Can this skill be used for existing datasets?

Yes, it is ideal for reviewing records with high disagreement among scorers or refining existing labels based on a new understanding of the criteria.

What format are the labeling decisions provided in?

Decisions are structured in a standardized YAML format including the label, primary reasons with specific guideline references, and optional observations.

How does it handle ambiguous cases?

The skill includes specific protocols to document ambiguity and signal when guidelines need clarification rather than forcing a potentially incorrect label.

What is ground truth labeling?

Ground truth labeling is the process of establishing definitive, authoritative answers for a dataset used to evaluate the accuracy and reasoning of AI models.

Why use this skill instead of manual labeling?

This skill enforces a rigorous methodology based on explicit guidelines rather than intuition, ensuring labels are defensible, consistent across the set, and auditable.

Ground Truth Labeling

Name: Ground Truth Labeling
Author: nicsuzor

bynicsuzor

0•

Data Science & ML

Establishes rigorous and defensible ground truth labels for evaluation datasets based on authoritative guidelines.

The Ground Truth skill provides a structured framework for creating, reviewing, and refining ground truth labels used in machine learning evaluation and reasoning tasks. It prioritizes authoritative guidelines over subjective intuition, ensuring that labels are consistent, reproducible, and well-documented. By incorporating a systematic workflow for analyzing records and constructing detailed YAML-formatted labels—including primary reasons and optional nuances—it helps developers and researchers build high-quality benchmarks for model performance auditing and dataset consistency.

Key Features

01Enforces guideline-based labeling over subjective intuition

02Systematic workflow for reviewing high-disagreement records

030 GitHub stars

04Structured YAML output for consistent label documentation

05Built-in consistency checks for dataset auditing

06Support for primary and optional reasoning categories

Use Cases

01Creating high-quality evaluation datasets for LLM benchmarking

02Resolving label disagreements between human or AI annotators

03Auditing existing datasets for consistency and guideline compliance

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nicsuzor/academicops ground-truth

For use in Claude.ai and ChatGPT

Download Skill