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Validate tabular data against configurable business rules — range checks, cross-column constraints, referential integrity against lookup tables, and temporal consistency.
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| name | business-rule-checker |
| description | Validate tabular data against configurable business rules — range checks, cross-column constraints, referential integrity against lookup tables, and temporal consistency. |
| version | 0.3.0 |
Validates dataset records against a set of business logic rules. Goes beyond schema validation to check domain-specific constraints: numeric range checks (e.g., price > 0), cross-column consistency (e.g., end_date >= start_date), referential integrity against external lookup tables (e.g., product_id must exist in product catalog), and temporal range validation (e.g., date within fiscal quarter). Records that fail rules are flagged or rejected.
[min, max][start, end]--input / -i: Path to dataset (CSV or Parquet) (required)--rules / -r: Path to rules config file (JSON or YAML)--lookup-dir: Directory containing lookup tables for referential checks--output / -o: Output directory--reject-action: What to do with failing rows: flag, reject, or separate (default: flag){
"rules": [
{"name": "positive_price", "type": "positive", "column": "unit_price"},
{"name": "positive_qty", "type": "positive", "column": "quantity"},
{"name": "valid_product", "type": "reference", "column": "product_id", "lookup": "product_catalog.json", "lookup_key": "id"},
{"name": "date_in_quarter", "type": "date-range", "column": "date", "min": "2024-01-01", "max": "2024-03-31"}
]
}
valid_records.csv: Records passing all rulesrejected_records.json: Records failing rules with violation detailsrule_summary.json: Per-rule pass/fail counts and percentagespython3 ./skills/business_rule_checker/checker.py -i <file> -r <rules> -o <output_dir>pandas, numpyValidate 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.