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golden-dataset-validation

Validate golden datasets for schema, duplicates, and referential integrity.

3|Updated Dec 27, 2025
Search Tags:#validation#deduplication#data-quality#schema-validation#referential-integrity#golden-dataset
One-click install
npx skills add https://github.com/yonatangross/create-yg-app --skill golden-dataset-validation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: golden-dataset-validation
Source: https://github.com/yonatangross/create-yg-app/tree/main/.claude/skills/golden-dataset-validation
Command: npx skills add https://github.com/yonatangross/create-yg-app --skill golden-dataset-validation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill validates golden datasets by enforcing schema rules, detecting duplicates, and ensuring referential integrity, thereby preventing data quality issues in AI workflows.

Core Features & Use Cases

  • Schema validation for documents and queries to guarantee consistent structure.
  • Duplicate detection via semantic similarity and URL checks to maintain dataset uniqueness.
  • Coverage analysis and referential checks to reveal gaps and preserve relationships between documents and queries.
  • Use Case: Pre-commit validation before adding new items to the golden dataset, and full-dataset audits.

Quick Start

Run full validation with the provided scripts to inspect new and existing dataset entries. For example, execute the pre-commit hook or the validate-all workflow to surface errors and warnings, then address issues before committing.

Frequently Asked Questions about golden-dataset-validation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate schema and detect duplicates in a golden dataset?▼

Validate golden datasets by running predefined scripts to enforce schema rules, check unique IDs, and detect semantic duplicates. This surfaces structural inconsistencies and ensures dataset uniqueness before committing new items.

How do I ensure referential integrity between documents and queries for AI evaluation?▼

Ensure referential integrity by executing full-dataset analyses that verify relationships between documents and queries. Coverage analysis reveals gaps and preserves links, maintaining dataset quality for AI workflows.

Can I run pre-commit validation to check data quality before adding new items?▼

Yes, pre-commit hooks validate data quality by executing per-document checks and schema validation before adding new items. This prevents data quality issues from entering the golden dataset repository.

What is the best way to perform a full-dataset audit for AI model training data?▼

Perform a full-dataset audit by executing the validate-all workflow to inspect existing dataset entries. It enforces schema rules, detects duplicates via semantic similarity, and validates URLs to guarantee training data quality.

Does schema validation work for both documents and queries in a golden dataset?▼

Schema validation works for both documents and queries by enforcing consistent structure across all entries. Predefined scripts check formats, unique IDs, and URL validity to guarantee dataset schema compliance.

Why does semantic duplicate detection matter for golden datasets?▼

Semantic duplicate detection identifies similar entries that unique ID checks miss, maintaining dataset uniqueness. It prevents redundant data from skewing AI evaluation and model training results.

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