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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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stars:0

forks:0

updated:2026年4月7日 22:10

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4 ファイル

SKILL.md

readonly

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

related-skills.json

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package.json

"author": "Pratiyush"

"repository": "Pratiyush/agent-catalog"

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ソフトウェア開発者コンピュータ・数学職15-1252L4

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

このリポジトリの他の Skills

このリポジトリの他の Skills

Data Validation

When to use this skill

Instructions

Output format

Gotchas