القائمة
Validate tabular data against a predefined JSON schema — checks column presence, data types, nullable constraints, and unique constraints.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| 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 |
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.
{
"columns": {
"column_name": {
"type": "string|int|float|date|bool",
"nullable": false,
"unique": false,
"pattern": "regex (for string columns)"
}
},
"required_columns": ["col1", "col2"]
}
--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)required_columns must existvalidation_report.json: Structured report with:
python3 ./skills/schema_validator/validate.py -i <file> -s <schema>pandas, jsonschemaValidate 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.