Why is coverage analysis important for AI datasets?

Coverage analysis ensures that your evaluation data includes a healthy mix of document types, domains, and difficulty levels, preventing the AI from being tested only on 'easy' or narrow cases.

What is a golden dataset in the context of AI?

A golden dataset is a hand-curated, high-quality collection of data that serves as the 'ground truth' for evaluating the performance and accuracy of AI models and RAG pipelines.

Does it support custom schema validation?

Yes, the skill follows a standardized Document and Query schema (v2.0) that ensures all required fields like IDs, source URLs, and content sections are present and correctly formatted.

How does this skill help with duplicate detection?

It uses similarity thresholds to flag content that is too similar to existing entries, preventing data redundancy that could skew evaluation metrics.

Golden Dataset Validator

Name: Golden Dataset Validator
Author: yonatangross

byyonatangross

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Data Science & ML

Validates AI evaluation datasets for schema compliance, content integrity, and duplicate detection to ensure high-quality benchmarking.

The Golden Dataset Validation skill is a specialized tool for AI engineers and data scientists to maintain the integrity of 'ground truth' datasets used for LLM and RAG evaluation. It automates rigorous schema checks for both documents and queries, detects redundant content using semantic similarity thresholds, and performs coverage analysis to identify gaps in domain or difficulty distribution. By enforcing strict referential integrity and content quality standards, this skill ensures that your AI benchmarks are reliable, diverse, and free from the noise of poor-quality or duplicate data.

Key Features

01Semantic duplicate detection with configurable similarity thresholds

0269 GitHub stars

03Automated schema validation for complex document and query structures

04Coverage analysis to identify gaps in domains and content types

05Difficulty distribution auditing for balanced AI evaluation benchmarks

06Referential integrity checks between queries and document sections

Use Cases

01Ensuring dataset diversity by analyzing distribution across difficulty levels and topics

02Auditing existing AI datasets to identify and remove near-duplicate content

03Pre-commit validation of new documents and queries for RAG evaluation sets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add yonatangross/orchestkit golden-dataset-validation

For use in Claude.ai and ChatGPT

Download Skill