Data validation
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Validate CSV and JSON data files against schemas and quality rules. Use when the user asks to check data quality, validate a dataset, or verify file contents match an expected schema.
The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
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Data Validation
When to use this skill
Activate when the user wants to:
- Check a CSV file for missing headers, type mismatches, or duplicates
- Validate JSON against a schema
- Run data quality checks before a pipeline or import
Instructions
- Identify the file type (CSV or JSON) from the user's request
- For CSV files, run:
uv run scripts/validate_csv.py <file-path> - For JSON files, run:
Omituv run scripts/validate_json.py <file-path> --schema <schema-path>--schemaif no schema is provided — the script checks structural integrity only. - Parse the JSON output from stdout
- Report findings grouped by severity: errors first, then warnings
Output format
Both scripts output JSON to stdout:
{
"file": "data.csv",
"valid": false,
"errors": [
{ "line": 3, "column": "age", "message": "Expected integer, got 'abc'" }
],
"warnings": [
{ "line": 7, "column": "email", "message": "Empty value" }
],
"summary": { "rows": 100, "errors": 1, "warnings": 1 }
}
Gotchas
- Python 3.11+ is required — scripts use
tomlliband modern type hints - Use
uv run(notpip install) to execute scripts with inline dependencies - CSV files must have a header row — headerless files are rejected
- JSON schema validation uses JSON Schema draft-07
- For custom schema formats, read
references/SCHEMA-GUIDE.md