Data Validation

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

1. Identify the file type (CSV or JSON) from the user's request
2. For CSV files, run:

   uv run scripts/validate_csv.py <file-path>
   

3. For JSON files, run:

   uv run scripts/validate_json.py <file-path> --schema <schema-path>
   

   Omit --schema if no schema is provided — the script checks structural integrity only.
4. Parse the JSON output from stdout
5. 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 tomllib and modern type hints
• Use uv run (not pip 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