup), time-based feature extraction, rolling window aggregates, and period-over-period growth rates.
Merges functionality from: numeric_normalizer, feature_calculator.
(x - min) / (max - min) → [0, 1](x - mean) / std → mean=0, std=1(x - median) / IQR → outlier-resistantlog(x + 1) → for right-skewed datax / max(|x|) → [-1, 1]quantity * unit_pricequantity * unit_cost (via catalog lookup)revenue - cost_totalmargin / revenue * 100year, month, weekday, week_number from date columns--input / -i: Path to dataset (CSV or Parquet) (required)--features: Comma-separated feature list (default: all)--normalize-columns: Columns to normalize--normalize-method: min-max, z-score, robust, log, max-abs (default: min-max)--cost-lookup: Path to product catalog JSON for margin calculations--date-column: Column containing dates (default: date)--window: Rolling window size in days (default: 7)--group-by: Column for group-level calculations--output / -o: Output path--save-params: Save scaler parameters to JSON--load-params: Load saved scaler parametersscaler_params.json (optional): Fitted normalization parametersfeature_summary.json: Statistics for computed featurespython3 ./skills/data_transformation/transform.py -i <file> -o <output>pandas, numpy, scikit-learn