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data-validation

Name: Data Validation
Author: Pratiyush

// 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.

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updated:7 de abril de 2026, 22:10

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SKILL.md

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name	data-validation
description	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.
license	Apache-2.0
compatibility	Requires Python 3.11+ and uv
metadata	{"author":"skillscraft","version":"1.0","category":"data"}
allowed-tools	Bash Read

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:
```
uv run scripts/validate_json.py <file-path> --schema <schema-path>
```
Omit --schema if 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 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

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package.json

"author": "Pratiyush"

"repository": "Pratiyush/agent-catalog"

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Desarrolladores de softwareOcupaciones informáticas y matemáticas15-1252L4

name	data-validation
description	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.
license	Apache-2.0
compatibility	Requires Python 3.11+ and uv
metadata	{"author":"skillscraft","version":"1.0","category":"data"}
allowed-tools	Bash Read

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:
```
uv run scripts/validate_json.py <file-path> --schema <schema-path>
```
Omit --schema if 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 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

data-validation

Data Validation

When to use this skill

Instructions

Output format

Gotchas

Más de este repositorio

Más de este repositorio

Data Validation

When to use this skill

Instructions

Output format

Gotchas