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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:7. April 2026 um 22:10

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4 Dateien

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

related-skills.json

gleiches Repository

project-maintenance.md

from "Pratiyush/agent-catalog"

Maintain project health by enforcing PR checklists, tracking open issues, verifying release readiness, and ensuring framework phases are up to date. Use when reviewing PRs, creating issues, preparing releases, or auditing project state.

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skill-template.md

from "Pratiyush/agent-catalog"

Scaffold new Agent Skills with best-practice structure and frontmatter. Use when the user wants to create a new skill, bootstrap a skill directory, or generate SKILL.md boilerplate following the Agent Skills spec.

2026-04-070

dependency-audit.md

from "Pratiyush/agent-catalog"

Audit project dependencies for known vulnerabilities, outdated packages, and license compliance issues. Use when the user asks to check dependencies, audit packages, review licenses, or assess supply chain security.

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hello-skill.md

from "Pratiyush/agent-catalog"

Generate friendly greeting messages in multiple languages. Use when the user asks for a hello message, welcome text, or localized greeting.

2026-04-070

release-notes.md

from "Pratiyush/agent-catalog"

Maintain project release notes, changelogs, and framework phase updates. Use when the user asks to generate a changelog, draft release notes, update CHANGELOG.md, or track framework phase transitions.

2026-04-070

test-generator.md

from "Pratiyush/agent-catalog"

Generate test file stubs and boilerplate from source code analysis. Use when the user asks to create tests, add test coverage, or scaffold test files for existing source code.

2026-04-070

package.json

"author": "Pratiyush"

"repository": "Pratiyush/agent-catalog"

GitHub-Repository öffnen Creator-Repositorys ansehen

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$ download --local

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SoftwareentwicklerInformatik- und Mathematikberufe15-1252L4

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

Mehr aus diesem Repository

Mehr aus diesem Repository

Data Validation

When to use this skill

Instructions

Output format

Gotchas