| name | schema-validator |
| description | Validate tabular data against a predefined JSON schema — checks column presence, data types, nullable constraints, and unique constraints. |
| version | 0.6.0 |
Schema Validator
Skill Summary
Validates a tabular dataset against a user-defined JSON schema. Checks that all required columns are present, column data types match expectations, nullable constraints are respected, and unique constraints hold. Produces a structured validation report indicating pass/fail for each rule, with row-level error details.
Schema Definition Format
{
"columns": {
"column_name": {
"type": "string|int|float|date|bool",
"nullable": false,
"unique": false,
"pattern": "regex (for string columns)"
}
},
"required_columns": ["col1", "col2"]
}
Inputs
--input / -i: Path to dataset file (CSV or Parquet) (required)--schema / -s: Path to JSON schema file (required)--output / -o: Output path for validation report--strict: Fail if extra columns exist beyond schema (default: false)--max-errors: Stop after N errors (default: unlimited)
Validation Checks
- Required columns: All columns listed in
required_columns must exist
- Type checking: Each column value must match declared type
- Nullable: Non-nullable columns must have zero null values
- Uniqueness: Unique columns must have no duplicate values
- Pattern matching: String values must match declared regex pattern
- Extra columns: In strict mode, flag columns not in schema
Output
validation_report.json: Structured report with:
- Overall pass/fail status
- Per-column validation results
- Row-level error details (row index, column, expected vs actual)
- Error counts by type
Implementation
- Command:
python3 ./skills/schema_validator/validate.py -i <file> -s <schema>
- Dependencies:
pandas, jsonschema
