Skills data-validator
📦

data-validator

Content revision r1 Low Risk ⚙️ External commands🌐 Network access

Validate Data Quality and Business Rules

Bad records, weak schemas, and unclear rules can slow delivery and hide data quality risks. This skill guides Claude, Codex, and Claude Code through validation checks, reports, and practical fixes.

Supports: Claude Codex Code(CC)
📊 74 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "data-validator" from https://skillstore.io/skills/curiouslearner-data-validator.md and its manifest at https://skillstore.io/api/skills/curiouslearner-data-validator/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/curiouslearner-data-validator.md Signed manifest GET /api/skills/curiouslearner-data-validator/manifest Signed lockfile GET /api/skills/curiouslearner-data-validator/lockfile

Test it

Using "data-validator". A customer table has required email and age fields, but some rows are missing values or contain invalid formats.

Expected outcome:

The report flags missing emails, invalid email formats, and ages outside the accepted range. It also summarizes affected columns and recommended fixes.

Using "data-validator". An order payload must match subtotal, discount, tax, shipping, and final total rules.

Expected outcome:

The validation result separates schema errors from business-rule errors. It explains which total check failed and which values need review.

Using "data-validator". A data quality review needs completeness, duplicates, format checks, and an overall score.

Expected outcome:

The generated report includes a summary score, column-level completeness, duplicate counts, invalid format counts, and prioritized cleanup actions.

Security Audit

Low Risk
v8 • 7/6/2026 Open versioned report

Reviewed all 39 static findings in SKILL.md. The external command, network, sensitive, and reconnaissance alerts are false positives from Markdown fences, example validation snippets, field names, and report formatting. One low privacy concern remains because report examples can include raw invalid samples from user data.

1
Files scanned
923
Lines analyzed
0
Review items
0
False positives ignored

Confirmed security concerns (1)

Low
SKILL.md:600SKILL.md:713SKILL.md:723SKILL.md:733SKILL.md:813-815
Validation Reports May Include Raw Data Samples
The examples include invalid sample values and duplicate rows in reports. Shared reports could expose personal data from email, phone, URL, or duplicate records.
The cited report examples return duplicate records and invalid samples directly. The risk is limited because there is no exfiltration path, but reports may be shared outside the original data context.

Risk Factors

⚙️ External commands (23)
SKILL.md:44-50 SKILL.md:50-57 SKILL.md:57-170 SKILL.md:170-173 SKILL.md:173-211 SKILL.md:211-215 SKILL.md:215-367 SKILL.md:367-371 SKILL.md:371-467 SKILL.md:467-471 SKILL.md:471-551 SKILL.md:551-557 SKILL.md:557-630 SKILL.md:630-634 SKILL.md:634-737 SKILL.md:737-741 SKILL.md:741-798 SKILL.md:798-815 SKILL.md:815-856 SKILL.md:856-874 SKILL.md:874-889 SKILL.md:889-892 SKILL.md:892-911
🌐 Network access (1)
SKILL.md:875
Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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https://skillstore.io/skills/curiouslearner-data-validator/audits/8?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

CuriousLearner. (2026). data-validator security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/curiouslearner-data-validator/audits/8

BibTeX citation

@techreport{curiouslearner-curiouslearner-data-validator-2026, author = {CuriousLearner}, title = {data-validator security audit report (audit version 8)}, institution = {Skillstore}, year = {2026}, number = {8}, url = {https://skillstore.io/skills/curiouslearner-data-validator/audits/8}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "data-validator security audit report (audit version 8)" version: "unspecified" type: report authors: - name: "CuriousLearner" date-released: "2026-07-06" url: "https://skillstore.io/skills/curiouslearner-data-validator/audits/8" identifiers: - type: other value: "skillstore:curiouslearner-data-validator:audit:8" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
55
Architecture
85
Maintainability
87
Content
70
Community
74
Spec Compliance

What You Can Build

Profile Pipeline Data Quality

Check incoming tables for missing values, duplicates, bad formats, and schema drift before downstream processing.

Validate API Payloads

Create JSON Schema or AJV checks for request and response bodies before integration testing.

Audit Business Rules

Translate product rules into validation checks for orders, registrations, eligibility, or compliance workflows.

Try These Prompts

Validate a JSON Record 
Validate this record against the schema I provide. List each failed field, the rule it failed, and the suggested correction.
Check a DataFrame Schema 
Review this table profile and expected schema. Identify missing columns, extra columns, type mismatches, null issues, duplicates, and range violations.
Design Business Rules 
Convert these business requirements into validation rules. Separate schema checks, semantic checks, cross-field checks, and user-facing error messages.
Build a Quality Report Plan 
Create a validation report plan for this dataset. Include completeness, uniqueness, validity, outliers, severity levels, and safe sample redaction rules.

Best Practices

  • Define schemas and business rules before validating production data.
  • Mask personal data before adding sample values to shared reports.
  • Test validation rules with valid records, invalid records, and edge cases.

Avoid

  • Do not rely on format checks as the only business-rule validation.
  • Do not run untrusted custom validator functions without review.
  • Do not publish raw invalid samples from sensitive datasets.

Frequently Asked Questions

Does this skill install a validator tool?
No. It provides validation guidance, reusable examples, and report structures that users adapt to their environment.
Can it validate JSON API payloads?
Yes. It includes JSON Schema and AJV patterns for request and response validation.
Can it check CSV or table data?
Yes. The examples use DataFrame checks for columns, types, nulls, duplicates, ranges, and patterns.
Does it create business-rule validations?
Yes. It shows how to define named rules with clear error messages and cross-field checks.
Can it generate data quality reports?
Yes. It outlines reports with completeness, duplicates, schema errors, format errors, and quality scores.
How should sensitive data be handled?
Mask or remove personal values before sharing reports, especially invalid samples and duplicate record examples.

Developer Details

License

MIT

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/CuriousLearner/devkit/tree/main/skills/data-validator

Ref

d11af420e07cd866fe39cfa937efef3223896c06

Maintenance freshness

7/18/2026

Usage

4 downloads · 217 views

File structure

📄 SKILL.md